INT2: Interactive Trajectory Prediction at Intersections
Zhijie Yan, Pengfei Li, Zheng Fu, Shaocong Xu, Yongliang Shi, Xiaoxue Chen, Yuhang Zheng, Yang Li
Abstract
Motion forecasting is an important component in autonomous driving systems. 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. To this end, we present a large-scale interactive trajectory prediction dataset named INT2 for INTeractive trajectory prediction at INTersections. INT2 includes 612,000 scenes, each lasting 1 minute, containing up to 10,200 hours of data. The agent trajectories are auto-labeled by a high-performance offline temporal detection and fusion algorithm, whose quality is further inspected by human judges. Vectorized semantic maps and traffic light information are also included in INT2. Additionally, the dataset poses an interesting domain mismatch challenge. For each intersection, we treat rush-hour and non-rush-hour segments as different domains. We benchmark the best open-sourced interactive trajectory prediction method on INT2 and Waymo Open Motion, under in-domain and cross-domain settings. The dataset, code and models are publicly available at https://github.com/AIR-DISCOVER/INT2.
BibTeX
@inproceedings{iccv2023_int2interactivet,
title = {INT2: Interactive Trajectory Prediction at Intersections},
author = {Zhijie Yan and Pengfei Li and Zheng Fu and Shaocong Xu and Yongliang Shi and Xiaoxue Chen and Yuhang Zheng and Yang Li and Tianyu Liu and Chuxuan Li and Nairui Luo and Xu Gao and Yilun Chen and Zuoxu Wang and Yifeng Shi and Pengfei Huang and Zhengxiao Han and Jirui Yuan and Jiangtao Gong and Guyue Zhou and Hang Zhao and Hao Zhao},
booktitle = {ICCV 2023},
year = {2023}
}