We present a new interaction mechanism of prediction and planning for end-to-end autonomous driving, called PPAD (Iterative Interaction of Prediction and Planning Autonomous Driving), which considers the timestep-wise interaction to better integrate prediction and planning. An ego vehicle performs motion planning at each timestep based on the trajectory prediction of surrounding agents (e.g., vehicles and pedestrians) and its local road conditions. Unlike existing end-to-end autonomous driving frameworks, PPAD models the interactions among ego, agents, and the dynamic environment in an autoregressive manner by interleaving the Prediction and Planning processes at every timestep, instead of a single sequential process of prediction followed by planning. Specifically, we design ego-to-agent, ego-to-map, and ego-to-BEV interaction mechanisms with hierarchical dynamic key objects attention to better model the interactions. The experiments on the nuScenes benchmark show that our approach outperforms state-of-the-art methods.

翻译：本文提出了一种用于端到端自动驾驶的预测与规划新型交互机制，称为PPAD（预测与规划自动驾驶迭代交互），该机制通过考虑时间步层面的交互以更好地整合预测与规划。自车基于对周围智能体（如车辆与行人）的轨迹预测及其局部道路条件，在每个时间步执行运动规划。与现有端到端自动驾驶框架不同，PPAD并非采用先预测后规划的单一顺序流程，而是通过在每个时间步交错进行预测与规划过程，以自回归方式建模自车、智能体与动态环境之间的交互。具体而言，我们设计了自车-智能体、自车-地图及自车-鸟瞰图（BEV）的交互机制，并采用分层动态关键对象注意力以更好地建模交互关系。在nuScenes基准测试上的实验表明，本方法性能优于现有最优方法。