The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …

How should we integrate representations from complementary sensors for autonomous
driving? Geometry-based fusion has shown promise for perception (eg, object detection …

How should representations from complementary sensors be integrated for autonomous
driving? Geometry-based sensor fusion has shown great promise for perception tasks such …

The release of nuPlan marks a new era in vehicle motion planning research, offering the first
large-scale real-world dataset and evaluation schemes requiring both precise short-term …

End-to-end driving systems have recently made rapid progress, in particular on CARLA.
Independent of their major contribution, they introduce changes to minor system …

Efficient reasoning about the semantic, spatial, and temporal structure of a scene is a crucial
prerequisite for autonomous driving. We present NEural ATtention fields (NEAT), a novel …

End-to-end approaches to autonomous driving commonly rely on expert demonstrations.
Although humans are good drivers, they are not good coaches for end-to-end algorithms …

Planning an optimal route in a complex environment requires efficient reasoning about the
surrounding scene. While human drivers prioritize important objects and ignore details not …

End-to-end autonomous driving has great potential in the transportation industry. However,
the lack of transparency and interpretability of the automatic decision-making process …

Simulators offer the possibility of safe, low-cost development of self-driving systems.
However, current driving simulators exhibit naïve behavior models for background traffic …