arXiv AI
Jun 2
From Cues to Horizons: Dynamic Risk Horizon Profiling for Trajectory Prediction
arXiv:2606. 00857v1 Announce Type: cross Abstract: Accurate and reliable vehicle trajectory prediction is essential for safe autonomous driving.
Super Agents and Confounders: Influence of surrounding agents on vehicle trajectory prediction
The paper analyzes state‑of‑the‑art vehicle trajectory predictors and finds that many surrounding agents actually worsen prediction accuracy. Using Shapley attribution, it shows models learn unstable, non‑causal decision schemes that vary across training runs. The authors introduce a Conditional Information Bottleneck that compresses agent features and ignores unhelpful ones, improving accuracy and robustness across datasets and architectures.
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arXiv AI
Jun 17
DriveJudge: Rethinking Autonomous Driving Evaluation with Vision-Language Models
arXiv:2606. 17362v1 Announce Type: cross Abstract: 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-dependent.
arXiv AI
Jun 16
Driving, Fast or Slow? Neuro-Symbolic Guidance for Motion Prediction in Multi-Modal Ground Mobility
arXiv:2606. 15251v1 Announce Type: cross Abstract: Accurate and interpretable motion prediction for heterogeneous traffic spaces, including pedestrians, bicycles, cars, and trucks, is essential for safe autonomous navigation.
arXiv AI
Oct 9
More Than Meets the Eye? Uncovering the Reasoning-Planning Disconnect in Training Vision-Language Driving Models
The paper introduces DriveMind, a large-scale driving Visual Question Answering corpus that aligns chain-of-thought reasoning with trajectory planning. Experiments with Vision‑Language Model agents show a causal disconnect: removing navigation priors significantly harms planning, while removing the chain-of-thought has little effect. The authors propose the Reasoning‑Planning Decoupling Hypothesis and present a training‑free probe to assess an agent’s reliance on priors.
arXiv Machine Learning
Jun 30
Zero-Label Driving Scenario Complexity Detection via Joint Embedding Predictive Architecture
arXiv:2606. 28383v1 Announce Type: cross Abstract: Identifying complex and safety-critical driving scenarios in large unlabelled datasets is an important but expensive problem.
arXiv AI
Jul 8
Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models
arXiv:2607. 06328v1 Announce Type: new Abstract: The increasing adoption of end-to-end learning for autonomous driving introduces increased model complexity and opacity, raising the risk of learning undesired or erroneous behavior.
arXiv AI
Jul 2
What's Hidden Matters: Identifying Planning-Critical Occluded Agents using Vision-Language Models
arXiv:2607. 00283v1 Announce Type: cross Abstract: Autonomous vehicles must safely navigate complex environments where planning-critical agents may be hidden from view.
arXiv Machine Learning
Sep 25
Active Client Selection in Federated Trajectory Prediction with Uncertainty-Awareness and Heterogeneous Complexity
The paper introduces active client selection strategies for federated learning in autonomous vehicle trajectory prediction, addressing challenges of high scene uncertainty and heterogeneous complexity across different driving environments. It proposes uncertainty-aware selectors that use per-client negative log-likelihood and aleatoric uncertainty, as well as a joint selector that balances scene complexity and uncertainty to prioritize informative clients. Experiments on the Argoverse dataset show that federated models outperform local training, with uncertainty-aware selection speeding convergence and improving key metrics, while the joint selector yields the best generalization under strong heterogeneity.
arXiv AI
Aug 5
Long-term Traffic Scene Prediction via Polynomial Representations in Autonomous Driving
arXiv:2608. 03330v1 Announce Type: new Abstract: This thesis addresses fundamental challenges in traffic scene prediction for autonomous driving by introducing robust and computationally efficient models based on polynomial representations.
arXiv AI
Aug 11
OmniV2X: A Generative Foundation Planner for Efficient End-to-End Cooperative Driving
arXiv:2606. 21165v2 Announce Type: replace-cross Abstract: We present OmniV2X, a generative foundation model for vehicle-to-everything (V2X) cooperative driving.
arXiv AI
Aug 3
Auto-JEPA: A Latent World Model of Continuous Intent for End-to-End Autonomous Driving
arXiv:2607. 29031v1 Announce Type: cross Abstract: Existing autonomous-driving world models typically perform dense prediction of future videos, occupancy states, BEV representations, or agent motion.
Hugging Face Trending Papers
Aug 18
Plug-and-Play Traffic Element Awareness for End-to-End Autonomous Driving
The paper introduces a plug‑and‑play method that injects traffic‑element signals—such as traffic lights and road signs—into end‑to‑end autonomous driving models with minimal architectural changes. By augmenting several public datasets with comprehensive traffic‑element annotations, the authors evaluate this integration across diverse driving paradigms, consistently improving performance on nuScenes, NAVSIM‑v1, NAVSIM‑v2, and Bench2Drive. The approach achieves a new state‑of‑the‑art result on the challenging NAVSIM‑v2 benchmark, demonstrating the broad utility of traffic‑element awareness.