4 papers
Comfort by Construction: Adaptive, Comfort-Bounded Action Spaces for Learned Driving Policies
Anna Rothenhäusler, Daniel Jost, Raghu Rajan +4
Data-driven driving simulators command accelerations and steering rates from a fixed grid without constraining the realized accelerations and jerks. As a result, reinforcement-lear…
Beyond Self-Play and Scale: A Behavior Benchmark for Generalization in Autonomous Driving
Aron Distelzweig, Faris Janjoš, Andreas Look +7
Recent Autonomous Driving (AD) works such as GigaFlow and PufferDrive have unlocked Reinforcement Learning (RL) at scale as a training strategy for driving policies. Yet such polic…
Don't double it: Efficient Agent Prediction in Occlusions
Anna Rothenhäusler, Markus Mazzola, Andreas Look +2
Occluded traffic agents pose a significant challenge for autonomous vehicles, as hidden pedestrians or vehicles can appear unexpectedly, yet this problem remains understudied. Exis…
Perfect Prediction or Plenty of Proposals? What Matters Most in Planning for Autonomous Driving
Aron Distelzweig, Faris Janjoš, Oliver Scheel +3
Traditionally, prediction and planning in autonomous driving (AD) have been treated as separate, sequential modules. Recently, there has been a growing shift towards tighter integr…