PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving
This addresses the challenge of safe and efficient autonomous driving for vehicles, though it appears incremental as it builds on existing frameworks with a novel interaction approach.
The paper tackles the problem of integrating prediction and planning in end-to-end autonomous driving by proposing PPAD, an iterative interaction mechanism that outperforms state-of-the-art methods on the nuScenes benchmark.
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.