Hybrid Trajectory Planning for Autonomous Driving in On-Road Dynamic Scenarios

A safe trajectory planning for on-road autonomous driving is a challenging problem owing to the variety and complexity of driving environments. The problem should involve the consideration of numerous aspects such as road geometry, lane-structured roads, traffic regulations, traffic participants, and vehicle physical limitations. Furthermore, dynamically changing movements of surrounding vehicles make the problem more challenging. It requires the planner's ability to react to the changes in the driving environments in real time. To solve this problem, sampling and numerical optimization-based trajectory planners were introduced. However, these methods have their own limitations in generating a safe trajectory in these dynamic scenarios. To overcome these issues, this paper proposes a hybrid trajectory planning scheme to integrate the strength of the sampling and optimization methods. With the sampling method for a lateral movement, the planner can deal with various trajectories with multiple maneuvers. This helps the planner to generate a reactive trajectory in a dynamically changing environment. The numerical optimization of a longitudinal movement enables the planner to adapt to diverse situations without restriction of predefined patterns for specific driving purposes. The proposed method was implemented with an embedded optimization coder and C++ environment. Based on this, its performance was evaluated through simulation and real driving tests in various on-road dynamic scenarios.