V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting

1 code implementation • CVPR 2023 • Haibao Yu, Wenxian Yang, Hongzhi Ruan, Zhenwei Yang, Yingjuan Tang, Xu Gao, Xin Hao, Yifeng Shi, Yifeng Pan, Ning Sun, Juan Song, Jirui Yuan, Ping Luo, Zaiqing Nie

Utilizing infrastructure and vehicle-side information to track and forecast the behaviors of surrounding traffic participants can significantly improve decision-making and safety in autonomous driving.

Autonomous Driving Decision Making +1

INT2: Interactive Trajectory Prediction at Intersections

1 code implementation • ICCV 2023 • Zhijie Yan, Pengfei Li, Zheng Fu, Shaocong Xu, Yongliang Shi, Xiaoxue Chen, Yuhang Zheng, Yang Li, Tianyu Liu, Chuxuan Li, Nairui Luo, Xu Gao, Yilun Chen, Zuoxu Wang, Yifeng Shi, Pengfei Huang, Zhengxiao Han, Jirui Yuan, Jiangtao Gong, Guyue Zhou, Hang Zhao, Hao Zhao

One of the most challenging problems in motion forecasting is interactive trajectory prediction, whose goal is to jointly forecasts the future trajectories of interacting agents.

Motion Forecasting Trajectory Prediction

A High Fidelity Simulation Framework for Potential Safety Benefits Estimation of Cooperative Pedestrian Perception

no code implementations • 17 Oct 2022 • Longrui Chen, Yan Zhang, Wenjie Jiang, Jiangtao Gong, Jiahao Shen, Mengdi Chu, Chuxuan Li, Yifeng Pan, Yifeng Shi, Nairui Luo, Xu Gao, Jirui Yuan, Guyue Zhou, Yaqin Zhang

This paper proposes a high-fidelity simulation framework that can estimate the potential safety benefits of vehicle-to-infrastructure (V2I) pedestrian safety strategies.