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

Semantics-guided Transformer-based Sensor Fusion for Improved Waypoint Prediction