1 citations · 2 across the 6 of their papers we have counts for
6 papers
Dynamics Distillation for Efficient and Transferable Control Learning
Xunjiang Gu, Kashyap Chitta, Mahsa Golchoubian +2
Robust control policy learning for autonomous driving requires training environments to be both physically realistic and computationally scalable, properties that existing simulato…
Delving into Mapping Uncertainty for Mapless Trajectory Prediction
Zongzheng Zhang, Xuchong Qiu, Boran Zhang +11
Recent advances in autonomous driving are moving towards mapless approaches, where High-Definition (HD) maps are generated online directly from sensor data, reducing the need for e…
Pseudo-Simulation for Autonomous Driving
Wei Cao, Marcel Hallgarten, Tianyu Li +11
Existing evaluation paradigms for Autonomous Vehicles (AVs) face critical limitations. Real-world evaluation is often challenging due to safety concerns and a lack of reproducibili…
Data Scaling Laws for End-to-End Autonomous Driving
Alexander Naumann, Xunjiang Gu, Tolga Dimlioglu +7
Autonomous vehicle (AV) stacks have traditionally relied on decomposed approaches, with separate modules handling perception, prediction, and planning. However, this design introdu…
Accelerating Online Mapping and Behavior Prediction via Direct BEV Feature Attention
Xunjiang Gu, Guanyu Song, Igor Gilitschenski +2
Understanding road geometry is a critical component of the autonomous vehicle (AV) stack. While high-definition (HD) maps can readily provide such information, they suffer from hig…
Producing and Leveraging Online Map Uncertainty in Trajectory Prediction
Xunjiang Gu, Guanyu Song, Igor Gilitschenski +2
High-definition (HD) maps have played an integral role in the development of modern autonomous vehicle (AV) stacks, albeit with high associated labeling and maintenance costs. As a…