vehicle dynamic model–driver model system: platform to evaluate car and human responses using double lane change circuit

Vehicle Dynamic Model–Driver Model (VDM-DM) system is developed to address the need to have a comprehensive system that can evaluate the performance of the car and the capability of the driver based on the planned trajectory. This is possible when VDM-DM system integrates the vehicle dynamic response with the driver model. The driver model determines the steer input from the geometrical properties of the intended path and this steer angle becomes the input for the vehicle dynamic response analysis. Finally, from the position of the car, the steer angle can be calculated. The position of the car will be then compared with the intended path and a new steer input can be determined by the driver model. Two case studies were carried out to demonstrate the application of the VDM-DM in evaluating the performance of the car and the capability of the driver using Double Lane Change (DLC) circuit. Based on the case studies, VDM-DM can be used as the tool to evaluate the performance of cars and capability of the drivers. This demonstrates that VDM-DM is capable to simulate the behavior of different drivers and hence, VDM-DM system has the potential to bring related road safety issue to the desktop

Heuristic based evolution for the coordination of autonomous vehicles in the absence of speed lanes

The current state of the art in the planning and coordination of autonomous vehicles is based upon the presence of speed lanes. In a traffic scenario where there is a large diversity between vehicles the removal of speed lanes can generate a significantly higher traffic bandwidth. Vehicle navigation in such unorganized traffic is considered. An evolutionary based trajectory planning technique has the advantages of making driving efficient and safe, however it also has to surpass the hurdle of computational cost. In this paper, we propose a real time genetic algorithm with Bezier curves for trajectory planning. The main contribution is the integration of vehicle following and overtaking behaviour for general traffic as heuristics for the coordination between vehicles. The resultant coordination strategy is fast and near-optimal. As the vehicles move, uncertainties may arise which are constantly adapted to, and may even lead to either the cancellation of an overtaking procedure or the initiation of one. Higher level planning is performed by Dijkstra's algorithm which indicates the route to be followed by the vehicle in a road network. Re-planning is carried out when a road blockage or obstacle is detected. Experimental results confirm the success of the algorithm subject to optimal high and low-level planning, re-planning and overtaking