28 citations · 136 across the 33 of their papers we have counts for
41 papers
Guiding End-to-End Driving Models with Endpoint-Constrained Trajectory Optimization
Brayden Zhang, Mahsa Golchoubian, Igor Gilitschenski +2
End-to-end driving policies are commonly trained through open-loop behavior cloning, yet they must ultimately operate in closed-loop when deployed on a vehicle, creating a fundamen…
OPTED: On-Policy Fine-Tuning for End-to-End Driving using a Render-Free Teacher
Damiano Da Col, Maximilian Igl, Peter Karkus +5
As scaling pre-training data alone yields diminishing returns, post-training is becoming increasingly important across physical AI domains such as autonomous driving. End-to-end dr…
Valdi: Value Diffusion World Models
Christopher Lindenberg, Kashyap Chitta
World models can enable Model Predictive Control (MPC), but this requires dynamics prediction that is both fast enough for online use and expressive enough to represent uncertain f…
World Engine: Towards the Era of Post-Training for Autonomous Driving
Tianyu Li, Li Chen, Caojun Wang +16
Autonomous vehicles must operate safely in the real world, where errors can have severe consequences. Although modern end-to-end driving policies excel in routine scenarios, their…
DriveJudge: Rethinking Autonomous Driving Evaluation with Vision-Language Models
Xinglong Sun, Kevin Xie, Jenny Schmalfuss +5
Autonomous driving has shifted towards end-to-end policy learning, where reliable, interpretable policy evaluation is a fundamental challenge as driving quality is highly context-d…
123D: Unifying Multi-Modal Autonomous Driving Data at Scale
Daniel Dauner, Valentin Charraut, Bastian Berle +10
The pursuit of autonomous driving has produced one of the richest sensor data collections in all of robotics. However, its scale and diversity remain largely untapped. Each dataset…
Dynamics Distillation for Efficient and Transferable Control Learning
Xunjiang Gu, Kashyap Chitta, Mahsa Golchoubian +2
Robust control policy learning for autonomous driving requires training environments to be both physically realistic and computationally scalable, properties that existing simulato…
LEAD: Minimizing Learner-Expert Asymmetry in End-to-End Driving
Long Nguyen, Micha Fauth, Bernhard Jaeger +4
Simulators can generate virtually unlimited driving data, yet imitation learning policies in simulation still struggle to achieve robust closed-loop performance. Motivated by this…
Latent Chain-of-Thought World Modeling for End-to-End Driving
Shuhan Tan, Kashyap Chitta, Yuxiao Chen +8
Recent Vision-Language-Action (VLA) models for autonomous driving explore inference-time reasoning as a way to improve driving performance and safety in challenging scenarios. Most…
Optimization-Guided Diffusion for Interactive Scene Generation
Shihao Li, Naisheng Ye, Tianyu Li +7
Realistic and diverse multi-agent driving scenes are crucial for evaluating autonomous vehicles, but safety-critical events which are essential for this task are rare and underrepr…
Agility Meets Stability: Versatile Humanoid Control with Heterogeneous Data
Yixuan Pan, Ruoyi Qiao, Li Chen +8
Humanoid robots are envisioned to perform a wide range of tasks in human-centered environments, requiring controllers that combine agility with robust balance. Recent advances in l…
ReSim: Reliable World Simulation for Autonomous Driving
Jiazhi Yang, Kashyap Chitta, Shenyuan Gao +7
How can we reliably simulate future driving scenarios under a wide range of ego driving behaviors? Recent driving world models, developed exclusively on real-world driving data com…
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…
CaRL: Learning Scalable Planning Policies with Simple Rewards
Bernhard Jaeger, Daniel Dauner, Jens Beißwenger +3
We investigate reinforcement learning (RL) for privileged planning in autonomous driving. State-of-the-art approaches for this task are rule-based, but these methods do not scale t…
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…
Hidden Biases of End-to-End Driving Datasets
Julian Zimmerlin, Jens Beißwenger, Bernhard Jaeger +2
End-to-end driving systems have made rapid progress, but have so far not been applied to the challenging new CARLA Leaderboard 2.0. Further, while there is a large body of literatu…
NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking
Daniel Dauner, Marcel Hallgarten, Tianyu Li +9
Benchmarking vision-based driving policies is challenging. On one hand, open-loop evaluation with real data is easy, but these results do not reflect closed-loop performance. On th…
Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability
Shenyuan Gao, Jiazhi Yang, Li Chen +5
World models can foresee the outcomes of different actions, which is of paramount importance for autonomous driving. Nevertheless, existing driving world models still have limitati…
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…
GenAD: Generalized Predictive Model for Autonomous Driving
Jiazhi Yang, Shenyuan Gao, Yihang Qiu +11
In this paper, we introduce the first large-scale video prediction model in the autonomous driving discipline. To eliminate the restriction of high-cost data collection and empower…
DriveLM: Driving with Graph Visual Question Answering
Chonghao Sima, Katrin Renz, Kashyap Chitta +7
We study how vision-language models (VLMs) trained on web-scale data can be integrated into end-to-end driving systems to boost generalization and enable interactivity with human u…
On Offline Evaluation of 3D Object Detection for Autonomous Driving
Tim Schreier, Katrin Renz, Andreas Geiger +1
Prior work in 3D object detection evaluates models using offline metrics like average precision since closed-loop online evaluation on the downstream driving task is costly. Howeve…
End-to-end Autonomous Driving: Challenges and Frontiers
Li Chen, Penghao Wu, Kashyap Chitta +3
The autonomous driving community has witnessed a rapid growth in approaches that embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle motion pl…
Parting with Misconceptions about Learning-based Vehicle Motion Planning
Daniel Dauner, Marcel Hallgarten, Andreas Geiger +1
The release of nuPlan marks a new era in vehicle motion planning research, offering the first large-scale real-world dataset and evaluation schemes requiring both precise short-ter…