Curvature-Continuous Trajectory Refinement for Autonomous Vehicles Using a Pure Pursuit Based Path Smoothing Framework

Path planning is a key element of autonomous vehicle navigation, allowing vehicles to calculate feasible paths in challenging environments for applications like automated parking and low speed autonomous driving. Algorithms such as Hybrid A*, Reeds-Shepp, and Dubins paths are widely used and can generate collision-free paths but tend to create curvature discontinuities. These discontinuities result in sudden steering transitions, which create control instabilities, higher mechanical stress, and lower passenger comfort. To overcome these issues, this paper suggests a path-smoothing technique based on the pure-pursuit algorithm to produce smoothed curve paths appropriate for real-world driving. This method utilizes the practical approach of the original path, but removes sudden transitions that destabilize control. By ensuring smooth curvature, the vehicle undergoes fewer jerky steering actions, improved energy efficiency, less actuator wear, and improved high-speed tracking. This paper provides a valuable approach to usual limitations of discrete path planning, on the contribution of control algorithms such as pure pursuit to bridging the gap between planning and execution towards more adaptable autonomous driving particularly automated parking systems.