Sensor Fusion by Spatial Encoding for Autonomous Driving
Quoc-Vinh Lai-Dang Jihui Lee Bumgeun Park Dongsoo Har
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.