Sensor fusion approaches for intelligent self-driving agents remain key to
driving scene understanding given visual global contexts acquired from input
sensors. Specifically, for the local waypoint prediction task, single-modality
networks are still limited by strong dependency on the sensitivity of the input
sensor, and thus recent works promote the use of multiple sensors in fusion in
feature level. While it is well known that multiple data modalities promote
mutual contextual exchange, deployment to practical driving scenarios requires
global 3D scene understanding in real-time with minimal computations, thus
placing greater significance on training strategies given a limited number of
practically usable sensors. In this light, we exploit carefully selected
auxiliary tasks that are highly correlated with the target task of interest
(e.g., traffic light recognition and semantic segmentation) by fusing auxiliary
task features and also using auxiliary heads for waypoint prediction based on
imitation learning. Our multi-task feature fusion augments and improves the
base network, TransFuser, by significant margins for safer and more complete
road navigation in CARLA simulator as validated on the Town05 Benchmark through
extensive experiments.

传感器融合在智能自驾代理中是关键，本文通过融合辅助任务和利用模仿学习来改进基础网络以提高道路导航的安全性和完整性。