# End-to-End Autonomous Driving through V2X Cooperation

Source: [End-to-End Autonomous Driving through V2X Cooperation](<https://www.sota2.com/research/paper/68fa765f1e209131cea6cb72>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:52:28.667Z

Authors: Haibao Yu, Wenxian Yang, Jiaru Zhong, Zhenwei Yang, Siqi Fan, Ping Luo, Zaiqing Nie

Published: 2024-03-31T15:22:11.000Z

## Abstract

Cooperatively utilizing both ego-vehicle and infrastructure sensor data via V2X communication has emerged as a promising approach for advanced autonomous driving. However, current research mainly focuses on improving individual modules, rather than taking end-to-end learning to optimize final planning performance, resulting in underutilized data potential. In this paper, we introduce UniV2X, a pioneering cooperative autonomous driving framework that seamlessly integrates all key driving modules across diverse views into a unified network. We propose a sparse-dense hybrid data transmission and fusion mechanism for effective vehicle-infrastructure cooperation, offering three advantages: 1) Effective for simultaneously enhancing agent perception, online mapping, and occupancy prediction, ultimately improving planning performance. 2) Transmission-friendly for practical and limited communication conditions. 3) Reliable data fusion with interpretability of this hybrid data. We implement UniV2X, as well as reproducing several benchmark methods, on the challenging DAIR-V2X, the real-world cooperative driving dataset. Experimental results demonstrate the effectiveness of UniV2X in significantly enhancing planning performance, as well as all intermediate output performance. The project is available at \\href{https://github.com/AIR-THU/UniV2X}{https://github.com/AIR-THU/UniV2X}.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Open-loop trajectory planning on DAIR-V2X-Seq (val)](<https://www.sota2.com/research/sota/open-loop-trajectory-planning-on-dair-v2x-seq-val.md>): Open-Loop Trajectory Planning; DAIR-V2X-Seq (val); L2 Error (1s): 1.45.

- [3D Object Detection on Griffin 25m](<https://www.sota2.com/research/sota/3d-object-detection-on-griffin-25m.md>): 3D Object Detection; Griffin 25m; AP (Overall, 0-100m): 16.

- [Multi-Object Tracking on Griffin 25m](<https://www.sota2.com/research/sota/multi-object-tracking-on-griffin-25m.md>): Multi-Object Tracking; Griffin 25m; AMOTA (0-100 m Overall): 15.

- [3D Object Detection on V2X-Seq](<https://www.sota2.com/research/sota/3d-object-detection-on-v2x-seq.md>): 3D Object Detection; V2X-Seq; AP (0-150m): 16.6.

- [3D Object Tracking on V2X-Seq](<https://www.sota2.com/research/sota/3d-object-tracking-on-v2x-seq.md>): 3D Object Tracking; V2X-Seq; AMOTA (0-150m Overall): 1.9.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2404.00717>)

[PDF](<https://arxiv.org/pdf/2404.00717.pdf>)