Before revision:  Current mainstream methods, such as functionality decomposition or end-to-end reinforcement learning (RL), either suffer high time complexity or poor interpretability and limited safety performance in real-world complex autonomous driving tasks.
After revision:  Current mainstream methods, such as functionality decomposition or end-to-end reinforcement learning (RL), either suffer high time complexity or poor interpretability and adaptability on real-world complex autonomous driving tasks.