14 citations · 60 across the 27 of their papers we have counts for
16 papers · 1 filter
KineWorld: Action-Induced Transport Fields for Embodied World Modeling
Ziying Song, Yuchen Liu, Zhuoran Xu +6
Embodied world models predict the visual consequences of candidate actions before execution. However, existing action-conditioned world models often adopt uniformly weighted visual…
InfraOcc: An Infrastructure Occupancy Benchmark with Static-to-Dynamic Reasoning
Lei Yang, Xiaokai Bai, Boqi Li +8
Fixed-viewpoint infrastructure sensors repeatedly observe the same traffic space, making roadside 3D occupancy structurally different from ego-vehicle perception: a near-persistent…
KnockGS:interaction-Grounded Calibrationof Physical Gaussian Representations
Chenchen Ge, Hanwen Shen, Bowen Jing +6
Physics-integrated 3D Gaussian representations now allow reconstructed deformable objects to be simulated and rendered under explicit material models. Existing pipelines, however,…
SparseCoop: Cooperative Perception with Kinematic-Grounded Queries
Jiahao Wang, Zhongwei Jiang, Wenchao Sun +8
Cooperative perception is critical for autonomous driving, overcoming the inherent limitations of a single vehicle, such as occlusions and constrained fields-of-view. However, curr…
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…
CoopTrack: Exploring End-to-End Learning for Efficient Cooperative Sequential Perception
Jiaru Zhong, Jiahao Wang, Jiahui Xu +3
Cooperative perception aims to address the inherent limitations of single-vehicle autonomous driving systems through information exchange among multiple agents. Previous research h…
StyleDrive: Towards Driving-Style Aware Benchmarking of End-To-End Autonomous Driving
Ruiyang Hao, Bowen Jing, Haibao Yu +1
Personalization, while extensively studied in conventional autonomous driving pipelines, has been largely overlooked in the context of end-to-end autonomous driving (E2EAD), despit…
Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark
Jiahao Wang, Xiangyu Cao, Jiaru Zhong +7
While cooperative perception can overcome the limitations of single-vehicle systems, the practical implementation of vehicle-to-vehicle and vehicle-to-infrastructure systems is oft…
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-…
RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception
Ruiyang Hao, Siqi Fan, Yingru Dai +7
The value of roadside perception, which could extend the boundaries of autonomous driving and traffic management, has gradually become more prominent and acknowledged in recent yea…
Flow-Based Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detection
Haibao Yu, Yingjuan Tang, Enze Xie +3
Cooperatively utilizing both ego-vehicle and infrastructure sensor data can significantly enhance autonomous driving perception abilities. However, the uncertain temporal asynchron…
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…
QUEST: Query Stream for Practical Cooperative Perception
Siqi Fan, Haibao Yu, Wenxian Yang +2
Cooperative perception can effectively enhance individual perception performance by providing additional viewpoint and expanding the sensing field. Existing cooperation paradigms a…
V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting
Haibao Yu, Wenxian Yang, Hongzhi Ruan +11
Utilizing infrastructure and vehicle-side information to track and forecast the behaviors of surrounding traffic participants can significantly improve decision-making and safety i…
Vehicle-Infrastructure Cooperative 3D Object Detection via Feature Flow Prediction
Haibao Yu, Yingjuan Tang, Enze Xie +4
Cooperatively utilizing both ego-vehicle and infrastructure sensor data can significantly enhance autonomous driving perception abilities. However, temporal asynchrony and limited…
DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection
Haibao Yu, Yizhen Luo, Mao Shu +8
Autonomous driving faces great safety challenges for a lack of global perspective and the limitation of long-range perception capabilities. It has been widely agreed that vehicle-i…