TY - GEN
T1 - VIPS
T2 - 28th ACM Annual International Conference on Mobile Computing and Networking, MobiCom 2022
AU - Shi, Shuyao
AU - Cui, Jiahe
AU - Jiang, Zhehao
AU - Yan, Zhenyu
AU - Xing, Guoliang
AU - Niu, Jianwei
AU - Ouyang, Zhenchao
N1 - Publisher Copyright:
© 2022 Association for Computing Machinery. All rights reserved.
PY - 2022/10/14
Y1 - 2022/10/14
N2 - Infrastructure-Assisted autonomous driving is an emerging paradigm that expects to significantly improve the driving safety of autonomous vehicles. The key enabling technology for this vision is to fuse LiDAR results from the roadside infrastructure and the vehicle to improve the vehicle's perception in real time. In this work, we propose VIPS, a novel lightweight system that can achieve decimeter-level and real-Time (up to 100 ms) perception fusion between driving vehicles and roadside infrastructure. The key idea of VIPS is to exploit highly efficient matching of graph structures that encode objects' lean representations as well as their relationships, such as locations, semantics, sizes, and spatial distribution. Moreover, by leveraging the tracked motion trajectories, VIPS can maintain the spatial and temporal consistency of the scene, which effectively mitigates the impact of asynchronous data frames and unpredictable communication/compute delays. We implement VIPS end-To-end based on a campus smart lamppost testbed. To evaluate the performance of VIPS under diverse situations, we also collect two new multi-view point cloud datasets using the smart lamppost testbed and an autonomous driving simulator, respectively. Experiment results show that VIPS can extend the vehicle's perception range by 140% within 58 ms on average, and delivers a 4X improvement in perception fusion accuracy and 47X data transmission saving over existing approaches.
AB - Infrastructure-Assisted autonomous driving is an emerging paradigm that expects to significantly improve the driving safety of autonomous vehicles. The key enabling technology for this vision is to fuse LiDAR results from the roadside infrastructure and the vehicle to improve the vehicle's perception in real time. In this work, we propose VIPS, a novel lightweight system that can achieve decimeter-level and real-Time (up to 100 ms) perception fusion between driving vehicles and roadside infrastructure. The key idea of VIPS is to exploit highly efficient matching of graph structures that encode objects' lean representations as well as their relationships, such as locations, semantics, sizes, and spatial distribution. Moreover, by leveraging the tracked motion trajectories, VIPS can maintain the spatial and temporal consistency of the scene, which effectively mitigates the impact of asynchronous data frames and unpredictable communication/compute delays. We implement VIPS end-To-end based on a campus smart lamppost testbed. To evaluate the performance of VIPS under diverse situations, we also collect two new multi-view point cloud datasets using the smart lamppost testbed and an autonomous driving simulator, respectively. Experiment results show that VIPS can extend the vehicle's perception range by 140% within 58 ms on average, and delivers a 4X improvement in perception fusion accuracy and 47X data transmission saving over existing approaches.
KW - infrastructure-Assisted autonomous driving
KW - perception fusion
KW - vehicle mobility
KW - vehicle-infrastructure information fusion
UR - https://www.scopus.com/pages/publications/85140926103
U2 - 10.1145/3495243.3560539
DO - 10.1145/3495243.3560539
M3 - 会议稿件
AN - SCOPUS:85140926103
T3 - Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM
SP - 133
EP - 146
BT - ACM MobiCom 2022 - Proceedings of the 2022 28th Annual International Conference on Mobile Computing and Networking
PB - Association for Computing Machinery
Y2 - 17 October 2202 through 21 October 2202
ER -