Emergency Obstacle Avoidance Trajectory Planning Method of Intelligent Vehicles Based on Improved Hybrid A^∗

In this article, we present a spatiotemporal trajectory planning algorithm for emergency obstacle avoidance. Utilizing obstacle and driving environment data from the sensing module, we construct a 3D spatiotemporal grid map. This informs our improved hybrid A^∗ algorithm, which identifies collision-safe, dynamically feasible trajectories. The traditional hybrid A^∗ algorithm is enhanced in three significant ways to make the search practical and feasible: (1) optimizing search efficiency with motion primitives based on child node acceleration, (2) integrating collision risk into the heuristic function to reduce ineffective node exploration, and (3) introducing a One-Shot search based on the Optimal Boundary Value Problem (OBVP) to improve goal state searches. Finally, the algorithm is tested in two scenarios: (1) a vehicle cut-in from an adjacent lane and (2) a pedestrian crossing. Simulation results indicate that our proposed emergency obstacle avoidance trajectory planning method can efficiently devise trajectories that not only circumvent obstacles safely and adhere to vehicle dynamics constraints, but also meet the real-time demands of emergency obstacle avoidance trajectory planning.