  How should we integrate representations from complementary sensors for
autonomous driving? Geometry-based fusion has shown promise for perception
(e.g. object detection, motion forecasting). However, in the context of
end-to-end driving, we find that imitation learning based on existing sensor
fusion methods underperforms in complex driving scenarios with a high density
of dynamic agents. Therefore, we propose TransFuser, a mechanism to integrate
image and LiDAR representations using self-attention. Our approach uses
transformer modules at multiple resolutions to fuse perspective view and bird's
eye view feature maps. We experimentally validate its efficacy on a challenging
new benchmark with long routes and dense traffic, as well as the official
leaderboard of the CARLA urban driving simulator. At the time of submission,
TransFuser outperforms all prior work on the CARLA leaderboard in terms of
driving score by a large margin. Compared to geometry-based fusion, TransFuser
reduces the average collisions per kilometer by 48%.
