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CARIAD SE

Industry researcheurope · de
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Research library36linked papers
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Selected work

Representative Papers

Do Not Forget the Obvious - RISC: A Risk-Informed Slice-Coverage Protocol for Safe Autonomous Driving

Aug 12, 2026

Current autonomous driving evaluation methods rely on aggregate metrics that struggle to effectively capture system failures in high-risk, low-frequency scenarios. To address this limitation, this work proposes RISC—a risk-informed evaluation protocol that enables model-agnostic and interpretable stress testing through computable risk slices, lightweight data annotation, and risk-guided sampling. Furthermore, the framework leverages large language models to assist in identifying critical yet often overlooked scenarios. Evaluated on monocular pedestrian perception tasks, RISC dramatically improves the detection rate of critical failures from 34.0% to 98.5%, demonstrating its superior capability in efficiently uncovering high-risk system deficiencies.

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Proposal-Conditioned Latent Diffusion for Closed-Loop Traffic Scenario Generation

Jun 25, 2026

This work addresses the computational bottleneck in closed-loop traffic simulation, where efficiently generating multi-agent behaviors that are simultaneously consistent, controllable, and interactive remains challenging—particularly for real-time replanning in autonomous driving. The authors propose a diffusion-based traffic scene generation framework conditioned on instance-level scene context and multimodal behavioral priors, augmented with a test-time guidance mechanism to modulate safety-critical behaviors. Innovatively integrating proposal priors with a compact latent action representation, the method significantly improves sampling efficiency without retraining and enables flexible trade-offs during inference among realism, safety, and controllability. Experiments on the Waymo Open Motion Dataset demonstrate that the approach achieves a strong balance across these desiderata in diverse interactive scenarios while substantially reducing per-step inference latency.

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CoPark: Learning Reactive Parking via Self-Play

Jun 02, 2026

This work addresses the challenge of multi-vehicle cooperative parking, where sub-meter positioning accuracy and real-time collision avoidance impose conflicting demands. To reconcile these objectives, the authors propose CoPark, a multi-agent self-play reinforcement learning framework built upon a residual policy architecture that integrates offline planning with online reactive correction. Specifically, longitudinal maneuvers employ threat-aware action modulation to yield right-of-way, while lateral control adheres to a reference trajectory to ensure precision, complemented by a closed-loop optimization layer that corrects terminal errors. Evaluated on the newly introduced Dragon Lake Parking and DSC3D benchmarks, CoPark achieves zero-shot success rates of 70–85% with collision rates as low as 3–6%, substantially outperforming classical control, imitation learning, and large-scale reinforcement learning baselines, while also exhibiting emergent complex behaviors such as reverse-path yielding.

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Recent publications

Latest Papers

Do Not Forget the Obvious - RISC: A Risk-Informed Slice-Coverage Protocol for Safe Autonomous Driving

Aug 12, 2026

Current autonomous driving evaluation methods rely on aggregate metrics that struggle to effectively capture system failures in high-risk, low-frequency scenarios. To address this limitation, this work proposes RISC—a risk-informed evaluation protocol that enables model-agnostic and interpretable stress testing through computable risk slices, lightweight data annotation, and risk-guided sampling. Furthermore, the framework leverages large language models to assist in identifying critical yet often overlooked scenarios. Evaluated on monocular pedestrian perception tasks, RISC dramatically improves the detection rate of critical failures from 34.0% to 98.5%, demonstrating its superior capability in efficiently uncovering high-risk system deficiencies.

0 citationsRead paper

Proposal-Conditioned Latent Diffusion for Closed-Loop Traffic Scenario Generation

Jun 25, 2026

This work addresses the computational bottleneck in closed-loop traffic simulation, where efficiently generating multi-agent behaviors that are simultaneously consistent, controllable, and interactive remains challenging—particularly for real-time replanning in autonomous driving. The authors propose a diffusion-based traffic scene generation framework conditioned on instance-level scene context and multimodal behavioral priors, augmented with a test-time guidance mechanism to modulate safety-critical behaviors. Innovatively integrating proposal priors with a compact latent action representation, the method significantly improves sampling efficiency without retraining and enables flexible trade-offs during inference among realism, safety, and controllability. Experiments on the Waymo Open Motion Dataset demonstrate that the approach achieves a strong balance across these desiderata in diverse interactive scenarios while substantially reducing per-step inference latency.

0 citationsRead paper

CoPark: Learning Reactive Parking via Self-Play

Jun 02, 2026

This work addresses the challenge of multi-vehicle cooperative parking, where sub-meter positioning accuracy and real-time collision avoidance impose conflicting demands. To reconcile these objectives, the authors propose CoPark, a multi-agent self-play reinforcement learning framework built upon a residual policy architecture that integrates offline planning with online reactive correction. Specifically, longitudinal maneuvers employ threat-aware action modulation to yield right-of-way, while lateral control adheres to a reference trajectory to ensure precision, complemented by a closed-loop optimization layer that corrects terminal errors. Evaluated on the newly introduced Dragon Lake Parking and DSC3D benchmarks, CoPark achieves zero-shot success rates of 70–85% with collision rates as low as 3–6%, substantially outperforming classical control, imitation learning, and large-scale reinforcement learning baselines, while also exhibiting emergent complex behaviors such as reverse-path yielding.

0 citationsRead paper