4 citations · 8 across the 14 of their papers we have counts for
6 papers · 1 filter
PRISM: Privileged Probabilistic Latent Supervision for End-to-End Autonomous Driving Motion Planning
Volodymyr Havrylov, Faris Janjoš, Andreas Look +2
End-to-end autonomous driving (E2E AD) systems integrate perception, prediction, and planning into a single differentiable architecture. While these models show great promise, thei…
Fail2Drive: Benchmarking Closed-Loop Driving Generalization
Simon Gerstenecker, Andreas Geiger, Katrin Renz
Generalization under distribution shift remains a central bottleneck for closed-loop autonomous driving. Although simulators like CARLA enable safe and scalable testing, existing b…
PlanT 2.0: Exposing Biases and Structural Flaws in Closed-Loop Driving
Simon Gerstenecker, Andreas Geiger, Katrin Renz
Most recent work in autonomous driving has prioritized benchmark performance and methodological innovation over in-depth analysis of model failures, biases, and shortcut learning.…
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…
Centaur: Robust End-to-End Autonomous Driving with Test-Time Training
Chonghao Sima, Kashyap Chitta, Zhiding Yu +5
How can we rely on an end-to-end autonomous vehicle's complex decision-making system during deployment? One common solution is to have a ``fallback layer'' that checks the planned…
SLEDGE: Synthesizing Driving Environments with Generative Models and Rule-Based Traffic
Kashyap Chitta, Daniel Dauner, Andreas Geiger
SLEDGE is the first generative simulator for vehicle motion planning trained on real-world driving logs. Its core component is a learned model that is able to generate agent boundi…