Publications

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Articles

  • A. Bender, G. Agamennoni, J. R. Ward, S. Worrall, and E. M. Nebot, “An Unsupervised Approach for Inferring Driver Behavior From Naturalistic Driving Data,” Intelligent Transportation Systems, IEEE Transactions on, vol. 16, iss. 6, pp. 3325-3336, 2015. doi:10.1109/TITS.2015.2449837
    [BibTeX]
    @Article{Bender2015a,
    Title = {An Unsupervised Approach for Inferring Driver Behavior From Naturalistic Driving Data},
    Author = {Bender, A. and Agamennoni, G. and Ward, J.R. and Worrall, S. and Nebot, E.M.},
    Journal = {Intelligent Transportation Systems, IEEE Transactions on},
    Year = {2015},
    Month = {Dec},
    Number = {6},
    Pages = {3325-3336},
    Volume = {16},
    Doi = {10.1109/TITS.2015.2449837},
    ISSN = {1524-9050},
    Keywords = {Bayes methods;Behavioral science;Clustering methods;Computational modeling;Data models;Clustering;driver behaviour;intelligent transportation systems;naturalistic driving data;segmentation}
    }

  • J. R. Ward, G. Agamennoni, S. Worrall, A. Bender, and E. Nebot, “Extending Time to Collision for probabilistic reasoning in general traffic scenarios,” Transportation Research Part C: Emerging Technologies, vol. 51, pp. 66-82, 2015.
    [BibTeX]
    @Article{Ward2015TTC,
    Title = {Extending Time to Collision for probabilistic reasoning in general traffic scenarios},
    Author = {Ward, James R and Agamennoni, Gabriel and Worrall, Stewart and Bender, Asher and Nebot, Eduardo},
    Journal = {Transportation Research Part C: Emerging Technologies},
    Year = {2015},
    Pages = {66--82},
    Volume = {51},
    Publisher = {Pergamon}
    }

  • M. Shan, S. Worrall, and E. Nebot, “Delayed-State Nonparametric Filtering in Cooperative Tracking,” IEEE Transactions on Robotics, vol. 31, iss. 4, pp. 962-977, 2015.
    [BibTeX]
    @Article{shan2015delayed,
    Title = {Delayed-State Nonparametric Filtering in Cooperative Tracking},
    Author = {Shan, Mao and Worrall, Stewart and Nebot, Eduardo},
    Journal = {IEEE Transactions on Robotics},
    Year = {2015},
    Number = {4},
    Pages = {962--977},
    Volume = {31},
    Publisher = {IEEE}
    }

  • M. Shan, S. Worrall, F. Masson, and E. Nebot, “Using Delayed Observations for Long-Term Vehicle Tracking in Large Environments,” Intelligent Transportation Systems, IEEE Transactions on, vol. 15, iss. 3, pp. 967-981, 2014. doi:10.1109/TITS.2013.2292934
    [BibTeX] [Abstract]

    The tracking of vehicles over large areas with limited position observations is of significant importance in many industrial applications. This paper presents algorithms for long-term vehicle motion estimation based on a vehicle motion model that incorporates the properties of the working environment and information collected by other mobile agents and fixed infrastructure collection points. The prediction algorithm provides long-term estimates of vehicle positions using speed and timing profiles built for a particular environment and considering the probability of a vehicle stopping. A limited number of data collection points distributed around the field are used to update the estimates, with negative information (no communication) also used to improve the prediction. This paper introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates to be relayed among vehicles and finally conveyed to the collection point for an improved position estimate. Positive and negative communication information is incorporated into the fusion stage, and a particle filter is used to incorporate the delayed observations harvested from vehicles in the field to improve the position estimates. The contributions of this work enable the optimization of fleet scheduling using discrete observations. Experimental results from a typical large-scale mining operation are presented to validate the algorithms.

    @Article{Shan2014,
    Title = {Using Delayed Observations for Long-Term Vehicle Tracking in Large Environments},
    Author = {Mao Shan and Worrall, S. and Masson, F. and Nebot, E.},
    Journal = {Intelligent Transportation Systems, IEEE Transactions on},
    Year = {2014},
    Month = {June},
    Number = {3},
    Pages = {967-981},
    Volume = {15},
    Abstract = {The tracking of vehicles over large areas with limited position observations is of significant importance in many industrial applications. This paper presents algorithms for long-term vehicle motion estimation based on a vehicle motion model that incorporates the properties of the working environment and information collected by other mobile agents and fixed infrastructure collection points. The prediction algorithm provides long-term estimates of vehicle positions using speed and timing profiles built for a particular environment and considering the probability of a vehicle stopping. A limited number of data collection points distributed around the field are used to update the estimates, with negative information (no communication) also used to improve the prediction. This paper introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates to be relayed among vehicles and finally conveyed to the collection point for an improved position estimate. Positive and negative communication information is incorporated into the fusion stage, and a particle filter is used to incorporate the delayed observations harvested from vehicles in the field to improve the position estimates. The contributions of this work enable the optimization of fleet scheduling using discrete observations. Experimental results from a typical large-scale mining operation are presented to validate the algorithms.},
    Doi = {10.1109/TITS.2013.2292934},
    ISSN = {1524-9050},
    Keywords = {image fusion;mobile radio;motion estimation;object tracking;particle filtering (numerical methods);traffic engineering computing;data collection points;delayed observation harvesting process;discrete observations;egocentric position updates;fixed infrastructure collection points;fleet scheduling optimization;fusion stage;large-scale mining operation;long-term vehicle motion estimation;long-term vehicle tracking;mobile agents;negative communication information;particle filter;peer-to-peer communication;positive communication information;prediction algorithm;speed profiles;timing profiles;vehicle motion model;vehicle positions;vehicle stopping probability;Acceleration;Data collection;Prediction algorithms;Roads;Timing;Tracking;Vehicles;Delayed observations;intervehicle communication;long-term motion prediction;particle filtering;vehicle tracking}
    }

  • J. Ward, S. Worrall, G. Agamennoni, and E. Nebot, “The Warrigal Dataset: Multi-Vehicle Trajectories and V2V Communications,” Intelligent Transportation Systems Magazine, IEEE, vol. 6, iss. 3, pp. 109-117, 2014. doi:10.1109/MITS.2014.2315660
    [BibTeX] [Abstract]

    Intelligent Transportation Systems rely on understanding, predicting and affecting the interactions between vehicles. Development of such systems must be based upon data derived from actual interactions if they are to be effective when used in real world applications. Increasingly, systems are being developed that are based on radio communication of state and intent between vehicles. Understanding of how these interactions occur is also necessary to creating robust systems. In order to test and compare new techniques, approaches and algorithms it is necessary to have a rich dataset to experiment with. This paper presents a detailed dataset useful for members of the Intelligent Transportation Systems community. It contains vehicle state information, vehicle-to-vehicle communications and road maps at high temporal resolution for large numbers of interacting vehicles over a long time period. This data set has already been used for a number of Intelligent Transportation Systems projects such as road mapping, driver intent prediction and collision avoidance among others.

    @Article{ward2014warrigal,
    Title = {The Warrigal Dataset: Multi-Vehicle Trajectories and V2V Communications},
    Author = {Ward, J. and Worrall, S. and Agamennoni, G. and Nebot, E.},
    Journal = {Intelligent Transportation Systems Magazine, IEEE},
    Year = {2014},
    Month = {Fall},
    Number = {3},
    Pages = {109-117},
    Volume = {6},
    Abstract = {Intelligent Transportation Systems rely on understanding, predicting and affecting the interactions between vehicles. Development of such systems must be based upon data derived from actual interactions if they are to be effective when used in real world applications. Increasingly, systems are being developed that are based on radio communication of state and intent between vehicles. Understanding of how these interactions occur is also necessary to creating robust systems. In order to test and compare new techniques, approaches and algorithms it is necessary to have a rich dataset to experiment with. This paper presents a detailed dataset useful for members of the Intelligent Transportation Systems community. It contains vehicle state information, vehicle-to-vehicle communications and road maps at high temporal resolution for large numbers of interacting vehicles over a long time period. This data set has already been used for a number of Intelligent Transportation Systems projects such as road mapping, driver intent prediction and collision avoidance among others.},
    Doi = {10.1109/MITS.2014.2315660},
    ISSN = {1939-1390},
    Keywords = {collision avoidance;intelligent transportation systems;radiocommunication;road vehicles;V2V communication;collision avoidance;driver intent prediction;intelligent transportation systems;multivehicle trajectory;radio communication;road mapping;road maps;robust system;temporal resolution;vehicle state information;vehicle-to-vehicle communication;warrigal dataset;Acceleration;Antennas;Electric vehicles;Global Positioning System;Intelligent vehicles;Traffic safety;Trajectory}
    }

  • S. Worrall, G. Agamennoni, J. Ward, and E. Nebot, “Fault Detection for Vehicular Ad Hoc Wireless Networks,” Intelligent Transportation Systems Magazine, IEEE, vol. 6, iss. 2, pp. 34-44, 2014. doi:10.1109/MITS.2014.2304974
    [BibTeX] [Abstract]

    An increasing number of intelligent transportation applications require robust and reliable wireless ad hoc communication. The process of communicating using radio requires a series of software and hardware modules to be functioning correctly. For many vehicle safety and automation applications communication is relied upon to the point where undetected faults can result in potentially dangerous situations, for example if a warning cannot be given in time to prevent a collision. The consequence of problems with any of the network components can be a partial or complete loss of radio communication. Generally, most systems will consider network failure when there is no communication, but this overlooks problems where a partial fault causes degradation in the communication performance. There is a fundamental requirement to detect and respond to the partial failure of a network to ensure that communication is not intermittent, or performs poorly after a certain range. The partial loss of communication is difficult to detect, and is often overlooked in mobile ad hoc network applications. This paper introduces a novel method for modeling the antenna performance using collected data, and using the model to determine the probability that an antenna has some level of performance degradation.

    @Article{Worrall2014,
    Title = {Fault Detection for Vehicular Ad Hoc Wireless Networks},
    Author = {Worrall, S. and Agamennoni, G. and Ward, J. and Nebot, E.},
    Journal = {Intelligent Transportation Systems Magazine, IEEE},
    Year = {2014},
    Month = {Summer},
    Number = {2},
    Pages = {34-44},
    Volume = {6},
    Abstract = {An increasing number of intelligent transportation applications require robust and reliable wireless ad hoc communication. The process of communicating using radio requires a series of software and hardware modules to be functioning correctly. For many vehicle safety and automation applications communication is relied upon to the point where undetected faults can result in potentially dangerous situations, for example if a warning cannot be given in time to prevent a collision. The consequence of problems with any of the network components can be a partial or complete loss of radio communication. Generally, most systems will consider network failure when there is no communication, but this overlooks problems where a partial fault causes degradation in the communication performance. There is a fundamental requirement to detect and respond to the partial failure of a network to ensure that communication is not intermittent, or performs poorly after a certain range. The partial loss of communication is difficult to detect, and is often overlooked in mobile ad hoc network applications. This paper introduces a novel method for modeling the antenna performance using collected data, and using the model to determine the probability that an antenna has some level of performance degradation.},
    Doi = {10.1109/MITS.2014.2304974},
    ISSN = {1939-1390},
    Keywords = {antennas;fault diagnosis;road safety;transportation;vehicular ad hoc networks;antenna performance;antenna probability;automation applications communication;fault detection;hardware modules;intelligent transportation;mobile ad hoc network;radio communication;software modules;vehicle safety;vehicular ad hoc wireless networks;Ad hoc networks;Communication services;Failure analysis;IEEE standards;Performance evaluation;Robust control;Wireless communication;Wireless sensor networks}
    }

  • M. Shan, S. Worrall, and E. Nebot, “Probabilistic Long-Term Vehicle Motion Prediction and Tracking in Large Environments,” Intelligent Transportation Systems, IEEE Transactions on, vol. 14, iss. 2, pp. 539-552, 2013. doi:10.1109/TITS.2012.2224657
    [BibTeX] [Abstract]

    Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can easily be achieved by providing vehicles with a constant communication link to a control center and having the vehicles broadcast their position. The problem dramatically changes when vehicles operate within a large environment of potentially hundreds of square kilometers and in difficult terrain. This paper presents algorithms for long-term vehicle motion prediction and tracking based on a multiple-model approach. It incorporates a probabilistic vehicle model that includes the structure of the environment. The prediction algorithm evaluates the vehicle position using acceleration, speed, and timing profiles built for the particular environment and considers the probability that the vehicle will stop. A limited number of data collection points distributed around the field are used to update the vehicle position estimate when in communication range, and prediction is used at points in between. A particle filter is used to estimate the vehicle position using both positive and negative information (whether communication is possible) in the fusion stage. The algorithms presented are validated with experimental results using data collected from a large-scale mining operation.

    @Article{Shan2013,
    Title = {Probabilistic Long-Term Vehicle Motion Prediction and Tracking in Large Environments},
    Author = {Mao Shan and Worrall, S. and Nebot, E.},
    Journal = {Intelligent Transportation Systems, IEEE Transactions on},
    Year = {2013},
    Month = {June},
    Number = {2},
    Pages = {539-552},
    Volume = {14},
    Abstract = {Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can easily be achieved by providing vehicles with a constant communication link to a control center and having the vehicles broadcast their position. The problem dramatically changes when vehicles operate within a large environment of potentially hundreds of square kilometers and in difficult terrain. This paper presents algorithms for long-term vehicle motion prediction and tracking based on a multiple-model approach. It incorporates a probabilistic vehicle model that includes the structure of the environment. The prediction algorithm evaluates the vehicle position using acceleration, speed, and timing profiles built for the particular environment and considers the probability that the vehicle will stop. A limited number of data collection points distributed around the field are used to update the vehicle position estimate when in communication range, and prediction is used at points in between. A particle filter is used to estimate the vehicle position using both positive and negative information (whether communication is possible) in the fusion stage. The algorithms presented are validated with experimental results using data collected from a large-scale mining operation.},
    Doi = {10.1109/TITS.2012.2224657},
    ISSN = {1524-9050},
    Keywords = {control engineering computing;mining industry;mobile communication;off-road vehicles;particle filtering (numerical methods);sensor fusion;traffic engineering computing;acceleration profiles;communication range;control center;fusion stage;industrial applications;large-scale mining operation;particle filter;probabilistic long-term vehicle motion prediction;probabilistic long-term vehicle motion tracking;speed profiles;timing profiles;vehicle position tracking;Acceleration;Correlation;Motion segmentation;Roads;Timing;Tracking;Vehicles;Long-term motion prediction;negative information;particle filtering;statistical model;vehicle tracking}
    }

  • G. Agamennoni, J. I. Nieto, and E. M. Nebot, “Approximate Inference in State-space Models with Heavy-tailed Noise,” IEEE Transactions on Signal Processing, vol. 60, iss. 10, pp. 5024-5037, 2012.
    [BibTeX]
    @Article{Agamennoni2012b,
    Title = {Approximate Inference in State-space Models with Heavy-tailed Noise},
    Author = {Agamennoni, G. and Nieto, J. I. and Nebot, E.M.},
    Journal = {IEEE Transactions on Signal Processing},
    Year = {2012},
    Month = {October},
    Number = {10},
    Pages = {5024--5037},
    Volume = {60}
    }

  • G. Agamennoni, J. I. Nieto, and E. M. Nebot, “Estimation of Multivehicle Dynamics by Considering Contextual Information,” IEEE Transactions on Robotics, vol. 28, iss. 4, pp. 855-870, 2012.
    [BibTeX]
    @Article{Agamennoni2012a,
    Title = {Estimation of Multivehicle Dynamics by Considering Contextual Information},
    Author = {Agamennoni, G. and Nieto, J.I. and Nebot, E.M.},
    Journal = {IEEE Transactions on Robotics},
    Year = {2012},
    Month = {August},
    Number = {4},
    Pages = {855--870},
    Volume = {28}
    }

  • S. Worrall, G. Agamennoni, J. Nieto, and E. Nebot, “A Context-Based Approach to Vehicle Behavior Prediction,” Intelligent Transportation Systems Magazine, IEEE, vol. 4, iss. 3, pp. 32-44, 2012. doi:10.1109/MITS.2012.2203230
    [BibTeX] [Abstract]

    Despite the best efforts of research and development carried out in the automotive industry, accidents continue to occur resulting in many deaths and injuries each year. It has been shown that the vast majority of accidents occur as a result (at least in part) of human error. This paper introduces the model for the Intelligent Systems for Risk Assessment (ISRA) project which has the goal of eliminating accidents by detecting risk, alerting the operators when appropriate, and ultimately removing some control of the vehicle from the operator when the risk is deemed unacceptable. The underlying premise is that vehicle dynamic information without contextual information is insufficient to understand the situation well enough to enable the analysis of risk. This paper defines the contextual information required to analyze the situation and shows how location context information can be derived using collected vehicle data. The process to infer high level vehicle state information using context information is also presented. The experimental results demonstrate the context based inference process using data collected from a fleet of mining vehicles during normal operation. The systems developed for the mining industry can later be extended to include more complex traffic scenarios that exist in the domain of ITS.

    @Article{Worrall2012,
    Title = {A Context-Based Approach to Vehicle Behavior Prediction},
    Author = {Worrall, S. and Agamennoni, G. and Nieto, J. and Nebot, E.},
    Journal = {Intelligent Transportation Systems Magazine, IEEE},
    Year = {2012},
    Month = {Fall},
    Number = {3},
    Pages = {32-44},
    Volume = {4},
    Abstract = {Despite the best efforts of research and development carried out in the automotive industry, accidents continue to occur resulting in many deaths and injuries each year. It has been shown that the vast majority of accidents occur as a result (at least in part) of human error. This paper introduces the model for the Intelligent Systems for Risk Assessment (ISRA) project which has the goal of eliminating accidents by detecting risk, alerting the operators when appropriate, and ultimately removing some control of the vehicle from the operator when the risk is deemed unacceptable. The underlying premise is that vehicle dynamic information without contextual information is insufficient to understand the situation well enough to enable the analysis of risk. This paper defines the contextual information required to analyze the situation and shows how location context information can be derived using collected vehicle data. The process to infer high level vehicle state information using context information is also presented. The experimental results demonstrate the context based inference process using data collected from a fleet of mining vehicles during normal operation. The systems developed for the mining industry can later be extended to include more complex traffic scenarios that exist in the domain of ITS.},
    Doi = {10.1109/MITS.2012.2203230},
    ISSN = {1939-1390},
    Keywords = {Accidents;Automotive engineering;Injuries;Intelligent vehicles;Research and development;Road transportation;Road vehicles}
    }

  • G. Agamennoni, J. I. Nieto, and E. M. Nebot, “Robust Inference of Principal Road Paths for Intelligent Transportation Systems,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, iss. 1, pp. 298-308, 2011.
    [BibTeX]
    @Article{Agamennoni2011a,
    Title = {Robust Inference of Principal Road Paths for Intelligent Transportation Systems},
    Author = {Agamennoni, G. and Nieto, J.I. and Nebot, E.M.},
    Journal = {IEEE Transactions on Intelligent Transportation Systems},
    Year = {2011},
    Month = {March},
    Number = {1},
    Pages = {298--308},
    Volume = {12},
    Owner = {gaga9329},
    Timestamp = {2011.04.12}
    }

  • S. Worrall, D. Orchansky, F. Masson, J. Nieto, and E. Nebot, “Determining High Safety Risk Scenarios by Applying Context Information.,” Journal of Physical Agents, vol. 4, iss. 2, 2010.
    [BibTeX] [Abstract] [Download PDF]

    <p>When mining vehicle operators take risks, there is<br />a increased probability of an accident that can cause injuries,<br />fatalities and large financial costs to the mine operators. It<br />can be assumed that the operators do not intentially take<br />unnecessarily high risk, and that the risks are hidden due to<br />factors such as adverse weather, fatigue, visual obstructions,<br />boredom, etc. This paper examines the potential of measuring<br />the risk of danger in a multi-agent situation by using the safe<br />rules of operation defined by mining safety management.</p><p>The problem with measuring safety is that the safe rules of<br />operation are heavily dependent on the context of the situation.<br />What is considered normal practice and safe in one part of the<br />mine (such as performing a u-turn in a parking lot) is not safe<br />on a haul road. To be able to measure safety, it is therefore<br />necessary to understand the different context areas in a mine<br />so that feedback of unsafe behaviour presented to the operator<br />is relevant to the context of the situation. This paper presents a<br />novel method for generating context area information using the<br />vehicle trajectory information collected from a group of vehicles<br />interacting in an area. Results are presented using real-life<br />data collected from several operating fleets of mining vehicles.<br />The algorithms presented have potential application to a large<br />variety of environments including Intelligent Transportation<br />Systems (ITS).</p>

    @Article{Worrall10,
    Title = {Determining High Safety Risk Scenarios by Applying Context Information.},
    Author = {Stewart Worrall and David Orchansky and Favio Masson and Juan Nieto and Eduardo Nebot},
    Journal = {Journal of Physical Agents},
    Year = {2010},
    Number = {2},
    Volume = {4},
    Abstract = {<p>When mining vehicle operators take risks, there is<br />a increased probability of an accident that can cause injuries,<br />fatalities and large financial costs to the mine operators. It<br />can be assumed that the operators do not intentially take<br />unnecessarily high risk, and that the risks are hidden due to<br />factors such as adverse weather, fatigue, visual obstructions,<br />boredom, etc. This paper examines the potential of measuring<br />the risk of danger in a multi-agent situation by using the safe<br />rules of operation defined by mining safety management.</p><p>The problem with measuring safety is that the safe rules of<br />operation are heavily dependent on the context of the situation.<br />What is considered normal practice and safe in one part of the<br />mine (such as performing a u-turn in a parking lot) is not safe<br />on a haul road. To be able to measure safety, it is therefore<br />necessary to understand the different context areas in a mine<br />so that feedback of unsafe behaviour presented to the operator<br />is relevant to the context of the situation. This paper presents a<br />novel method for generating context area information using the<br />vehicle trajectory information collected from a group of vehicles<br />interacting in an area. Results are presented using real-life<br />data collected from several operating fleets of mining vehicles.<br />The algorithms presented have potential application to a large<br />variety of environments including Intelligent Transportation<br />Systems (ITS).</p>},
    ISSN = {1888-0258},
    Keywords = {Safety; Human Machine Interfaces; Data Mining; Algorithms},
    Url = {http://www.jopha.net/index.php/jopha/article/view/70}
    }

  • E. Nebot, J. Guivant, and S. Worrall, “Haul truck alignment monitoring and operator warning system,” Journal of Field Robotics, vol. 23, iss. 2, pp. 141-161, 2006. doi:10.1002/rob.20114
    [BibTeX] [Abstract] [Download PDF]

    This paper presents a haul truck alignment monitoring system that provides early warning signals to the operator when the truck is about to lose control. It is considered that a truck is in this condition when it crosses the center of the road or veers to the side of the road at speed in an uncontrolled manner. The system provides different levels of warnings to the driver and other haul trucks in visual contact. The system is based on a laser range and bearing sensor that measure the relative distance to standard polyvinyl chloride poles located at the side of the road. Using this approach, a high level of accuracy and reliability can be achieved with low cost since the installation and maintenance of the infrastructure does not require special expertise or expensive machinery. The system also logs raw sensor data and warning events generated by the truck. This information is downloaded using wireless interfaces to a base station for postprocessing purposes. This is essential to monitor the operation of the system and determine potential degradation of its components. Experimental results are shown demonstrating the robustness of the system. These results were obtained from actual data extracted from a database built with more that 12 months of continued operation of the system in two different mines.

    @Article{nebot2006haul,
    Title = {Haul truck alignment monitoring and operator warning system},
    Author = {Nebot, Eduardo and Guivant, Jose and Worrall, Stewart},
    Journal = {Journal of Field Robotics},
    Year = {2006},
    Number = {2},
    Pages = {141--161},
    Volume = {23},
    Abstract = {This paper presents a haul truck alignment monitoring system that provides early warning signals to the operator when the truck is about to lose control. It is considered that a truck is in this condition when it crosses the center of the road or veers to the side of the road at speed in an uncontrolled manner. The system provides different levels of warnings to the driver and other haul trucks in visual contact. The system is based on a laser range and bearing sensor that measure the relative distance to standard polyvinyl chloride poles located at the side of the road. Using this approach, a high level of accuracy and reliability can be achieved with low cost since the installation and maintenance of the infrastructure does not require special expertise or expensive machinery. The system also logs raw sensor data and warning events generated by the truck. This information is downloaded using wireless interfaces to a base station for postprocessing purposes. This is essential to monitor the operation of the system and determine potential degradation of its components. Experimental results are shown demonstrating the robustness of the system. These results were obtained from actual data extracted from a database built with more that 12 months of continued operation of the system in two different mines.},
    Doi = {10.1002/rob.20114},
    ISSN = {1556-4967},
    Publisher = {Wiley Subscription Services, Inc., A Wiley Company},
    Url = {http://dx.doi.org/10.1002/rob.20114}
    }

  • E. Nebot, J. Guivant, and S. Worrall, “Haul Truck Travel Path Alignment, Proximity and Fleet Monitoring System,” Australian Mining, vol. 221, 2005.
    [BibTeX]
    @Article{nebot2005haul,
    Title = {Haul Truck Travel Path Alignment, Proximity and Fleet Monitoring System},
    Author = {Nebot, EM and Guivant, JE and Worrall, S},
    Journal = {Australian Mining},
    Year = {2005},
    Volume = {221},
    Owner = {stewart},
    Timestamp = {2014.08.15}
    }

  • S. Thrun, M. Montemerlo, D. Koller, B. Wegbreit, J. I. Nieto, and E. M. Nebot, “FastSLAM: An Efficient Solution to the Simultaneous Localization And Mapping Problem with Unknown Data Association,” Journal of Machine Learning Research, 2004.
    [BibTeX]
    @Article{Thrun04,
    Title = {Fast{SLAM}: An Efficient Solution to the Simultaneous Localization And Mapping Problem with Unknown Data Association},
    Author = {Thrun, S. and Montemerlo, M. and Koller, D. and Wegbreit, B. and Nieto, J.I. and Nebot, E.M.},
    Journal = {Journal of Machine Learning Research},
    Year = {2004},
    Note = {In press},
    Owner = {gaga9329},
    Timestamp = {2010.12.08}
    }

  • S. Sukkarieh, E. M. Nebot, and H. F. Durrant-Whyte, “A high integrity IMU/GPS navigation loop for autonomous land vehicle applications,” Robotics and Automation, IEEE Transactions on, vol. 15, iss. 3, pp. 572-578, 1999. doi:10.1109/70.768189
    [BibTeX]
    @Article{Sukkarieh1999,
    Title = {A high integrity IMU/GPS navigation loop for autonomous land vehicle applications},
    Author = {Sukkarieh, S. and Nebot, E.M. and Durrant-Whyte, H.F.},
    Journal = {Robotics and Automation, IEEE Transactions on},
    Year = {1999},
    Month = {Jun},
    Number = {3},
    Pages = {572-578},
    Volume = {15},
    Doi = {10.1109/70.768189},
    ISSN = {1042-296X},
    Keywords = {Global Positioning System;computerised navigation;inertial navigation;mobile robots;vehicles;Global Positioning System;autonomous land vehicle applications;fault detection;high-frequency faults;high-integrity IMU/GPS navigation loop;inertial measurement unit;low-cost strapdown IMU;low-frequency faults;multipath errors;sensor fusion;Agriculture;Control systems;Fault detection;Freight handling;Frequency;Global Positioning System;Land vehicles;Measurement units;Navigation;Remotely operated vehicles}
    }

In Proceedings

  • A. Bender, J. R. Ward, S. Worrall, and E. M. Nebot, “Predicting Driver Intent from Models of Naturalistic Driving,” in Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on, 2015, pp. 1609-1615. doi:10.1109/ITSC.2015.262
    [BibTeX]
    @InProceedings{Bender2015b,
    Title = {Predicting Driver Intent from Models of Naturalistic Driving},
    Author = {Bender, Asher and Ward, James R. and Worrall, Stewart and Nebot, Eduardo M.},
    Booktitle = {Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on},
    Year = {2015},
    Month = {Sept},
    Pages = {1609-1615},
    Doi = {10.1109/ITSC.2015.262},
    Keywords = {Computational modeling;Data models;Hidden Markov models;Roads;Training;Trajectory;Vehicles}
    }

  • S. Worrall, J. Ward, A. Bender, and E. M. Nebot, “GPS/GNSS Consistency in a Multi-path Environment and During Signal Outages,” in Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on, 2015, pp. 2505-2511. doi:10.1109/ITSC.2015.408
    [BibTeX]
    @InProceedings{Worrall2015,
    Title = {{GPS}/{GNSS} Consistency in a Multi-path Environment and During Signal Outages},
    Author = {Worrall, Stewart and Ward, James and Bender, Asher and Nebot, Eduardo M.},
    Booktitle = {Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on},
    Year = {2015},
    Month = {Sept},
    Pages = {2505-2511},
    Doi = {10.1109/ITSC.2015.408},
    Keywords = {Global Positioning System;Logic gates;Noise;Satellites;Sensors;Uncertainty;Vehicles}
    }

  • G. Agamennoni, S. Worrall, J. R. Ward, and E. M. Nebot, “Automated extraction of driver behaviour primitives using Bayesian agglomerative sequence segmentation,” in Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on, 2014, pp. 1449-1455. doi:10.1109/ITSC.2014.6957890
    [BibTeX]
    @InProceedings{Agamennoni2014a,
    Title = {Automated extraction of driver behaviour primitives using Bayesian agglomerative sequence segmentation},
    Author = {Agamennoni, G. and Worrall, S. and Ward, J.R. and Nebot, E.M.},
    Booktitle = {Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on},
    Year = {2014},
    Month = {Oct},
    Pages = {1449-1455},
    Doi = {10.1109/ITSC.2014.6957890},
    Keywords = {Bayes methods;driver information systems;Bayesian agglomerative sequence segmentation;ITS applications;acceleration;automated extraction;braking;driver behaviour primitives;driving maneuvers;driving primitives;inertial measurement unit;minimal preprocessing;raw sensor data;statistical patterns;turning;Acceleration;Bayes methods;Hidden Markov models;Linear regression;Market research;Time series analysis;Vehicles}
    }

  • J. Ward, G. Agamennoni, S. Worrall, and E. Nebot, “Vehicle collision probability calculation for general traffic scenarios under uncertainty,” in Intelligent Vehicles Symposium Proceedings, 2014 IEEE, 2014, pp. 986-992. doi:10.1109/IVS.2014.6856430
    [BibTeX] [Abstract]

    Vehicle-to-vehicle (V2V) communication systems allow vehicles to share state information with one another to improve safety and efficiency of transportation networks. One of the key applications of such a system is in the prediction and avoidance of collisions between vehicles. If a method to do this is to succeed it must be robust to measurement uncertainty. The method should also be general enough that it does not rely on constraints on vehicle motion for the accuracy of its predictions. It should work for all interactions between vehicles and not just a select subset. This paper presents a method for collision probability calculation that addresses these problems.

    @InProceedings{ward2014vehicle,
    Title = {Vehicle collision probability calculation for general traffic scenarios under uncertainty},
    Author = {Ward, J. and Agamennoni, G. and Worrall, S. and Nebot, E.},
    Booktitle = {Intelligent Vehicles Symposium Proceedings, 2014 IEEE},
    Year = {2014},
    Month = {June},
    Pages = {986-992},
    Abstract = {Vehicle-to-vehicle (V2V) communication systems allow vehicles to share state information with one another to improve safety and efficiency of transportation networks. One of the key applications of such a system is in the prediction and avoidance of collisions between vehicles. If a method to do this is to succeed it must be robust to measurement uncertainty. The method should also be general enough that it does not rely on constraints on vehicle motion for the accuracy of its predictions. It should work for all interactions between vehicles and not just a select subset. This paper presents a method for collision probability calculation that addresses these problems.},
    Doi = {10.1109/IVS.2014.6856430},
    Keywords = {mobile communication;road safety;road traffic;traffic engineering computing;V2V;general traffic scenarios;measurement uncertainty;transportation network efficiency;transportation network safety;vehicle collision probability calculation;vehicle-to-vehicle communication systems;Mathematical model;Safety;Support vector machines;Trajectory;Uncertainty;Vectors;Vehicles}
    }

  • S. Worrall, J. Ward, G. Agamennoni, D. Orchansky, and E. Nebot, “Estimating time to interaction for vehicles in ITS applications,” in Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on, 2014, pp. 1053-1059.
    [BibTeX]
    @InProceedings{worrall2014estimating,
    Title = {Estimating time to interaction for vehicles in ITS applications},
    Author = {Worrall, Stewart and Ward, James and Agamennoni, Gabriel and Orchansky, David and Nebot, Eduardo},
    Booktitle = {Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on},
    Year = {2014},
    Organization = {IEEE},
    Pages = {1053--1059}
    }

  • G. Agamennoni, J. R. Ward, S. Worrall, and E. M. Nebot, “Bayesian model-based sequence segmentation for inferring primitives in driving-behavioral data,” in Information Fusion (FUSION), 2014 17th International Conference on, 2014, pp. 1-8.
    [BibTeX]
    @InProceedings{Agamennoni2014bayesian,
    Title = {Bayesian model-based sequence segmentation for inferring primitives in driving-behavioral data},
    Author = {Agamennoni, Gabriel and Ward, James R and Worrall, Stewart and Nebot, Eduardo M},
    Booktitle = {Information Fusion (FUSION), 2014 17th International Conference on},
    Year = {2014},
    Organization = {IEEE},
    Pages = {1--8}
    }

  • J. Ward, S. Worrall, G. Agamennoni, and E. Nebot, “Comprehensive data collection and context based metric evaluation for safety monitoring,” in Intelligent Transportation Systems – (ITSC), 2013 16th International IEEE Conference on, 2013, pp. 658-663. doi:10.1109/ITSC.2013.6728306
    [BibTeX] [Abstract]

    The only direct method to evaluate safety is to monitor the number of accidents and near misses. However, vehicle accidents are statistically infrequent and near misses are heavily underreported, making this approach unfeasible. An alternative strategy is to examine and evaluate metrics which have been shown to be precursors to accidents. The widespread use of metric evaluation for measuring safety in vehicle operations is limited due to a lack of ubiquitous data collection and communication systems. In addition, the effective evaluation of a safety metric is strongly dependent on the high-level context of the situation. This paper presents a system that records and exchanges data and context information to facilitate the calculation of informative safety metrics, and shows results from a number of implementations of this system in a mining context.

    @InProceedings{ward2013intelligent,
    Title = {Comprehensive data collection and context based metric evaluation for safety monitoring},
    Author = {Ward, J. and Worrall, S. and Agamennoni, G. and Nebot, E.},
    Booktitle = {Intelligent Transportation Systems - (ITSC), 2013 16th International IEEE Conference on},
    Year = {2013},
    Month = {Oct},
    Pages = {658-663},
    Abstract = {The only direct method to evaluate safety is to monitor the number of accidents and near misses. However, vehicle accidents are statistically infrequent and near misses are heavily underreported, making this approach unfeasible. An alternative strategy is to examine and evaluate metrics which have been shown to be precursors to accidents. The widespread use of metric evaluation for measuring safety in vehicle operations is limited due to a lack of ubiquitous data collection and communication systems. In addition, the effective evaluation of a safety metric is strongly dependent on the high-level context of the situation. This paper presents a system that records and exchanges data and context information to facilitate the calculation of informative safety metrics, and shows results from a number of implementations of this system in a mining context.},
    Doi = {10.1109/ITSC.2013.6728306},
    Keywords = {data recording;electronic data interchange;road accidents;road safety;road vehicles;traffic engineering computing;ubiquitous computing;autonomous vehicles;comprehensive context based metric evaluation;comprehensive data collection;data exchange;data recording;driver-assisted vehicles;informative safety metrics;safety evaluation;safety measurement;safety monitoring;semi-autonomous vehicles;ubiquitous communication systems;ubiquitous data collection;vehicle accidents;Context;Market research;Measurement;Monitoring;Roads;Safety;Vehicles}
    }

  • J. Ward, S. Worrall, G. Agamennoni, and E. Nebot, “Vehicle operation safety monitoring using context based metrics: A case study,” in Intelligent Vehicles Symposium Workshops (IV Workshops), 2013 IEEE, 2013, pp. 19-24. doi:10.1109/IVWorkshops.2013.6615220
    [BibTeX] [Abstract]

    This paper presents results of the deployment of vehicle safety systems developed by the Intelligent Vehicles and Safety Systems Group at the Australian Centre for Field Robotics. The technology was deployed within an active mine site in Australia undergoing standard open-cut mining operations. Data were collected from the vehicles and analysed in order to assess the overall safety and performance of the vehicle operations within the mine. Metrics calculated include distributions of vehicle paths along stretches of road for driving line analysis, and statistics around vehicle events such as overspeed and proximity to other vehicles.

    @InProceedings{Ward13,
    Title = {Vehicle operation safety monitoring using context based metrics: A case study},
    Author = {Ward, J. and Worrall, S. and Agamennoni, G. and Nebot, E.},
    Booktitle = {Intelligent Vehicles Symposium Workshops (IV Workshops), 2013 IEEE},
    Year = {2013},
    Month = {June},
    Pages = {19-24},
    Abstract = {This paper presents results of the deployment of vehicle safety systems developed by the Intelligent Vehicles and Safety Systems Group at the Australian Centre for Field Robotics. The technology was deployed within an active mine site in Australia undergoing standard open-cut mining operations. Data were collected from the vehicles and analysed in order to assess the overall safety and performance of the vehicle operations within the mine. Metrics calculated include distributions of vehicle paths along stretches of road for driving line analysis, and statistics around vehicle events such as overspeed and proximity to other vehicles.},
    Doi = {10.1109/IVWorkshops.2013.6615220},
    Keywords = {Global Positioning System;automated highways;data analysis;road safety;road vehicles;safety systems;Australian Centre for Field Robotics;GPS;Intelligent Vehicles and Safety Systems Group;active mine site;context based metrics;data analysis;driving line analysis;open-cut mining operations;vehicle events;vehicle operation safety monitoring system;vehicle paths;Global Positioning System;Measurement;Monitoring;Roads;Safety;Standards;Vehicles}
    }

  • G. Agamennoni, J. R. Ward, S. Worrall, and E. M. Nebot, “Anomaly detection in driving behaviour by road profiling,” in Intelligent Vehicles Symposium Workshops (IV Workshops), 2013 IEEE, 2013, pp. 25-30. doi:10.1109/IVWorkshops.2013.6615221
    [BibTeX] [Abstract]

    This paper presents a statistical method for detecting anomalous driving behavior by analyzing cross-sectional profiles of the road. A profile captures the way vehicles normally traverse the road; a statistical hypothesis test determines whether the observed behavior is anomalous. Experimental results on genuine data collected by a fleet of vehicles demonstrates the potential of this new method.

    @InProceedings{Agamennoni13a,
    Title = {Anomaly detection in driving behaviour by road profiling},
    Author = {Agamennoni, G. and Ward, J.R. and Worrall, S. and Nebot, E.M.},
    Booktitle = {Intelligent Vehicles Symposium Workshops (IV Workshops), 2013 IEEE},
    Year = {2013},
    Month = {June},
    Pages = {25-30},
    Abstract = {This paper presents a statistical method for detecting anomalous driving behavior by analyzing cross-sectional profiles of the road. A profile captures the way vehicles normally traverse the road; a statistical hypothesis test determines whether the observed behavior is anomalous. Experimental results on genuine data collected by a fleet of vehicles demonstrates the potential of this new method.},
    Doi = {10.1109/IVWorkshops.2013.6615221},
    Keywords = {road safety;road traffic;statistical analysis;anomalous driving behavior detection;road profiling;statistical hypothesis test;statistical method;Australia;Conferences;Cost function;Global Positioning System;Probability;Roads;Vehicles}
    }

  • S. Worrall, G. Agamennoni, J. Ward, and E. Nebot, “Fault detection for vehicular ad-hoc wireless networks,” in Intelligent Vehicles Symposium (IV), 2013 IEEE, 2013, pp. 298-303. doi:10.1109/IVS.2013.6629485
    [BibTeX] [Abstract]

    An increasing number of intelligent transportation applications require robust and reliable wireless communication. To achieve the required quality of service it is necessary to implement redundancy in the critical path which includes the radio software and hardware. In a real-world application there are many things that can cause the communication between two vehicles to degrade or stop completely. This paper describes a novel technique for detecting degradation or failure of communication links by comparing the performance of the radios to a probabilistic model built using data collected in the field. The results show that this techinique can successfully detect when there is partial or complete failure to communicate due to damage to the external components such as antennas, connectors and cables.

    @InProceedings{Worrall13,
    Title = {Fault detection for vehicular ad-hoc wireless networks},
    Author = {Worrall, S. and Agamennoni, G. and Ward, J. and Nebot, E.},
    Booktitle = {Intelligent Vehicles Symposium (IV), 2013 IEEE},
    Year = {2013},
    Month = {June},
    Pages = {298-303},
    Abstract = {An increasing number of intelligent transportation applications require robust and reliable wireless communication. To achieve the required quality of service it is necessary to implement redundancy in the critical path which includes the radio software and hardware. In a real-world application there are many things that can cause the communication between two vehicles to degrade or stop completely. This paper describes a novel technique for detecting degradation or failure of communication links by comparing the performance of the radios to a probabilistic model built using data collected in the field. The results show that this techinique can successfully detect when there is partial or complete failure to communicate due to damage to the external components such as antennas, connectors and cables.},
    Doi = {10.1109/IVS.2013.6629485},
    ISSN = {1931-0587},
    Keywords = {fault diagnosis;probability;quality of service;radiowave propagation;telecommunication network reliability;vehicular ad hoc networks;communication links;critical path;fault detection;intelligent transportation;probabilistic model;quality of service;reliable wireless communication;vehicular ad hoc wireless networks;Antenna measurements;Antennas;Data models;Logistics;Mathematical model;Vehicles;Wireless communication}
    }

  • B. Clarke, S. Worrall, G. Brooker, and E. Nebot, “Towards mapping of dynamic environments with FMCW radar,” in Intelligent Vehicles Symposium Workshops (IV Workshops), 2013 IEEE, 2013, pp. 140-145. doi:10.1109/IVWorkshops.2013.6615240
    [BibTeX] [Abstract]

    Frequency-modulated continuous waveform (FMCW) microwave and millimetre-wave radar is an attractive sensor for intelligent transport systems due to its reliable all-weather performance. This paper discusses issues involved in the design of FMCW radar mapping systems for use in collision avoidance in large vehicles operating in dynamic environments. The performance characteristics of radar are examined before an analysis is made of traditional grid-based and feature-based mapping approaches, both conceptually and in terms of implementation. The probability hypothesis density (PHD) filter is discussed as a potentially superior approach for radar mapping in dynamic environments.

    @InProceedings{clarke2013towards,
    Title = {Towards mapping of dynamic environments with FMCW radar},
    Author = {Clarke, B. and Worrall, S. and Brooker, G. and Nebot, E.},
    Booktitle = {Intelligent Vehicles Symposium Workshops (IV Workshops), 2013 IEEE},
    Year = {2013},
    Month = {June},
    Pages = {140-145},
    Abstract = {Frequency-modulated continuous waveform (FMCW) microwave and millimetre-wave radar is an attractive sensor for intelligent transport systems due to its reliable all-weather performance. This paper discusses issues involved in the design of FMCW radar mapping systems for use in collision avoidance in large vehicles operating in dynamic environments. The performance characteristics of radar are examined before an analysis is made of traditional grid-based and feature-based mapping approaches, both conceptually and in terms of implementation. The probability hypothesis density (PHD) filter is discussed as a potentially superior approach for radar mapping in dynamic environments.},
    Doi = {10.1109/IVWorkshops.2013.6615240},
    Keywords = {CW radar;FM radar;automated highways;collision avoidance;millimetre wave radar;probability;road vehicle radar;FMCW microwave radar;FMCW radar;PHD filter;all-weather performance;collision avoidance;dynamic environment mapping;feature-based mapping approach;frequency-modulated continuous waveform radar;grid-based mapping approach;intelligent transport system;large vehicles;millimetre-wave radar;probability hypothesis density filter;radar mapping;radar performance characteristics;Clutter;Noise;Radar cross-sections;Uncertainty;Vehicles}
    }

  • G. Agamennoni, S. Worrall, J. Ward, and E. Nebot, “Robust non-linear smoothing for vehicle state estimation,” in Intelligent Vehicles Symposium (IV), 2013 IEEE, 2013, pp. 156-162. doi:10.1109/IVS.2013.6629464
    [BibTeX] [Abstract]

    This paper presents a robust, non-linear smoothing algorithm and develops the theory behind it. This algorithm is extremely robust to outliers and missing data and handles state-dependent noise. Implementing it is straightforward as it consists mainly of two sub-routines: (a) the Rauch-Tung-Striebel recursions, or Kalman smoother; and (b) a backtracking line search strategy. The computational load grows linearly with the number of data because the algorithm preserves the underlying structure of the problem. Global convergence to a local optimum is guaranteed, under mild assumptions.

    @InProceedings{Agamennoni13,
    Title = {Robust non-linear smoothing for vehicle state estimation},
    Author = {Agamennoni, G. and Worrall, S. and Ward, J. and Nebot, E.},
    Booktitle = {Intelligent Vehicles Symposium (IV), 2013 IEEE},
    Year = {2013},
    Month = {June},
    Pages = {156-162},
    Abstract = {This paper presents a robust, non-linear smoothing algorithm and develops the theory behind it. This algorithm is extremely robust to outliers and missing data and handles state-dependent noise. Implementing it is straightforward as it consists mainly of two sub-routines: (a) the Rauch-Tung-Striebel recursions, or Kalman smoother; and (b) a backtracking line search strategy. The computational load grows linearly with the number of data because the algorithm preserves the underlying structure of the problem. Global convergence to a local optimum is guaranteed, under mild assumptions.},
    Doi = {10.1109/IVS.2013.6629464},
    ISSN = {1931-0587},
    Keywords = {convergence;road vehicles;search problems;smoothing methods;state estimation;Kalman smoother;Rauch-Tung-Striebel recursions;backtracking line search strategy;global convergence;robust nonlinear smoothing algorithm;state-dependent noise;vehicle state estimation;Approximation methods;Convergence;Global Positioning System;Robot sensing systems;Robustness;Smoothing methods;Vehicles}
    }

  • B. Clarke, S. Worrall, G. Brooker, and E. Nebot, “Sensor modelling for radar-based occupancy mapping,” in Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, 2012, pp. 3047-3054. doi:10.1109/IROS.2012.6386255
    [BibTeX] [Abstract]

    This paper addresses the issue of creating a sensor model for a new short-range 24GHz close proximity detection (CPD) radar. The CPD radar is designed to provide improved situational awareness to the driver of a large vehicle. It is able to detect light vehicles and other targets at ranges from 2.2m to 45m within an arc of 160\textdegree azimuth, but radar measurements contain data from noise and clutter which must be filtered out. Dynamic thresholds such as constant false-alarm rate (CFAR) processors [19] do not work well with short measurement vectors or targets that occupy multiple measurement bins. In this paper, a new method is used where measurements of environmental noise, clutter and targets are used to calculate the false alarm and target detection probabilities for each bin and develop a fixed detection threshold for each bin. This filter is used to construct a sensor model which maps measurement power to probability of bin occupancy, which is then used to generate an occupancy grid map of the environment from CPD radar measurements.

    @InProceedings{clarke2012sensor,
    Title = {Sensor modelling for radar-based occupancy mapping},
    Author = {Clarke, B. and Worrall, S. and Brooker, G. and Nebot, E.},
    Booktitle = {Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on},
    Year = {2012},
    Month = {Oct},
    Pages = {3047-3054},
    Abstract = {This paper addresses the issue of creating a sensor model for a new short-range 24GHz close proximity detection (CPD) radar. The CPD radar is designed to provide improved situational awareness to the driver of a large vehicle. It is able to detect light vehicles and other targets at ranges from 2.2m to 45m within an arc of 160\textdegree azimuth, but radar measurements contain data from noise and clutter which must be filtered out. Dynamic thresholds such as constant false-alarm rate (CFAR) processors [19] do not work well with short measurement vectors or targets that occupy multiple measurement bins. In this paper, a new method is used where measurements of environmental noise, clutter and targets are used to calculate the false alarm and target detection probabilities for each bin and develop a fixed detection threshold for each bin. This filter is used to construct a sensor model which maps measurement power to probability of bin occupancy, which is then used to generate an occupancy grid map of the environment from CPD radar measurements.},
    Doi = {10.1109/IROS.2012.6386255},
    ISSN = {2153-0858},
    Keywords = {driver information systems;object detection;radar clutter;radar detection;CPD radar;close proximity detection radar;clutter;environmental noise;frequency 24 GHz;light vehicle detection;radar-based occupancy mapping;sensor modelling;situational awareness;size 2.2 m to 45 m;target detection;Clutter;Rotation measurement}
    }

  • B. Clarke, S. Worrall, G. Brooker, and E. Nebot, “Towards mapping of dynamic environments with FMCW radar,” in Intelligent Vehicles Symposium (IV), 2012 IEEE, 2012, pp. 535-540.
    [BibTeX]
    @InProceedings{clarke2012improving,
    Title = {Improving situational awareness with radar information},
    Author = {Clarke, Bryan and Worrall, Stewart and Brooker, Graham and Martinez, Javier and Nebot, Eduardo@InProceedings{clarke2013towards, Title = {Towards mapping of dynamic environments with FMCW radar}, Author = {Clarke, Bryan and Worrall, Stewart and Brooker, Graham and Nebot, Eduardo}, Booktitle = {Intelligent Vehicles Symposium (IV), 2013 IEEE}, Year = {2013}, Organization = {IEEE}, Pages = {147--152} } @InProceedings{clarke2013towards, Title = {Towards mapping of dynamic environments with FMCW radar}, Author = {Clarke, Bryan and Worrall, Stewart and Brooker, Graham and Nebot, Eduardo}, Booktitle = {Intelligent Vehicles Symposium (IV), 2013 IEEE}, Year = {2013}, Organization = {IEEE}, Pages = {147--152} }},
    Booktitle = {Intelligent Vehicles Symposium (IV), 2012 IEEE},
    Year = {2012},
    Organization = {IEEE},
    Pages = {535--540},
    Owner = {stewart},
    Timestamp = {2014.08.15}
    }

  • M. Shan, S. Worrall, and E. Nebot, “Long term vehicle motion prediction and tracking in large environments,” in Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on, 2011, pp. 1978-1983. doi:10.1109/ITSC.2011.6082922
    [BibTeX] [Abstract]

    Vehicle motion tracking and prediction over large areas is of significant importance in many industrial applications. This paper presents algorithms for long term vehicle prediction and tracking based on a model of the vehicle that incorporates the properties of the working environment. It uses a limited number of data collection points distributed around the field to update estimates when vehicles are in range of the collection points. The algorithm evaluates the prediction and tracking of vehicle positions using speed and timing profiles built for the particular environment and considering vehicle stopping probability. Positive and negative information from observers is also introduced in the fusion stage. Experimental results from a large scale mining operation using peer to peer communication system are presented to validate the algorithm.

    @InProceedings{shan2011long,
    Title = {Long term vehicle motion prediction and tracking in large environments},
    Author = {Mao Shan and Worrall, S. and Nebot, E.},
    Booktitle = {Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on},
    Year = {2011},
    Month = {Oct},
    Pages = {1978-1983},
    Abstract = {Vehicle motion tracking and prediction over large areas is of significant importance in many industrial applications. This paper presents algorithms for long term vehicle prediction and tracking based on a model of the vehicle that incorporates the properties of the working environment. It uses a limited number of data collection points distributed around the field to update estimates when vehicles are in range of the collection points. The algorithm evaluates the prediction and tracking of vehicle positions using speed and timing profiles built for the particular environment and considering vehicle stopping probability. Positive and negative information from observers is also introduced in the fusion stage. Experimental results from a large scale mining operation using peer to peer communication system are presented to validate the algorithm.},
    Doi = {10.1109/ITSC.2011.6082922},
    ISSN = {2153-0009},
    Keywords = {tracking;traffic engineering computing;large environments;long term vehicle motion prediction;peer to peer communication system;speed profile;timing profile;vehicle motion tracking;vehicle position;vehicle stopping probability;Observers;Probability density function;Roads;Timing;Tracking;Uncertainty;Vehicles}
    }

  • G. Agamennoni, J. I. Nieto, and E. M. Nebot, “A Bayesian Approach for Driving Behavior Inference,” in IEEE Intelligent Vehicles Symposium, 2011, pp. 595-600.
    [BibTeX]
    @InProceedings{Agamennoni2011,
    Title = {A {B}ayesian Approach for Driving Behavior Inference},
    Author = {Agamennoni, G. and Nieto, J.I. and Nebot, E.M.},
    Booktitle = {IEEE Intelligent Vehicles Symposium},
    Year = {2011},
    Month = {June},
    Pages = {595--600}
    }

  • G. Agamennoni, J. I. Nieto, and E. M. Nebot, “An Outlier-robust Kalman Filter,” in Proceedings of the 2011 IEEE International Conference on Robotics and Automation, 2011.
    [BibTeX]
    @InProceedings{Agamennoni2011b,
    Title = {An Outlier-robust {K}alman Filter},
    Author = {Agamennoni, G. and Nieto, J.I. and Nebot, E.M.},
    Booktitle = {Proceedings of the 2011 IEEE International Conference on Robotics and Automation},
    Year = {2011},
    Owner = {gaga9329},
    Timestamp = {2011.01.09}
    }

  • D. Orchansky, S. Worrall, A. Maclean, and E. Nebot, “Designing a User Interface for Improving the Awareness of Mining Vehicle Operators,” in IEEE International Conference on Intelligent Transportation Systems, 2010, pp. 1435-1441.
    [BibTeX] [Abstract]

    Vehicle accidents are a major concern in open pit mines around the world. Factors such as visibility, fatigue and human error are the cause of many collisions in surface mines. These factors have a direct impact in the awareness of the vehicle operators. Intelligent transportation systems can address these factors and consequently benefit vehicular safety. Technology has the potential to effectively aid the operator in the tasks of driving and positively contribute in the overall safety of the mine. However, the design and implementation of these systems and their user interfaces is not trivial. This paper presents a strategy for improving the drivers awareness and introduces the design of a user interface that helps the operator in the process of decision making. The system infers the presence of nearby threats and high risk situations and communicates this information to the operator in a combination of audio-visual cues. This paper presents an overview of the mechanism designed to infer high risk situations. Experimental results obtained from real-life operation of the system in several mine sites in Australia and overseas demonstrated how the information provided can enhance the operators awareness and contribute to an improvement in vehicle safety.

    @InProceedings{Orchansky2010,
    Title = {Designing a User Interface for Improving the Awareness of Mining Vehicle Operators},
    Author = {David Orchansky and Stewart Worrall and Andrew Maclean and Eduardo Nebot},
    Booktitle = {IEEE International Conference on Intelligent Transportation Systems},
    Year = {2010},
    Month = {September},
    Pages = {1435-1441},
    Abstract = {Vehicle accidents are a major concern in open pit mines around the world. Factors such as visibility, fatigue and human error are the cause of many collisions in surface mines. These factors have a direct impact in the awareness of the vehicle operators. Intelligent transportation systems can address these factors and consequently benefit vehicular safety. Technology has the potential to effectively aid the operator in the tasks of driving and positively contribute in the overall safety of the mine. However, the design and implementation of these systems and their user interfaces is not trivial. This paper presents a strategy for improving the drivers awareness and introduces the design of a user interface that helps the operator in the process of decision making. The system infers the presence of nearby threats and high risk situations and communicates this information to the operator in a combination of audio-visual cues. This paper presents an overview of the mechanism designed to infer high risk situations. Experimental results obtained from real-life operation of the system in several mine sites in Australia and overseas demonstrated how the information provided can enhance the operators awareness and contribute to an improvement in vehicle safety.},
    Keywords = {Hmi and Human-Machine Interaction, Driver Assistance Systems, Advanced Vehicle Safety Systems},
    Owner = {dorchansky},
    Timestamp = {2010.10.22}
    }

  • A. Arelovich, F. Masson, O. Agamennoni, S. Worrall, and E. Nebot, “Heuristic rule for truck dispatching in open-pit mines with local information-based decisions,” in Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on, 2010, pp. 1408-1414. doi:10.1109/ITSC.2010.5625231
    [BibTeX]
    @InProceedings{arelovich2010heuristic,
    Title = {Heuristic rule for truck dispatching in open-pit mines with local information-based decisions},
    Author = {Arelovich, A and Masson, F. and Agamennoni, O. and Worrall, S. and Nebot, E.},
    Booktitle = {Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on},
    Year = {2010},
    Month = {Sept},
    Pages = {1408-1414},
    Doi = {10.1109/ITSC.2010.5625231},
    ISSN = {2153-0009},
    Keywords = {estimation theory;mining;peer-to-peer computing;probability;traffic engineering computing;heuristic rule;local information-based decisions;open-pit mines;peer to peer communication system;probability density function estimation;truck dispatching;vehicle position estimation;Accidents;Dispatching;Driver circuits;Histograms;Mathematical model;Roads;Vehicles}
    }

  • S. Worrall, D. Orchansky, F. Masson, and E. Nebot, “Improving vehicle safety using context based detection of risk,” in Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on, 2010, pp. 379-385. doi:10.1109/ITSC.2010.5625185
    [BibTeX] [Abstract]

    When mining vehicle operators take risks, there is a increased probability of an accident that can cause injuries, fatalities and large financial costs to the mine operators. It can be assumed that the operators do not intentially take unnecessarily high risk, and that the risks are hidden due to factors such as adverse weather, fatigue, visual obstructions, boredom, etc. This paper examines the potential of measuring the risk of danger in a situation by using the safe rules of operation defined by mining safety management. The problem with measuring safety is that the safe rules of operation are heavily dependent on the context of the situation. What is considered normal practice and safe in one part of the mine (such as performing a u-turn in a parking lot) is not safe on a haul road. To be able to measure safety, it is therefore necessary to understand the different context areas in a mine so that feedback of unsafe behaviour presented to the operator is relevant to the context of the situation. This paper presents a novel method for generating context area information using the vehicle trajectory information collected from vehicles in the mine. Results are presented using real-life data collected from several operating fleets of mining vehicles.

    @InProceedings{worrall2010improving,
    Title = {Improving vehicle safety using context based detection of risk},
    Author = {Worrall, S. and Orchansky, D. and Masson, F. and Nebot, E.},
    Booktitle = {Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on},
    Year = {2010},
    Month = {Sept},
    Pages = {379-385},
    Abstract = {When mining vehicle operators take risks, there is a increased probability of an accident that can cause injuries, fatalities and large financial costs to the mine operators. It can be assumed that the operators do not intentially take unnecessarily high risk, and that the risks are hidden due to factors such as adverse weather, fatigue, visual obstructions, boredom, etc. This paper examines the potential of measuring the risk of danger in a situation by using the safe rules of operation defined by mining safety management. The problem with measuring safety is that the safe rules of operation are heavily dependent on the context of the situation. What is considered normal practice and safe in one part of the mine (such as performing a u-turn in a parking lot) is not safe on a haul road. To be able to measure safety, it is therefore necessary to understand the different context areas in a mine so that feedback of unsafe behaviour presented to the operator is relevant to the context of the situation. This paper presents a novel method for generating context area information using the vehicle trajectory information collected from vehicles in the mine. Results are presented using real-life data collected from several operating fleets of mining vehicles.},
    Doi = {10.1109/ITSC.2010.5625185},
    ISSN = {2153-0009},
    Keywords = {mining industry;probability;risk management;road safety;traffic information systems;context areas;context based detection;financial costs;mine operators;mining safety management;mining vehicle operators;real-life data;unsafe behaviour;vehicle safety improvement;Context;Data mining;Joining processes;Roads;Safety;Shape;Vehicles}
    }

  • G. Agamennoni, J. I. Nieto, and E. M. Nebot, “Robust and accurate road map inference,” in Robotics and Automation (ICRA), 2010 IEEE International Conference on, 2010, pp. 3946-3953. doi:10.1109/ROBOT.2010.5509778
    [BibTeX] [Abstract]

    Over the last ten years, electronic vehicle guidance systems have become increasingly popular. However, their performance is subject to the availability and accuracy of digital road maps. Most current digital maps are still inadequate for advanced applications in unstructured environments. Lack of detailed up-to-date information and insufficient accuracy and refinement of the road geometry are among the most important shortcomings. The massive use of inexpensive GPS receivers, combined with the rapidly increasing availability of wireless communication infrastructure, suggests that large volumes of data combining both modalities will be available in a near future. The approach presented here draws on machine learning techniques to process logs of position traces to consistently build a detailed and accurate representation of the road network and, more importantly, extract the actual paths followed by vehicles. Experimental results with data from large mining operations are presented to validate the algorithm.

    @InProceedings{Agamennoni09,
    Title = {Robust and accurate road map inference},
    Author = {Agamennoni, G. and Nieto, J.I and Nebot, E.M.},
    Booktitle = {Robotics and Automation (ICRA), 2010 IEEE International Conference on},
    Year = {2010},
    Month = {May},
    Pages = {3946-3953},
    Abstract = {Over the last ten years, electronic vehicle guidance systems have become increasingly popular. However, their performance is subject to the availability and accuracy of digital road maps. Most current digital maps are still inadequate for advanced applications in unstructured environments. Lack of detailed up-to-date information and insufficient accuracy and refinement of the road geometry are among the most important shortcomings. The massive use of inexpensive GPS receivers, combined with the rapidly increasing availability of wireless communication infrastructure, suggests that large volumes of data combining both modalities will be available in a near future. The approach presented here draws on machine learning techniques to process logs of position traces to consistently build a detailed and accurate representation of the road network and, more importantly, extract the actual paths followed by vehicles. Experimental results with data from large mining operations are presented to validate the algorithm.},
    Doi = {10.1109/ROBOT.2010.5509778},
    ISSN = {1050-4729},
    Keywords = {Global Positioning System;cartography;data mining;driver information systems;learning (artificial intelligence);GPS receivers;data mining;digital road maps;electronic vehicle guidance systems;machine learning techniques;road geometry;road map inference;wireless communication infrastructure;Availability;Data mining;Global Positioning System;Information geometry;Machine learning;Navigation;Roads;Robustness;Vehicles;Wireless communication}
    }

  • G. Agamennoni, J. I. Nieto, and E. M. Nebot, “Robust and Accurate Road Map Inference,” in Proceedings of the 2009 IEEE International Conference on Robotics and Automation, 2009.
    [BibTeX]
    @InProceedings{Agamennoni2009,
    Title = {Robust and Accurate Road Map Inference},
    Author = {Gabriel Agamennoni and Juan Ignacio Nieto and Eduardo Mario Nebot},
    Booktitle = {Proceedings of the 2009 IEEE International Conference on Robotics and Automation},
    Year = {2009},
    Owner = {gaga9329},
    Timestamp = {2010.05.30}
    }

  • S. Worrall and E. M. Nebot, “A Probabilistic Method for Detecting Impending Vehicle Interactions,” in Proceedings of the 2008 IEEE International Conference on Robotics and Automation, 2008, pp. 1787-1791. doi:10.1109/ROBOT.2008.4543467
    [BibTeX]
    @InProceedings{Worrall08,
    Title = {A Probabilistic Method for Detecting Impending Vehicle Interactions},
    Author = {Worrall, S. and Nebot, E.M.},
    Booktitle = {Proceedings of the 2008 IEEE International Conference on Robotics and Automation},
    Year = {2008},
    Month = {May},
    Pages = {1787--1791},
    Doi = {10.1109/ROBOT.2008.4543467},
    ISSN = {1050-4729},
    Keywords = {collision avoidance, risk management, vehicles autonomous mining, collision avoidance, impending vehicle interactions detection, maneuvers risk assessment, nonautonomous mining, probabilistic method, risk assessment, short term path planning, situational awareness}
    }

  • G. Agamennoni, J. I. Nieto, and E. M. Nebot, “Mining GPS Data for Extracting Significant Places,” in Proceedings of the 2008 IEEE International Conference on Robotics and Automation, 2008.
    [BibTeX]
    @InProceedings{Agamennoni2008,
    Title = {Mining {GPS} Data for Extracting Significant Places},
    Author = {Agamennoni, G. and Nieto, J.I. and Nebot, E.M.},
    Booktitle = {Proceedings of the 2008 IEEE International Conference on Robotics and Automation},
    Year = {2008}
    }

  • D. Orchansky, S. Worrall, and E. Nebot, “An Effective Way of Displaying Situation Awareness Information in Mining Vehicles.” 2008.
    [BibTeX]
    @InProceedings{orchansky2008effective,
    Title = {An Effective Way of Displaying Situation Awareness Information in Mining Vehicles},
    Author = {Orchansky, D and Worrall, S and Nebot, E},
    Year = {2008},
    Journal = {2008 Australian Mining Technology Conference},
    Owner = {stewart},
    Timestamp = {2014.08.15}
    }

  • S. Worrall and E. Nebot, “Truck Localisation in a Mine Using Sparse Observations,” in Australasian Conference on Robotics and Automation, 2008.
    [BibTeX]
    @InProceedings{worrall2008truck,
    Title = {Truck Localisation in a Mine Using Sparse Observations},
    Author = {Worrall, Stewart and Nebot, Eduardo},
    Booktitle = {Australasian Conference on Robotics and Automation},
    Year = {2008},
    Owner = {stewart},
    Timestamp = {2014.08.15}
    }

  • S. Worrall and E. M. Nebot, “Using Non-parametric Filters and Sparse Observations to Localise a Fleet of Mining Vehicles,” in Proceedings of the 2007 IEEE International Conference on Robotics and Automation, 2007, pp. 509-516. doi:10.1109/ROBOT.2007.363837
    [BibTeX]
    @InProceedings{Worrall07,
    Title = {Using Non-parametric Filters and Sparse Observations to Localise a Fleet of Mining Vehicles},
    Author = {Worrall, S. and Nebot, E.M.},
    Booktitle = {Proceedings of the 2007 IEEE International Conference on Robotics and Automation},
    Year = {2007},
    Month = {April},
    Pages = {509--516},
    Doi = {10.1109/ROBOT.2007.363837},
    ISSN = {1050-4729},
    Keywords = {Global Positioning System, filtering theory, mining, mining equipment, nonparametric statistics, traffic engineering computingmining operation, mining vehicle localisation, nonparametric filters, radio network, sparse observation, vehicle management, vehicle position, wireless network}
    }

  • S. Worrall and E. Nebot, “Automated process for generating digitised maps through GPS data compression,” in Australasian Conference on Robotics and Automation, 2007.
    [BibTeX]
    @InProceedings{Worrall07a,
    Title = {Automated process for generating digitised maps through {GPS} data compression},
    Author = {Worrall, Stewart and Nebot, Eduardo},
    Booktitle = {Australasian Conference on Robotics and Automation},
    Year = {2007}
    }