arXiv AI

A Digital Twin Framework for Traffic-Aware UAV Pavement Monitoring in Open-Traffic Conditions

arXiv:2606. 20742v2 Announce Type: replace-cross Abstract: UAV-based pavement inspection can reduce the cost and risk of road-surface monitoring, but real-world deployment remains difficult when traffic, pedestrians, and temporary occlusions affect defect visibility.

arXiv Machine Learning

Jul 14

A Risk-Field Enhanced Closed-Loop Digital Twin Framework for Autonomous Driving Safety Validation

arXiv:2607. 09772v1 Announce Type: cross Abstract: Autonomous driving systems require reliable safety validation before real-world deployment.

By Yongzhi Liu

rag agents reinforcement-learning robotics safety

arXiv AI

Jun 6

Rethinking Infrastructure Inspection as Image Difference Classification: A Traffic Sign Case Study

arXiv:2606. 06375v1 Announce Type: new Abstract: Digital twins (DTs) allow the digitalization of road infrastructure inspection, though this is hindered by limited annotated data.

By Ching Yau Fergus Mok, Lavindra de Silva, Varun Kumar Reja, Ioannis Brilakis

Hugging Face Trending Papers

Jun 23

OmniPath: A Multi-Modal Agentic Framework for Auditing Wheelchair Accessibility

For a wheelchair user, a standard blue line on a map is often a broken promise. While platforms like OpenStreetMap (OSM) successfully capture where a path is, they frequently fail to convey how it physically feels to travel on it.

agents multimodal

arXiv AI

Jun 24

OmniPath: A Multi-Modal Agentic Framework for Auditing Wheelchair Accessibility

arXiv:2606. 24129v1 Announce Type: new Abstract: For a wheelchair user, a standard blue line on a map is often a broken promise.

By ASM Mobarak Hossain, Nadim Mahmud, Vaskar Raychoudhury, Md Osman Gani

agents multimodal

arXiv Machine Learning

Jun 10

WHU-Infra3D: A Full-stack Multi-modal Dataset and Benchmark for 3D Roadside Infrastructure Inventory

arXiv:2606. 09882v1 Announce Type: cross Abstract: The paradigm of digital twin cities is shifting from coarse visual mapping toward more precise and actionable digitization of urban assets.

By Chong Liu, Luxuan Fu, Xuyu Feng, Zhen Dong, Bisheng Yang

computer-vision multimodal benchmarks safety

arXiv AI

Jul 21

DSBench: A Comprehensive Benchmark for Evaluating External and In-Cabin Risks

arXiv:2511. 14592v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) show great promise for autonomous driving, but their suitability for safety-critical scenarios is largely unexplored, raising safety concerns.

By Xianhui Meng, Yuchen Zhang, Zhijian Huang, Zheng Lu, Ziling Ji, Yandan Lin, Yaoyao Yin, Hongyuan Zhang, Wei Zhou, Guangfeng Jiang, Li Zhang, Long Chen, Hangjun Ye, Jun Liu, Xiaoshuai Hao

llms agents robotics fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jul 20

PRISA: Proactive Infrastructure LiDAR Framework for Intersection Safety Assessment

arXiv:2607. 16156v1 Announce Type: new Abstract: Urban intersections are among the most hazardous locations in road networks, posing significant risks to vehicles and vulnerable road users (VRUs) such as pedestrians and cyclists.

By Tam Bang, Hussam Abubakr, Emiliano de la Garza Villarreal, Truc Phuong Nguyen, Austin Harris, Toru Hirano, Mina Sartipi, Yunfei Xu, Hoang H. Nguyen

agents safety

arXiv Machine Learning

Jun 30

In-Vehicle Digital Twin-Based Collision Warning Framework with Sybil Attack Detection

arXiv:2606. 28625v1 Announce Type: cross Abstract: Connected Vehicles (CVs) rely extensively on communication technologies to enable data-driven predictive analyses for enhancing performance and safety.

By Mohammad Imtiaz Hasan, Abyad Enan, Jean Michel Tine, Araf Rahman, M Sabbir Salek, Mashrur Chowdhury

safety

arXiv AI

2d ago

Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directions

arXiv:2410. 08491v3 Announce Type: replace-cross Abstract: Automated vehicles (AVs) promise to enhance transportation safety and efficiency.

By Saeed Rahmani, Sabine Rieder, Erwin de Gelder, Marcel Sonntag, Jorge Lorente Mallada, Sytze Kalisvaart, Vahid Hashemi, Bart van Arem, Simeon C. Calvert

safety

arXiv AI

22h ago

The 10th AI City Challenge

arXiv:2608. 17044v1 Announce Type: cross Abstract: The 10th AI City Challenge, held with ECCV 2026, marks a decade of community benchmarking for intelligent transportation, smart cities, and physical AI.

By Zheng Tang, Shuo Wang, David C. Anastasiu, Ming-Ching Chang, Anuj Sharma, Quan Kong, Munkhjargal Gochoo, Jun-Wei Hsieh, Tomasz Kornuta, Zhedong Zheng, Renran Tian, Judah Goldfeder, Fulgencio Navarro, Yuxing Wang, Yizhou Wang, Sameer Satish Pusegaonkar, Anqi Li, Nalin Dadhich, Ridham Kachhadiya, Dhanishtha Patil, Haoquan Liang, Jiajun Li, Han Zhang, Yilin Zhao, Zaid Pervaiz Bhat, Shuyu Yang, Ashutosh Kumar, Rong Wang, Rafael Martin Nieto, Peter Christiansen, Ahmed Abduljawad, Mohanrasu Shanmugam, Nadeem Shaik, Sujit Biswas, Xunlei Wu, Vidya Murali, Rama Chellappa

rag computer-vision fine-tuning multimodal benchmarks safety

arXiv AI

Jul 23

D3VL: Understanding Driving Scenes from 3D Time Series Data and Video with Language Models

arXiv:2607. 19528v1 Announce Type: cross Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have triggered the development of end-to-end MLLMs for autonomous driving.

By Heesang Han, A. Lynn Abbott, Abhijit Sarkar

llms agents robotics efficiency multimodal safety

arXiv Machine Learning

Jun 3

WildRoadBench: A Wild Aerial Road-Damage Grounding Benchmark for Vision-Language Models and Autonomous Agents

arXiv:2605. 20306v2 Announce Type: replace-cross Abstract: We introduce WildRoadBench, a wild aerial road-damage grounding benchmark that couples direct visual grounding by vision-language models with autonomous research-and-engineering by LLM-driven agents on a single professionally annotated UAV corpus.

By Bingnan Liu, Chenhang Cui, Rui Huang, Jiani Luo, Zhirong Shen, Tinghao Wang, Xiande Huang, Lingbei Meng, Fei Shen, An Zhang

llms agents multimodal benchmarks

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