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20182023
most citedRoad Traffic Law Adaptive Decision-making for Self-Driving Vehicles

27 citations · 66 across the 13 of their papers we have counts for

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Showing 2022Show all

6 papers · 1 filter

cs.AI2022★ 3 cited

Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions

Chulin Xie, Zhong Cao, Yunhui Long +3

Recent advances in machine learning have enabled its wide application in different domains, and one of the most exciting applications is autonomous vehicles (AVs), which have encou…

cs.RO2022★ 2 cited

Map Container: A Map-based Framework for Cooperative Perception

Kun Jiang, Yining Shi, Benny Wijaya +4

The idea of cooperative perception is to benefit from shared perception data between multiple vehicles and overcome the limitations of on-board sensors on single vehicle. However,…

cs.CV2022★ 2 cited

Bridging the View Disparity Between Radar and Camera Features for Multi-modal Fusion 3D Object Detection

Taohua Zhou, Yining Shi, Junjie Chen +3

Environmental perception with the multi-modal fusion of radar and camera is crucial in autonomous driving to increase accuracy, completeness, and robustness. This paper focuses on…

cs.AI2022

Long-Tail Prediction Uncertainty Aware Trajectory Planning for Self-driving Vehicles

Weitao Zhou, Zhong Cao, Yunkang Xu +4

A typical trajectory planner of autonomous driving commonly relies on predicting the future behavior of surrounding obstacles. Recently, deep learning technology has been widely ad…

cs.CV2022

SRCN3D: Sparse R-CNN 3D for Compact Convolutional Multi-View 3D Object Detection and Tracking

Yining Shi, Jingyan Shen, Yifan Sun +5

Detection and tracking of moving objects is an essential component in environmental perception for autonomous driving. In the flourishing field of multi-view 3D camera-based detect…

cs.RO2022★ 27 cited

Road Traffic Law Adaptive Decision-making for Self-Driving Vehicles

Jiaxin Liu, Wenhui Zhou, Hong Wang +6

Self-driving vehicles have their own intelligence to drive on open roads. However, vehicle managers, e.g., government or industrial companies, still need a way to tell these self-d…