Future Students

The next few years will see a significant transformation of the automotive and transportation industry. The introduction of communication between vehicles and infrastructure and new sophisticated perception technology will enable cooperative operations dramatically changing the way we will design and use transportation systems. The introduction of different level of autonomy in urban roads will require breakthroughs in position, perception and communication.

The first stage will see the introduction of cooperative ITS application with the introduction of Vehicle to Vehicle (V2V) and Vehicle to infrastructure (V2I) technology. This will enable cooperative safety applications demanding positioning with a required accuracy not available in most areas. We will expect vehicles retrofitted with standard communication capabilities and a number of internal and external perception sensors. Although the sensors will be part of a particular vehicle functionality, their information will also be available for other applications.

The second stage will include the deployment of fully autonomous vehicles and the interaction with all existing road users: vehicles, bicycles and pedestrian. Truly intelligent vehicles must be capable of developing a high-level understanding of the traffic scene in order to drive safely and effectively amongst human drivers as well as other intelligent vehicles. Acquiring the necessary information is a challenging task since contextual variables are not directly observable and must be inferred from low-level data.

The projects below present some of the areas that will be addressed as PhD / Masters research projects.

Cooperative Situation Awareness

Situation awareness involves the sensing of the local environment, understanding the situation and predicting the future state. For Intelligent Transportation Systems, situational awareness is essential for detecting unsafe behaviours and for allowing the introduction of autonomous systems into complex traffic scenarios. Cooperative situation awareness involves the sharing of information between local groups of vehicles to improve the understanding of the current scenario. By fusing the information received over communication networks it is possible for vehicles to have a better understanding of the risks, allowing safer operation and reducing accidents. Furthermore, existing fleet of vehicles will be able to share high level perception capabilities provided by smart autonomous vehicles operating in the proximity area. This project will research into multimodal perception and efficient communication of vital information between vehicles.

Contact

  • s.worrall@acfr.usyd.edu.au
  • nebot@acfr.usyd.edu.au

Vehicle Localisation and Sensor Fusion

One of the fundamental requirements of Cooperative Intelligent Transport Systems (C-ITS) is navigation ie knowing the position, velocity and attitude of the vehicle at all times with a figure representing the uncertainty. A probabilistic estimate of these properties can be obtained by fusing information from GPS/GLONASS receivers and local reference sensors such as gyroscopes, accelerometers, wheel encoders and other vehicle sensors. C-ITS systems will require different levels of accuracy for different applications. The accuracy required has been categorized according to the following levels:

  • Road level: (5 meters, 1-5 sec). Required to know which road the vehicle is navigating and which other vehicles are in proximity. This enables proximity awareness capabilities in automotive and other industry applications
  • Lane-level: (1.5 meter, 1 sec). Enables applications relating lane level vehicle interactions
  • Where in the lane: (< 1 meter , 0.1 sec). Enables applications for warning of crossing lanes etc.

An additional specification can be stated for high performance C-ITS and autonomous vehicles:

  • Lateral / longitudinal road location: ( < 0.1 meters, 0.1 sec ). Enable high speed interaction / autonomous applications.

Current GNSS solutions can only provide information for basic applications such as GPS direction assistance. The position estimate to satisfy other requirements can be improved by incorporating information from a map or environment model. A key area of research in ITS is the fusion of available sensor information to provide a high integrity position and state estimate. An additional important constraint is the use of low cost sensors to increase the uptake of this technology into the general vehicle population. This information is essential in modern vehicle safety and automation applications.

Contact

  • s.worrall@acfr.usyd.edu.au
  • nebot@acfr.usyd.edu.au

Collision Prediction and Avoidance Under Uncertainty

All positioning systems feature uncertainty in the reported position. Any system for predicting and avoiding collisions between intelligent vehicles must account for this uncertainty. Probabilistic models of vehicle conflicts present a promising avenue for tackling this problem. This work can be further extended to examine trustworthiness of state information shared by other vehicles, and develop a method of obtaining maximum value from the information given its trustworthiness. Trustworthiness may be influenced by sensing uncertainty, sensor failure or deliberate falsehood. Detecting and acting on this will be vital for any future intelligent vehicle fleet.

Contact

  • j.ward@acfr.usyd.edu.au
  • nebot@acfr.usyd.edu.au

Urban Street Mapping

This project addresses the evaluation of global maps to assist vehicle navigation. The main goal is the determination of complete set of feature to achieve high integrity navigation with the level of accuracy for autonomous operations.

Road/Lane Maps: The first stage will look at mapping of all vehicle / cycle / ramps lanes. The project will investigate algorithm to be used with high quality perception sensors. It will further extend these techniques to be used with low cost sensors for rapid deployment in all roads around Australia. It will also research into other type of infrastructure that could be sensed to improve the integrity of localisation. The aim of this infrastructure will be reliable detection by multimodal sensing to improve the integrity under all weather conditions. This will also look at the combination of radar signal processing and beacon design to be able to localise under all weather conditions.

3D salient Features: Road infrastructure will play an important role for localisation, specially in motorways, country roads and open areas. Urban areas will present different challenges since high dense traffic could prevent detection of visual artificial landmarks at road level. Nevertheless, the combination of building and infrastructure usually presents very high density salient features that remain static with time. This project will develop methods to obtain navigation maps that can be downloaded in an efficient manner through V2I infrastructure to be used by map matching algorithm to register current mobile position.

Contact

  • s.worrall@acfr.usyd.edu.au
  • nebot@acfr.usyd.edu.au

Data Analytics for Naturalistic Driving

Large scale trials of Intelligent Transportation Systems will generate vast amounts of data that is reflective of real world scenarios rather than artificially constrained by experimental design. Methods will need to be developed to capture, warehouse and analyse this information. Vehicles are equipped with sensors which can directly measure the vehicle state. This data allows algorithms to generate hypotheses about the behaviour of the driver and the vehicle. However, measurements of the vehicle state only form indirect observations of driver behaviour. It is difficult to directly measure human actions without biasing their behaviour. Utilising non-invasive measurement technologies such as estimating head orientation, gaze tracking and driver posture provides a means for collecting direct observations of the driver. This expressive data can be used by learning algorithms to model driver behaviour in natural driving conditions.

Contact

  • s.worrall@acfr.usyd.edu.au
  • j.ward@acfr.usyd.edu.au
  • nebot@acfr.usyd.edu.au

Driver Intent

Human drivers possess a natural ability to perceive our surroundings from the point of view of other people and reason about their intentions. To drive safely, we must develop a high-level understanding of the situation. The estimation of driver intent is a key research area in the domain of Intelligent Transportation Systems. Mathematical tools are required to take a range of vehicle sensor information and attempt to match the reasoning carried out by a human driver. The resulting estimates will form the basis for collision risk assessment and probabilistic decision-making.

Contact

  • a.bender@acfr.usyd.edu.au
  • nebot@acfr.usyd.edu.au

Transport Safety Evaluation and Monitoring

As Intelligent Transportation Systems are developed and deployed there will be a need for evaluating the impact upon safety that they bring. High quality, high frequency data will be available from a variety of sources (in-vehicle sensors, roadside infrastructure) that have not been analysed before. By developing techniques for analysing this data, transport safety can be evaluated and resources targeted at the highest risk areas. This analysis will also be necessary to justify the investments in ITS as they are deployed, and to monitor their effect and ensure that they are achieving their aim of increasing safety.

Contact

  • j.ward@acfr.usyd.edu.au
  • nebot@acfr.usyd.edu.au

Vulnerable Road User Detection & Intent Prediction

Autonomous systems can be designed to operate reliably in highly structured environments. Although road-rules provide structure to traffic situations, roads frequently contain unpredictable conditions and scenarios which can violate assumptions. Pedestrians and cyclists are an important example, particularly in densely populated urban environments. To minimise the risk autonomous systems impose on these vulnerable road users, systems must be designed specifically to consider them. The goal is to identify pedestrians, model their trajectories and predict their intent. To achieve these goals, research in this area will draw from the fields of perception, computer vision, filtering and machine learning.

Contact

  • a.bender@acfr.usyd.edu.au
  • nebot@acfr.usyd.edu.au