A path planning algorithm based on multifactor cost pruning optimization
Wangwang Li, Dejun Yin · 2025
Addressing the obstacle avoidance path planning problem for intelligent vehicles, this paper proposes a dynamic path planning method based on multi-factor cost pruning optimization. This method enhances planning efficiency while ensuring safety by pruning decisions using a multi-factor cost function combined with dynamic programming principles. Specifically, it selects key obstacles based on the dynamic window division according to vehicle speed and integrates factors such as obstacle cost and road boundary cost to calculate decision priority. Additionally, an evaluation function is designed to assess the environment's complexity by considering the proportion of obstacles in the dynamic window relative to the window area, distribution, dynamic characteristics, road conditions, and other factors. The pruning proportion is dynamically adjusted based on environmental complexity to maintain safety and maximize planning efficiency. Experimental results indicate that this method can promptly respond to environmental changes, optimize planning efficiency, ensure path safety and rationality, and demonstrate high practical applicability and effectiveness. Through these innovative methods and experimental results, this paper provides significant technical support and theoretical guidance for the field of intelligent vehicle path planning, highlighting its broad application prospects and scientific research value.