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Real-time kinodynamic trajectory planning in dynamic environments is criticalyet challenging for autonomous driving. In this letter, we propose an efficienttrajectory planning system for autonomous driving in complex dynamic scenariosthrough iterative and incremental path-speed optimization. Exploiting thedecoupled structure of the planning problem, a path planner based on Gaussianprocess first generates a continuous arc-length parameterized path in theFren\'{e}t frame, considering static obstacle avoidance and curvatureconstraints. We theoretically prove that it is a good generalization of thewell-known jerk optimal solution. An efficient s-t graph search method isintroduced to find a speed profile along the generated path to deal withdynamic environments. Finally, the path and speed are optimized incrementallyand iteratively to ensure kinodynamic feasibility. Various simulated scenarioswith both static obstacles and dynamic agents verify the effectiveness androbustness of our proposed method. Experimental results show that our methodcan run at 20 Hz. The source code is released as an open-source package.

Real-Time Trajectory Planning for Autonomous Driving with Gaussian Process and Incremental Refinement