Risk Occupancy: A New and Efficient Paradigm through Vehicle-Road-Cloud Collaboration
<|reference_start|>Risk Occupancy: A New and Efficient Paradigm through Vehicle-Road-Cloud Collaboration: This study introduces the 4D Risk Occupancy within a vehicle-road-cloud architecture, integrating the road surface spatial, risk, and temporal dimensions, and endowing the algorithm with beyond-line-of-sight, all-angles, and efficient abilities. The algorithm simplifies risk modeling by focusing on directly observable information and key factors, drawing on the concept of Occupancy Grid Maps (OGM), and incorporating temporal prediction to effectively map current and future risk occupancy.   Compared to conventional driving risk fields and grid occupancy maps, this algorithm can map global risks more efficiently, simply, and reliably. It can integrate future risk information, adapting to dynamic traffic environments. The 4D Risk Occupancy also unifies the expression of BEV detection and lane line detection results, enhancing the intuitiveness and unity of environmental perception. Using DAIR-V2X data, this paper validates the 4D Risk Occupancy algorithm and develops a local path planning model based on it. Qualitative experiments under various road conditions demonstrate the practicality and robustness of this local path planning model. Quantitative analysis shows that the path planning based on risk occupation significantly improves trajectory planning performance, increasing safety redundancy by 12.5% and reducing average deceleration by 5.41% at an initial braking speed of 8 m/s, thereby improving safety and comfort. This work provides a new global perception method and local path planning method through Vehicle-Road-Cloud architecture, offering a new perceptual paradigm for achieving safer and more efficient autonomous driving.<|reference_end|>
@article{chen2024risk,
    title={Risk Occupancy: A New and Efficient Paradigm through Vehicle-Road-Cloud
  Collaboration},
    author={Jiaxing Chen, Wei Zhong, Bolin Gao, Yifei Liu, Hengduo Zou, Jiaxi Liu,
  Yanbo Lu, Jin Huang, Zhihua Zhong},
    journal={arXiv preprint arXiv:2408.07367},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07367},
    primaryClass={cs.RO}
}