18 citations · 71 across the 50 of their papers we have counts for
7 papers · 2 filters
Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning
Zhiyu Huang, Xinshuo Weng, Maximilian Igl +5
Autonomous driving necessitates the ability to reason about future interactions between traffic agents and to make informed evaluations for planning. This paper introduces the \tex…
System-Level Safety Monitoring and Recovery for Perception Failures in Autonomous Vehicles
Kaustav Chakraborty, Zeyuan Feng, Sushant Veer +4
The safety-critical nature of autonomous vehicle (AV) operation necessitates development of task-relevant algorithms that can reason about safety at the system level and not just a…
LoRD: Adapting Differentiable Driving Policies to Distribution Shifts
Christopher Diehl, Peter Karkus, Sushant Veer +2
Distribution shifts between operational domains can severely affect the performance of learned models in self-driving vehicles (SDVs). While this is a well-established problem, pri…
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…
RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation Learned Planners and Predictors for Robustness under Distribution Shifts
Jay Patrikar, Sushant Veer, Apoorva Sharma +2
Modern motion planners for autonomous driving frequently use imitation learning (IL) to draw from expert driving logs. Although IL benefits from its ability to glean nuanced and mu…
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…