7 papers
DriveE2E: Closed-Loop Benchmark for End-to-End Autonomous Driving through Real-to-Simulation
Haibao Yu, Wenxian Yang, Ruiyang Hao +4
Closed-loop evaluation is increasingly critical for end-to-end autonomous driving. Current closed-loop benchmarks using the CARLA simulator rely on manually configured traffic scen…
Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition
Ruiyang Hao, Haibao Yu, Jiaru Zhong +16
With the rapid advancement of autonomous driving technology, vehicle-to-everything (V2X) communication has emerged as a key enabler for extending perception range and enhancing dri…
LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation
Zhenwei Yang, Jilei Mao, Wenxian Yang +4
Temporal perception, defined as the capability to detect and track objects across temporal sequences, serves as a fundamental component in autonomous driving systems. While single-…
End-to-End Autonomous Driving through V2X Cooperation
Haibao Yu, Wenxian Yang, Jiaru Zhong +4
Cooperatively utilizing both ego-vehicle and infrastructure sensor data via V2X communication has emerged as a promising approach for advanced autonomous driving. However, current…
Learning Cooperative Trajectory Representations for Motion Forecasting
Hongzhi Ruan, Haibao Yu, Wenxian Yang +2
Motion forecasting is an essential task for autonomous driving, and utilizing information from infrastructure and other vehicles can enhance forecasting capabilities. Existing rese…
Leveraging Temporal Contexts to Enhance Vehicle-Infrastructure Cooperative Perception
Jiaru Zhong, Haibao Yu, Tianyi Zhu +4
Infrastructure sensors installed at elevated positions offer a broader perception range and encounter fewer occlusions. Integrating both infrastructure and ego-vehicle data through…