Path Planning and Tracking for Vehicle Collision Avoidance Based on Model Predictive Control With Multiconstraints

TLDR

The study proposes a path‑planning and tracking framework to keep autonomous vehicles collision‑free. The method constructs a 3‑D virtual danger field from road and obstacle functions to generate a trajectory, then employs a multiconstrained MPC controller to compute steering angles for collision avoidance, validated in Simulink/CarSim simulations. Simulations demonstrate the approach works across many scenarios, with the MMPC controller delivering dynamic tracking and good maneuverability.

Abstract

A path planning and tracking framework is presented to maintain a collision-free path for autonomous vehicles. For path-planning approaches, a 3-D virtual dangerous potential field is constructed as a superposition of trigonometric functions of the road and the exponential function of obstacles, which can generate a desired trajectory for collision avoidance when a vehicle collision with obstacles is likely to happen. Next, to track the planned trajectory for collision avoidance maneuvers, the path-tracking controller formulated the tracking task as a multiconstrained model predictive control (MMPC) problem and calculated the front steering angle to prevent the vehicle from colliding with a moving obstacle vehicle. Simulink and CarSim simulations are conducted in the case where moving obstacles exist. The simulation results show that the proposed path-planning approach is effective for many driving scenarios, and the MMPC-based path-tracking controller provides dynamic tracking performance and maintains good maneuverability.