Research on the Multi-Armed Bandit Algorithm in Path Planning for Autonomous Vehicles

Department of Computer Science, Southwest University, 400715, Chongqing, China

^* Corresponding author: 222023321102092@email.swu.edu.cn

Abstract

In the technological revolution of the 21st century, autonomous driving technology is rapidly changing transportation modes, and path planning, as a key component, relies heavily on advanced algorithm optimization. The Multi-Arm Bandit (MAB) algorithm may become an efficient decision optimization tool in autonomous driving path planning. Because it can continuously experiment, learn, and quickly determine the optimal strategy to maximize profits under resource constraints. When applied to autonomous driving, the MAB algorithm may be able to demonstrate its advantages. In complex traffic environments, it dynamically adjusts strategies to adapt to constantly changing road conditions, plans safe and efficient driving paths, and quickly responds to unexpected situations to ensure driving safety. Compared with other algorithms, the learning and adaptability of MAB algorithm makes it particularly suitable for the dynamics and unpredictability of real-world driving scenarios. However, the practical application of MAB algorithm in autonomous driving faces challenges, including accurately evaluating path efficiency, efficiently processing large amounts of traffic data, and ensuring the stability and reliability of the algorithm. Further in-depth research and exploration are crucial for fully utilizing the advantages of MAB algorithm in path planning and promoting the sustainable development and enhancement of autonomous driving technology.