Efficient local trajectory generation for self-driving cars: combining waypoints, spline curves, and predictive control

Abstract This paper introduces a robust and efficient Localized Spline-based Path-Planning (LSPP) algorithm designed to enhance autonomous vehicle navigation on highways. The LSPP approach prioritizes smooth maneuvering, obstacle avoidance, passenger comfort, and adherence to road constraints, including lane boundaries, through an optimized trajectory generation using quintic spline functions and a speed profile. Leveraging real-time data from the vehicle’s sensor fusion module, the LSPP algorithm accurately interprets the positions of surrounding vehicles and obstacles, creating a safe, dynamically feasible path that is relayed to the Model Predictive Control (MPC) track-following module for precise execution. Extensive simulations in diverse highway scenarios and traffic conditions demonstrate LSPP’s effectiveness in delivering smooth, kinematically feasible trajectories, with results showcasing improved lane-keeping, obstacle avoidance, and computational efficiency over traditional path-planning methods. The findings confirm LSPP’s promise as a powerful solution for safe, comfortable, and efficient autonomous highway driving.