arXiv Machine Learning

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

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%.

By Yutong Liu, Chenyi Wang, Ming F. Li, Qingzhao Zhang

agents benchmarks safety

arXiv AI

Jun 17

C2FL: Clustered Continual Federated Learning under Spatial and Temporal Drift

arXiv:2606. 18003v1 Announce Type: cross Abstract: Collective Adaptive Systems (CAS) increasingly rely on machine learning to let each node learn from locally sensed data, aligning its behavior with the surrounding environment.

By Davide Domini, Gianluca Aguzzi, Lorenzo Pellegrini, Mirko Viroli, Lukas Esterle

robotics safety

arXiv AI

Aug 13

Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment

arXiv:2608. 12198v1 Announce Type: cross Abstract: Recent research in machine and deep learning has shown the potential of learningbased motion planning approaches to improve the driving behavior of automated vehicles, especially in complex environments.

By Jean-Pierre Busch, Guido Linden, Jan Bergmann, Lutz Eckstein

safety

arXiv AI

Jun 6

RiskFlow: Fast and Faithful Safety-Critical Traffic Scenario Generation

arXiv:2606. 06423v1 Announce Type: cross Abstract: Safety-critical traffic scenario generation is essential for evaluating autonomous driving systems under rare but high-risk interactions.

By Qi Lan, Yining Tang, Yu Shen, Yi Zhou, Yuhao Wei, Jie Li, Guofa Li

agents diffusion robotics safety

arXiv Computer Vision

1d ago

VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation

arXiv:2609.02462v1 Announce Type: new Abstract: End-to-end autonomous driving in urban environments requires robust decision-making under partial observability and complex multi-agent interactions. S...

By Hoonhee Cho, Jae-Young Kang, Giwon Lee, Hyemin Yang, Heejun Park, Kuk-Jin Yoon

agents robotics benchmarks

arXiv Machine Learning

Aug 27

Quantum-Inspired Modeling of Driving Behavior

The paper introduces a quantum-inspired representation of driver behavior that models drivers as evolving density matrices, capturing continuous, probabilistic, context-dependent, and history-dependent interactions among behavioral variables. Trained unsupervised on the I‑24 MOTION dataset, the framework identifies three interpretable driving regimes—free flow, transition, and congestion—and reproduces macroscopic traffic phenomena such as the fundamental diagram and hysteresis loops. The representation also enhances practical applications by providing context-dependent parameters for classical car‑following models and enabling autonomous vehicles to forecast nearby drivers’ motion in real time.

By Mohammad Elayan, Omid Armantalab, Wissam Kontar

agents safety

arXiv Machine Learning

Jun 8

Federated Foundation Models over Vehicular Networks

arXiv:2606. 06786v1 Announce Type: new Abstract: This paper presents a forward-looking vision for integrating the emerging multi-modal multi-task federated foundation models (M3T FedFMs) into vehicular networks, with the goal of unifying the expressive power of multi-modal multi-task foundation models (M3T FMs) with the privacy-preserving and distributed learning capabilities of federated learning (FL).

By Kasra Borazjani, Fardis Nadimi, Payam Abdisarabshali, Owen Palinski, Allan Salihovic, Dinh Nguyen, Minghui Liwang, Seyyedali Hosseinalipour

fine-tuning multimodal safety

arXiv Machine Learning

Aug 26

Adaptive Multi-Mode Out-of-Distribution Detection for Trajectory Prediction in Autonomous Vehicles

arXiv:2509.13577v3 Announce Type: replace-cross Abstract: Trustworthy trajectory prediction grounds autonomous vehicle (AV) safety, yet deployed models inevitably face out-of-distribution (OOD) scene...

By Tongfei Guo, Lili Su

agents multimodal benchmarks safety

arXiv Machine Learning

Jul 10

Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learning

arXiv:2607. 08137v1 Announce Type: cross Abstract: Federated reinforcement learning (FRL) is crucial for enabling collaborative learning across multiple agents without sharing raw data, thereby enhancing privacy and scalability in the decision-making process within dynamic vehicular environments.

By Zifan Zhang, Minghong Fang, Dianwei Chen, Zhuqing Liu, Prashant Khanduri, Xianfeng Yang, Anupam Das, Yuchen Liu

agents reinforcement-learning robotics safety

arXiv Machine Learning

Jul 8

Deep Reinforcement Learning for Dynamic Origin-Destination Matrix Estimation in Microscopic Traffic Simulations Considering Credit Assignment

arXiv:2511. 06229v3 Announce Type: replace Abstract: This paper focuses on dynamic origin-destination matrix estimation (DODE), a crucial calibration process necessary for the effective application of microscopic traffic simulations.

By Donggyu Min, Seongjin Choi, Dong-Kyu Kim

agents reinforcement-learning

arXiv Computer Vision

3d ago

HybridWorldSim: A Scalable and Controllable High-fidelity Simulator for Autonomous Driving

arXiv:2511.22187v4 Announce Type: replace Abstract: Realistic and controllable simulation is critical for advancing end-to-end autonomous driving, yet existing approaches often struggle to support no...

By Qiang Li, Yingwenqi Jiang, Tuoxi Li, Duyu Chen, Xiang Feng, Yucheng Ao, Shangyue Liu, Xingchen Yu, Youcheng Cai, Yumeng Liu, Yuexin Ma, Xin Hu, Li Liu, Yu Zhang, Linkun Xu, Bingtao Gao, Xueyuan Wang, Shuchang Zhou, Xianming Liu, Ligang Liu

agents robotics benchmarks

arXiv Machine Learning

Jun 17

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.

By Doyeon Jang

agents

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