Prediction of vehicle trajectories with map data for cooperative systems

Cooperative systems are being investigated to increase road safety. Here, vehicles and road infrastructure will establish a wireless network to communicate safety-related information. A vehicle will obtain a perception of the environment extended in time and space, when compared with the perception achieved by the driver or by the current active safety systems. Applications, such as support to lane keeping and frontal collision warning, will be able to provide an earlier anticipation of hazards. For that, the predicted trajectories of vehicles should span longer time ahead than todate. This work aimed to develop an algorithm to predict trajectories of vehicles for cooperative systems. The specific objectives were the prediction up to 5s ahead of (1) vehicle trajectories in straight and curved roads of one lane, and (2) vehicle route in urban junctions. The algorithm should be suitable for any vehicle in a cooperative system. It was implemented an algorithm that fuses trajectories based on the vehicle motion and on the vehicle motion constrained to the road. An innovative model to predict trajectories of vehicles based on their motion was proposed. The model is the most accurate and among the fastest, when compared with models in the literature. In addition, a breakthrough algorithm was implemented to predict vehicle route in urban junctions. The algorithm achieves a high rate of correct predictions up to 5s ahead of the junction, while keeping the rate of false predictions near zero. Prediction of trajectories were analyzed in different driving cases. Finally, the algorithms implemented were demonstrated in a cooperative scenario. Data was acquired on real time with vehicles that were exchanging information during the drive. The algorithm runs offline, but no design change is needed for real time execution.Validerat; 20101217 (root