A Structured Trajectory Planning Approach Using Rule- Based Waypoints and Spline Optimization for Autonomous Vehicles

Wael A. Farag; Muhammad  Nadeem

doi:10.6180/jase.202606_29(6).0012

A Structured Trajectory Planning Approach Using Rule- Based Waypoints and Spline Optimization for Autonomous Vehicles

Wael A. Farag¹This email address is being protected from spambots. You need JavaScript enabled to view it. and Muhammad Nadeem²

¹Electrical Power Eng. Dept., Cairo University, Giza, Egypt

²College of Engineering and Technology, American University of the Middle East, Kuwait

Received: March 3, 2025
Accepted: September 1, 2025
Publication Date: November 5, 2025

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202606_29(6).0012

This paper presents the Localized Spline-based Path-Planning (LSPP) algorithm, a robust and efficient frame work designed to improve trajectory generation for autonomous highway navigation. LSPP integrates intelligent rule-based waypoint selection, quintic spline optimization, and a model predictive control (MPC) track following mechanism to generate smooth, dynamically feasible, and safe local paths in real time. The algorithm leverages sensor fusion data to adapt to surrounding traffic, obstacles, and road boundaries while ensuring passenger comfort and adherence to kinematic constraints. Extensive simulations across varied highway scenarios show that LSPP achieves a 34% reduction in lateral deviation, a 27% improvement in obstacle clearance, and a 22% increase in computational efficiency compared to conventional A*, RRT, and clothoid-based planners. These quantitative gains highlight LSPP’s superiority in maintaining lane discipline, avoiding collisions, and enabling real-time responsiveness. The results affirm LSPP’s potential as a practical and scalable solution for safe and efficient autonomous driving in structured environments.

Keywords: MPCControl; Spline; Self-Driving Car; Autonomous Driving; Highway Navigation; Tracking; Path Planning

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