Real-time Obstacle Avoidance Control and Path Planning with Verification for Autonomous Vehicles Using Sensor Measurements

In recent years, autonomous technology has advanced rapidly, with the real-time obstacle avoidance capability of autonomous vehicles on the road being a primary concern for ensuring safety.Therefore, we propose an innovative path planning and control methodology to enhance the real-time obstacle avoidance performance of autonomous vehicles.In terms of path planning, we propose a global path planning approach based on an optimized A* algorithm, incorporating a dynamic weighting method and cubic-spline curve smoothing.This improves path search efficiency by 20% and enhances path smoothness by 3%.Additionally, we introduce a local path planning method based on fifth-degree polynomial interpolation curves.Regarding the control approach, we design a position-velocity double-layer proportional-integral-derivative controller for longitudinal control and a feedforward linear quadratic regulator controller for lateral control.Finally, we validate the proposed methodology through control system design, driving path simulation analysis, and real-world vehicle tests using sensor measurements.The application of this innovative control method in a campus environment demonstrates a clear improvement in the real-time driving capability of autonomous vehicles, increasing the obstacle avoidance success rate by 5%.Furthermore, it enables autonomous vehicles to achieve precise control and navigation on the road, reducing the vehicle's lateral control error and shortening the longitudinal control response time by 0.1 s.These findings provide a valuable technical reference for path planning and control in autonomous vehicle systems.