Robust Online Path Planning for Autonomous Vehicle Using Sequential Quadratic Programming

In urban driving scenarios, it is a key component for autonomous vehicles to generate a smooth, kinodynamically feasible, and collision-free path. We present an optimization-based path planning method for autonomous vehicles navigating in cluttered environment, e.g., roads partially blocked by static or moving obstacles. Our method first computes a collision-free reference line using quadratic programming(QP), and then using the reference line as initial guess to generate a smooth and feasible path by iterative optimization using sequential quadratic programming(SQP). It works within a fractions of a second, thus permitting efficient regeneration.