Adaptive Maneuver Planning for Autonomous Vehicles Using Behavior Tree on Apollo Platform

In safety-critical systems such as autonomous driving systems, behavior planning is a significant challenge. The presence of numerous dynamic obstacles makes the driving environment unpredictable. The planning algorithm should be safe, reactive, and adaptable to environmental changes. The paper presents an adaptive maneuver planning algorithm based on an evolving behavior tree created with genetic programming. In addition, we make a technical contribution to the Baidu Apollo autonomous driving platform, allowing the platform to test and develop overtaking maneuver planning algorithms.