Enhanced RRT motion planning for autonomous vehicles: a review on safety testing applications

Abstract Autonomous vehicles (AVs) utilize powerful motion planning algorithms to navigate complex environments while ensuring safety and efficiency. Rapidly Exploring Random Trees (RRT) and its advanced variations have been extensively used for motion planning due to their ability to effectively traverse high-dimensional spaces. This study comprehensively analyzes improved RRT-based motion planning methods, highlighting their significance in AV safety testing and performance evaluations. We explore RRT enhancements, including RRT*, Informed RRT*, and Bidirectional RRT, and evaluate their effectiveness in addressing AV safety issues. Furthermore, we investigate simulation frameworks and real-world applications that validate these methods. The report concludes with future research directions aimed at enhancing the safety and reliability of AV motion planning.