How should representations from complementary sensors be integrated for autonomous driving? Geometry-based sensor fusion has shown great promise for perception tasks such as object detection and motion forecasting. However, for the actual driving task, the global context of the 3D scene is key: a change in traffic light state can affect the behavior of a vehicle geometrically distant from that traffic light. Geometry alone may therefore be insufficient for effectively fusing representations in end-to-end driving models. In this talk, I will demonstrate that existing sensor fusion methods under...-perform in the presence of a high density of dynamic agents and complex scenarios, which require global contextual reasoning such as handling traffic oncoming from multiple directions at uncontrolled intersections. Towards tackling this challenge, I will present TransFuser, a novel Multi-Modal Fusion Transformer, to integrate image and LiDAR representations using attention. Moreover, in the second part of the talk, I wi
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