Speed profile optimisation for intelligent vehicles in dynamic traffic scenarios

In the autonomous navigation of intelligent vehicles, collision avoidance is essential for driving safety. Similar to the driving preference of human, the driving path and speed can be determined separately. This paper is concerned with speed profile optimisation problem for dynamic obstacle avoidance given the reference path. The optimisation consists of smoothness, risk, and efficiency terms with obstacle constraints. For task formulation, the s-t motion space is constructed to describe the motion of the ego vehicle and obstacles. Then the high-dimensional trajectory space is mapped to the low-dimensional s-t space for computational efficiency. The speed optimisation problem is transformed into a path searching problem considering collision avoidance and searching efficiency. RRT-based algorithm is proposed to search for the optimal speed profile in the s-t space asymptotically. In each searching step, node extension strategy is designed for the space exploring efficiency; then the tree structure is locally refined for asymptotic optimisation. The optimal speed profile is generated after the searching process converges and the speed profile is planned periodically. For performance evaluation, simulation tests in typical traffic conditions are conducted based on the SUMO (Simulation of Urban MObility) platform. Results show the effectiveness and efficiency of this method.