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：我们提出了一种用于端到端自动驾驶的新型预测与规划交互机制，称为PPAD（预测与规划交替迭代驱动的自动驾驶），该方法通过考虑时间步层面的交互来更好地融合预测与规划。自车根据周围智能体（如车辆和行人）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通过在每个时间步交替执行预测与规划过程（而非先预测后规划的单次顺序流程），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，我们设计了自车到智能体、自车到地图、自车到BEV三种交互机制，并引入层次化动态关键目标注意力以更精细地建模交互。在nuScenes基准上的实验表明，我们的方法优于现有最先进方法。