Perception Helps Planning: Facilitating Multi-Stage Lane-Level Integration via Double-Edge Structures
<|reference_start|>Perception Helps Planning: Facilitating Multi-Stage Lane-Level Integration via Double-Edge Structures: When planning for autonomous driving, it is crucial to consider essential traffic elements such as lanes, intersections, traffic regulations, and dynamic agents. However, they are often overlooked by the traditional end-to-end planning methods, likely leading to inefficiencies and non-compliance with traffic regulations. In this work, we endeavor to integrate the perception of these elements into the planning task. To this end, we propose Perception Helps Planning (PHP), a novel framework that reconciles lane-level planning with perception. This integration ensures that planning is inherently aligned with traffic constraints, thus facilitating safe and efficient driving. Specifically, PHP focuses on both edges of a lane for planning and perception purposes, taking into consideration the 3D positions of both lane edges and attributes for lane intersections, lane directions, lane occupancy, and planning. In the algorithmic design, the process begins with the transformer encoding multi-camera images to extract the above features and predicting lane-level perception results. Next, the hierarchical feature early fusion module refines the features for predicting planning attributes. Finally, the double-edge interpreter utilizes a late-fusion process specifically designed to integrate lane-level perception and planning information, culminating in the generation of vehicle control signals. Experiments on three Carla benchmarks show significant improvements in driving score of 27.20%, 33.47%, and 15.54% over existing algorithms, respectively, achieving the state-of-the-art performance, with the system operating up to 22.57 FPS.<|reference_end|>
@article{you2024perception,
    title={Perception Helps Planning: Facilitating Multi-Stage Lane-Level
  Integration via Double-Edge Structures},
    author={Guoliang You, Xiaomeng Chu, Yifan Duan, Wenyu Zhang, Xingchen Li, Sha
  Zhang, Yao Li, Jianmin Ji, Yanyong Zhang},
    journal={arXiv preprint arXiv:2407.11644},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11644},
    primaryClass={cs.CV cs.RO}
}