arXiv AI
Jul 29
Real-Time Driver Safety Scoring Through Inverse Crash Probability Modeling
arXiv:2603. 14841v3 Announce Type: replace-cross Abstract: Road crashes remain a leading cause of preventable fatalities.
Credibility-Weighted Pricing of Autonomous Vehicle Liability Under Operational Design Domain Shift
arXiv:2606. 17451v1 Announce Type: new Abstract: Automated Driving System deployments create a foundational ratemaking challenge: sparse experience, shifting operational design domains, and non-stationary risk across software releases.
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arXiv AI
3d ago
Adversarial Trust Poisoning in Vehicular Collaborative Perception
The paper introduces TrustFlip, an attack that exploits consistency‑based defenses in vehicular collaborative perception by deploying physical adversarial objects to create inconsistent observations among benign vehicles. This misattribution lowers the trust score of a targeted vehicle, leading to its exclusion from the collaboration and a degradation of perception performance. The authors evaluate the attack across multiple architectures, showing it can remove a benign vehicle in up to 87.7% of scenarios and reduce Average Precision by up to 13%, and propose a mitigation called TrustReflect that reduces the attack success rate by 35–100%.
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.
arXiv Machine Learning
Aug 20
Trust as a Field: A Macroscopic Representation for Vehicular Networks
The paper introduces a spatio‑temporal trust‑field framework that aggregates individual vehicle trust into a continuous representation over road segments. Using simulation‑based experiments with synthetic trajectories, the authors analyze trust‑field behavior in simple road scenarios. They also explore reconstructing the full trust field from sparse roadside‑unit measurements, comparing a coordinate‑based deep learning baseline with a field‑informed method that enforces measurement consistency, finding the latter yields more accurate recovery of low‑trust patterns and lower reconstruction error.
arXiv AI
Jun 8
ScenicRules: An Autonomous Driving Benchmark with Multi-Objective Specifications and Abstract Scenarios
arXiv:2602. 16073v2 Announce Type: replace-cross Abstract: Developing autonomous driving systems for complex traffic environments requires balancing multiple objectives, such as avoiding collisions, obeying traffic rules, and making efficient progress.
arXiv AI
Jul 2
Creating Impactful Autonomous Driving Datasets: A Strategic Guide from Research Gap to Benchmark
arXiv:2607. 00710v1 Announce Type: cross Abstract: Well-designed autonomous driving datasets have fundamentally shaped research progress, yet existing literature primarily describes what datasets contain rather than how to strategically design impactful ones.
arXiv AI
Aug 19
Physics-Grounded Causal Auditing of End-to-End Driving Planners
The paper introduces CADET, a training‑free framework for auditing, benchmarking, and repairing spurious reliance in pretrained end‑to‑end autonomous‑driving planners. It addresses the problem that such planners often learn statistical shortcuts—associating co‑occurring scene elements with driving decisions—rather than causal variables, which undermines reliability in rare scenarios. CADET can detect and correct these causal confusions without retraining the model or updating its parameters.
arXiv AI
2d ago
CoLT-Drive: Counterfactual Long-Tail Benchmarking and Knowledge-Preserving Adaptation for Driving Affordance Prediction
The paper introduces CoLT-Drive, a 3,536-sample counterfactual long‑tail benchmark for evaluating decision‑level driving affordance prediction, which tests whether models can infer how rare objects affect an ego vehicle’s high‑level actions. It also proposes KPA, a knowledge‑preserving adaptation framework that combines structured prompting, expert merging, and a regime‑aware LoRA mixture‑of‑experts module to improve small VLMs on driving tasks. Experiments show KPA achieves 60.8% pair accuracy on CoLT‑Drive, outperforming the Qwen3‑VL‑2B baseline and LoRA SFT while keeping competitive in‑domain performance.
arXiv AI
Jun 15
CADET: Physics-Grounded Causal Auditing and Training-Free Deconfounding of End-to-End Driving Planners
arXiv:2606. 14438v1 Announce Type: cross Abstract: End-to-end (E2E) autonomous-driving planners trained by imitation are prone to statistical shortcuts: they associate scene elements that merely co-occur with expert actions (a roadside object, a building facade) with driving decisions, rather than the variables that causally determine them.
arXiv AI
2d ago
Risk-Aware Decision-Making for Autonomous Overtaking: A World Model-Based Mixture-of-Experts Framework
The paper introduces WM‑RMoE, a World Model‑based Risk‑aware Mixture‑of‑Experts framework for autonomous overtaking. It uses a learned latent dynamics model to perform multi‑step rollouts, evaluating cumulative risk at the trajectory level, and employs a hierarchical gating mechanism to coordinate long‑, short‑horizon, and rule‑based safety experts. A Gaussian Mixture Model preserves multimodal maneuver branches, improving robustness and preventing behavioral averaging, leading to better safety compliance, decision stability, and generalization in experiments.
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
3d ago
What Emerges and What Breaks in Self-Play Driving
The paper reports on training autonomous driving policies via self‑play, extending previous work by using Transformers and a real‑city high‑definition map. On CARLA and Waymo benchmarks, the resulting policies underperform compared to Gigaflow, with identified failure modes such as reward hacking at traffic lights and lack of incentive to stop at stop signs. The authors also analyze which traffic rules emerge from self‑play and confirm that reward conditioning produces diverse driving behaviors.