Li Chen, Penghao Wu, Kashyap Chitta, Bernhard Jaeger, Andreas Geiger, Hongyang Li
TPAMI 2024
In this survey, we provide a comprehensive analysis of more than 270 papers on the motivation, roadmap, methodology, challenges, and future trends in end-to-end autonomous driving.
Qingwen Bu, Jia Zeng, Li Chen, Yanchao Yang, Guyue Zhou, Junchi Yan, Ping Luo, Heming Cui, Yi Ma, Hongyang Li
NeurIPS 2024
CLOVER employs a text-conditioned video diffusion model for generating visual plans as reference inputs, then these sub-goals guide the feedback-driven policy to generate actions with an error measurement strategy.
Daniel Dauner, Marcel Hallgarten, Tianyu Li, Xinshuo Weng, Zhiyu Huang, Zetong Yang, Hongyang Li, Igor Gilitschenski, Boris Ivanovic, Marco Pavone, Andreas Geiger, Kashyap Chitta
NeurIPS 2024 Track Datasets and Benchmarks
Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking.
Jiazhi Yang, Shenyuan Gao, Yihang Qiu, Li Chen, Tianyu Li, Bo Dai, Kashyap Chitta, Penghao Wu, Jia Zeng, Ping Luo, Jun Zhang, Andreas Geiger, Yu Qiao, Hongyang Li
CVPR 2024 (Highlight)
We aim to establish a generalized video prediction paradigm for autonomous driving by presenting the largest multimodal driving video dataset to date, OpenDV-2K, and a generative model that predicts the future given past visual and textual input, GenAD.
A new self-supervised pre-training task for end-to-end autonomous driving, predicting future point clouds from historical visual inputs, joint modeling the 3D geometry and temporal dynamics for simultaneous perception, prediction, and planning.
Yulu Gao, Chonghao Sima, Shaoshuai Shi, Shangzhe Di, Si Liu, Hongyang Li
IROS 2023
We propose Sparse Dense Fusion (SDF), a complementary framework that incorporates both sparse-fusion and dense-fusion modules via the Transformer architecture.
Tianyu Li, Li Chen, Huijie Wang, Yang Li, Jiazhi Yang, Xiangwei Geng, Shengyin Jiang, Yuting Wang, Hang Xu, Chunjing Xu, Junchi Yan, Ping Luo, Hongyang Li
arXiv 2023
A new baseline for scene topology reasoning, which unifies heterogeneous feature learning and enhances feature interactions via the graph neural network architecture and the knowledge graph design.
Li Chen, Chonghao Sima, Yang Li, Zehan Zheng, Jiajie Xu, Xiangwei Geng, Hongyang Li, Conghui He, Jianping Shi, Yu Qiao, Junchi Yan
ECCV 2022 (Oral) [Redefine the Community]
PersFormer adopts a unified 2D/3D anchor design and an auxiliary task to detect 2D/3D lanes; we release one of the first large-scale real-world 3D lane datasets, OpenLane.