In partnership with Ibeo, an urban vehicle was retrofitted with state of the art 360 degrees perception and communication technology. This vehicle perception and localisation capabilities were demonstrated at the ITS world congress in Melbourne (October 2016). This vehicle will collect a significant amount of naturalistic driving information and provide reference information for our fundamental research work in situational awareness, safety, machine learning and driverless vehicles. Large amounts of data are generated by the sensors and algorithms used in intelligent connected vehicles. In order to improve system performance and to facilitate work on more advanced algorithms these data must be collected and stored for later access. The manner in which this is done should facilitate meaningful future use of the data. We have made a number of our datasets from existing projects available for use by other researchers..

This term refers to the strategy of detecting nearby threats using sensors mounted to the ego vehicle such as cameras, lasers and radars. This form of threat detection requires sensing and the processing and interpretation of the sensor data to estimate the pose and dynamic properties of vehicles within sensor range. The ACFR has significant experience in the design and use of multimodal perception using all most type of standard sensors and development of new sensing in the area of Millimetre Wave Radar Technology. These developments have been applied to various industries such as defence, agriculture and mining among others.

High quality road maps are desirable for localisation and trajectory estimation. Many parameters can be encoded into the map, such as velocity profiles, acceleration profiles, expected behaviour and other similar information. This can be used in the threat estimation through time to collision and anomaly detection by examining the range of typical behaviour given the location. We have significant experience in the formulation and use of maps based on collected information with several contributions published in this area.

Threat assessment involves the estimation of the threat caused by vehicles and other road users in the local vicinity. We consider threat assessment in two categories: proximal safety indicators and detecting threats through behaviour analysis and the detection of anomalies.

Proximal safety indicators

Estimating the time to interaction between vehicles can be used as part of threat assessment. The potential motion of the vehicles is defined in an environmental model. The location of each vehicle on the map is determined based on the position and heading. The distance via the road network is then calculated based on the shortest path search over the map. This allows the estimation of time until vehicle interaction, and the more well known time to collision estimate based on velocity models for each road.

Behaviour analysis

Improvements in driver situation awareness can only occur if the outputs of intelligent connected vehicle systems can be conveyed to the driver. The provision of timely, accurate, useful and appropriate information is a key concern in the design and implementation of human machine interfaces. Our group has significant expertise with development and deployment of safety systems in industrial and urban environments.

The implementation of autonomy in urban environments presents significant challenges. As the quality of the situation awareness information becomes more reliable/robust, the next stage is to evaluate the capability of the technology to transfer control of the vehicle from the driver. To remove control of the vehicle will require a comprehensive understanding of the position and sensor uncertainty, and an estimation of the full state of the hardware (such as sensors, antennas, processing units, etc). The concept of integrity is currently investigated for vehicle automation with successful demonstration of these capabilities in international collaborative projects, such as our collaboration with Renault.