FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving

This repository contains resources related to the paper "FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving".

Overview

FusionAD is an approach to building a unified network for leveraging multi-sensory data in end-to-end manners. It offers enhanced performance on perception tasks, prediction, and planning for autonomous driving. Notably, FusionAD integrates critical information from both camera and LiDAR sensors and uses this data beyond just perception tasks.

Key Contributions

• Proposes a BEV-fusion based, multi-sensory, multi-task, end-to-end learning approach for the key tasks in autonomous driving. The fusion-based method significantly improves the results compared to the camera-based BEV method.
• Introduces the FMSPnP module, which incorporates a refinement net and mode attention for the prediction task. Additionally, it integrates relaxed collision loss and fusion with vectorized ego information for the planning task.
• Conducts comprehensive studies across multiple tasks to validate the effectiveness of the proposed method. The experimental results show that FusionAD achieves state-of-the-art results in prediction and planning tasks while maintaining competitive results in intermediate perception tasks.

Paper

For more details, please refer to the full paper.

Getting Started

Train/Eval

Main Results

∗ denotes evaluation using checkpoints from official implementation.

Method	Detection (mAP, NDS)	Tracking (AMOTA, AMOTP)	Mapping (IoU-Lane, IoU-D)	Prediction (ADE, FDE, MR, EPA)	Occupancy (VPQ-n, VPQ-f, IoU-n, IoU-f)	Planning (DE, CR(avg), CR(traj)
UniAD	0.382*, 0.499*	0.359, 1.320	0.313, 0.691	0.708, 1.025, 0.151, 0.456	54.7, 33.5, 63.4, 40.2	1.03, 0.31, 1.46*
FusionAD	0.574, 0.646	0.501, 1.065	0.367, 0.731	0.389, 0.615, 0.084, 0.620	64.7, 50.2, 70.4, 51.0	0.81, 0.12, 0.37

Motion Forecasting Results

Method	minADE	minFDE	MR	EPA
PnPNet	1.15	1.95	0.226	0.222
VIP3D	2.05	2.84	0.246	0.226
UniAD	0.71	1.02	0.151	0.456
FusionAD	0.388	0.617	0.086	0.626

Occupancy Prediction Results

Method	IoU-n	IoU-f	VPQ-n	VPQ-f
FIERY	59.4	36.7	50.2	29.9
StretchBEV	55.5	37.1	46.0	29.0
ST-P3	-	38.9	-	32.1
BEVerse	61.4	40.9	54.3	36.1
PowerBEV	62.5	39.3	55.5	33.8
UniAD	63.4	40.2	54.7	33.5
FusionAD	71.2	51.5	65.5	51.1

Planning Results

ID	DE_avg	CR_1s	CR_2s	CR_3s	CR_avg	CR_traj
FF	1.43	0.06	0.17	1.07	0.43	-
EO	1.60	0.04	0.09	0.88	0.33	-
ST-P3	2.11	0.23	0.62	1.27	0.71	-
UniAD	1.03	0.05	0.17	0.71	0.31	1.46
VAD	0.37	0.07	0.10	0.24	0.14	-
FusionAD	0.81	0.02	0.08	0.27	0.12	0.37

Cases Comparison with the UniAD

Case 1

cam_distortion.webm

Perception of a bus. FusionAD detects the heading correctly while distorsion exists in near range, but UniAD incorrectly predicts the heading.

Case 2

uturn.webm

Prediction of U-turn. FusionAD consistantly predicts the U-turn earlier in all modes which aligns with the ground-truth trace, while UniAD still predicts the move-foward, left-turn and U-turn modes until the very last second U-turn actually happens.

Citation

If you find our work useful in your research, please consider citing:

@article{yetengju2023fusionad,
  title={FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving},
  author={Ye, Tengju* and Jing, Wei* and Hu, Chunyong and Huang, Shikun and Gao, Lingping and Li, Fangzhen and Wang, Jingke and Guo, Ke and Xiao, Wencong and Mao, Weibo and Zheng, Hang and Li, Kun and Chen, Junbo and Yu, Kaicheng},
  note={*Equal Contribution},
  year={2023}
}

Acknowledgements

We acknowledge the authors of UniAD repository for their valuable contribution.

Contact

For any questions or suggestions, feel free to reach out to us:

• yetengju@gmail.com
• 21wjing@gmail.com
• kyu@westlake.edu.cn