LAUSR

Efficient trajectory planning for intelligent vehicles in dynamic environments is a non-trivial task due to the diversity and complexity of driving scenarios. It requires the planner to be capable of responding to the changes in driving scenarios in real-time. This paper proposes a hybrid trajectory planning framework by combining the sampling- and numerical optimization-based approaches to cope with the complex driving scenarios. First, a risk field model is introduced to assess the risks with the static and moving obstacles. Then, the sampling-based approach is used to generate collision-free trajectory candidates via the Path Velocity Decomposition method. Thus, the optimal behavior trajectory can be obtained by considering curve smoothness, collision risk, and travel time. The optimization-based method is adopted to optimize the behavior trajectory to guarantee safety, vehicle dynamics stability, and driving comfort using the Sequential Quadratic Programming within the spatio-temporal boundaries. Finally, the proposed framework is examined in typical dynamic driving scenarios through simulation, and the results verify its competency in generating high-quality trajectories in real-time.