9 papers
INTERACT: Interactive Planning for Autonomous Driving via Anchor-Conditioned Prediction and Trust-Region Refinement
Aron Distelzweig, Andreas Look, Faris Janjoš +3
Driving in dense urban traffic is interactive: whether a merge or an unprotected turn succeeds depends on how surrounding agents respond to the ego vehicle. Conventional planners p…
DiffuSearch: How Hybrid Trajectory Planning Benefits from Aligned Objectives in Diffusion and Action Space
Steffen Hagedorn, Aron Distelzweig, Alexandru P. Condurache
In trajectory planning for autonomous driving, hybrid planning architectures are often realized as a collection of disparate modules, each with its own objectives. This lack of a u…
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…
Driving is a Game: Combining Planning and Prediction with Bayesian Iterative Best Response
Aron Distelzweig, Yiwei Wang, Faris Janjoš +5
Autonomous driving planning systems perform nearly perfectly in routine scenarios using lightweight, rule-based methods but still struggle in dense urban traffic, where lane change…
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…
When Planners Meet Reality: How Learned, Reactive Traffic Agents Shift nuPlan Benchmarks
Steffen Hagedorn, Luka Donkov, Aron Distelzweig +1
Planner evaluation in closed-loop simulation often uses rule-based traffic agents, whose simplistic and passive behavior can hide planner deficiencies and bias rankings. Widely use…
Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback
Steffen Hagedorn, Aron Distelzweig, Marcel Hallgarten +1
In automated driving, predicting trajectories of surrounding vehicles supports reasoning about scene dynamics and enables safe planning for the ego vehicle. However, existing model…
Stochasticity in Motion: An Information-Theoretic Approach to Trajectory Prediction
Aron Distelzweig, Andreas Look, Eitan Kosman +3
In autonomous driving, accurate motion prediction is crucial for safe and efficient motion planning. To ensure safety, planners require reliable uncertainty estimates of the predic…
Motion Forecasting via Model-Based Risk Minimization
Aron Distelzweig, Eitan Kosman, Andreas Look +3
Forecasting the future trajectories of surrounding agents is crucial for autonomous vehicles to ensure safe, efficient, and comfortable route planning. While model ensembling has i…