PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866913438676025344 |
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| author | Chen, Zhili Ye, Maosheng Xu, Shuangjie Cao, Tongyi Chen, Qifeng |
| author_facet | Chen, Zhili Ye, Maosheng Xu, Shuangjie Cao, Tongyi Chen, Qifeng |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2311_08100 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving Chen, Zhili Ye, Maosheng Xu, Shuangjie Cao, Tongyi Chen, Qifeng Computer Vision and Pattern Recognition Robotics 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. |
| title | PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2311.08100 |