TY - GEN
T1 - Edge-Assisted Low-Latency Object Detection for Networked Vehicles Using Point Cloud
AU - Qin, Tian
AU - Hou, Jiawei
AU - Yang, Peng
AU - Cao, Xiaofeng
AU - Wu, Ye
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - In this paper, with the objective of enhancing the perception quality for autonomous vehicles based on point cloud data, we design an edge-Assisted on-road perception framework. This framework contains three modules: Adaptive point cloud transmission, driving environment complexity evaluation, and perception task scheduling. In specific, to suppress the transmission delay posed by fluctuating wireless links, we finely configure the quality of point cloud data to be transmitted with sampling and compression fidelity tuning. Then, taking the driving environment complexity into consideration, we propose perception score, a novel perception quality evaluation metric, according to which the edge server schedules the received tasks. Furthermore, an overall perception score maximization problem is formulated to obtain the optimal scheduling strategy at the edge. At last, a greedy-based time window constraint algorithm is designed, which can solve the problem at low computational complexity. Extensive experiments show that, compared to other benchmarks, our algorithm exhibits significant advantages in precise and low-latency object detection for autonomous vehicles.
AB - In this paper, with the objective of enhancing the perception quality for autonomous vehicles based on point cloud data, we design an edge-Assisted on-road perception framework. This framework contains three modules: Adaptive point cloud transmission, driving environment complexity evaluation, and perception task scheduling. In specific, to suppress the transmission delay posed by fluctuating wireless links, we finely configure the quality of point cloud data to be transmitted with sampling and compression fidelity tuning. Then, taking the driving environment complexity into consideration, we propose perception score, a novel perception quality evaluation metric, according to which the edge server schedules the received tasks. Furthermore, an overall perception score maximization problem is formulated to obtain the optimal scheduling strategy at the edge. At last, a greedy-based time window constraint algorithm is designed, which can solve the problem at low computational complexity. Extensive experiments show that, compared to other benchmarks, our algorithm exhibits significant advantages in precise and low-latency object detection for autonomous vehicles.
UR - http://www.scopus.com/inward/record.url?scp=85201938929&partnerID=8YFLogxK
U2 - 10.1109/ICCNC63989.2024.00017
DO - 10.1109/ICCNC63989.2024.00017
M3 - Conference Proceeding
AN - SCOPUS:85201938929
T3 - Proceedings - 2024 International Conference on Cloud and Network Computing, ICCNC 2024
SP - 49
EP - 56
BT - Proceedings - 2024 International Conference on Cloud and Network Computing, ICCNC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 International Conference on Cloud and Network Computing, ICCNC 2024
Y2 - 31 May 2024 through 2 June 2024
ER -