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.

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.

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.

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.