PPAD：端到端自动驾驶中预测与规划的迭代交互
PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving
AI总结：
提出PPAD框架，通过时间步级交错预测与规划并设计多交互机制，在nuScenes上超越现有方法。
摘要：
我们提出了一种用于端到端自动驾驶的预测与规划的新型交互机制，称为PPAD（预测与规划自动驾驶的迭代交互），该机制考虑了时间步级别的交互，以更好地整合预测与规划。自车在每个时间步基于周围代理（如车辆和行人）的轨迹预测及其局部道路状况执行运动规划。与现有的端到端自动驾驶框架不同，PPAD通过在每个时间步交错进行预测和规划过程，以自回归方式建模自车、代理和动态环境之间的交互，而不是采用先预测后规划的单次顺序过程。具体而言，我们设计了自车到代理、自车到地图以及自车到BEV的交互机制，并采用分层动态关键对象注意力来更好地建模这些交互。在nuScenes基准上的实验表明，我们的方法优于最先进的方法。
英文摘要：
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.
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