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Steffen Hagedorn

2 accepted papers

2026

When Planners Meet Reality: How Learned, Reactive Traffic Agents Shift nuPlan Benchmarks

ICRA 2026poster

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 used IDM agents simply follow a lead vehicle and cannot react to vehicles in adjacent lanes, hindering tests of complex int…

2025

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback

IROS 2025

In automated driving, predicting trajectories of surrounding vehicles supports reasoning about scene dynamics and enables safe planning for the ego vehicle. However, existing models handle predictions as an instantaneous task of forecasting future trajectories based on observed information. As time

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