Sensor fusion is critical to perception systems for task domains such as autonomous driving and robotics. Recently, the Transformer integrated with CNN has demonstrated high performance in sensor fusion for various perception tasks. In this work, we introduce a method for fusing data from camera and LiDAR. By employing Transformer modules at multiple resolutions, proposed method effectively combines local and global contextual relationships. The performance of the proposed method is validated by extensive experiments with two adversarial benchmarks with lengthy routes and high-density traffics. The proposed method outperforms previous approaches with the most challenging benchmarks, achieving significantly higher driving and infraction scores. Compared with TransFuser, it achieves 8% and 19% improvement in driving scores for the Longest6 and Town05 Long benchmarks, respectively.
@article{arxiv.2308.10707,
title = {Sensor Fusion by Spatial Encoding for Autonomous Driving},
author = {Quoc-Vinh Lai-Dang and Jihui Lee and Bumgeun Park and Dongsoo Har},
journal= {arXiv preprint arXiv:2308.10707},
year = {2023}
}
Comments
This paper has been accepted for Lecture presentation at the 2023 IEEE SENSORS conference