arXiv Machine Learning

DriveDNA: A Large-Scale Multimodal Naturalistic Driving Dataset and Benchmark for Driving Style Identification

arXiv:2607. 23822v1 Announce Type: new Abstract: Driving style captures stable, driver-specific patterns in how a vehicle is driven.

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.

By Xinglong Sun, Kevin Xie, Jenny Schmalfuss, Despoina Paschalidou, Xiuming Zhang, Sanja Fidler, Kashyap Chitta, Jose M. Alvarez

llms agents robotics multimodal benchmarks

arXiv AI

Jul 21

Spatiotemporal Facial Action Unit Detection using Twin Cycle Autoencoders for Driver Monitoring

arXiv:2607. 16760v1 Announce Type: cross Abstract: Driver monitoring systems (DMS) increasingly rely on facial cues to infer drowsiness, distraction, and cognitive load in real time.

By Sai Sidharth D

efficiency benchmarks safety

arXiv Machine Learning

5d ago

TraVEL: Trajectory-Guided Video Embedding Learning for Driving-Video Retrieval

arXiv:2608. 13495v1 Announce Type: cross Abstract: Efficiently retrieving relevant clips from large-scale driving logs is essential for data curation, model development, and safety analysis.

By Yi-Chung Chen, Philip Jacobson, Tom Lampo, Yiren Lu, Jin Yao, David I. Inouye, Jing Gao, Danhua Guo, Burhan Yaman

rag fine-tuning multimodal benchmarks safety

arXiv AI

Jun 26

Risk-Aware Selective Multimodal Driver Monitoring with Driver-State World Modeling

arXiv:2606. 26922v1 Announce Type: cross Abstract: Continuous driver monitoring in automated vehicles requires low-latency inference while avoiding unsafe decisions under uncertain driver states.

By Daosheng Qiu, Haozhuang Chi, Hao Su, Shu Long, Xinyue Miao, Yongle Dong, Wei Zhang

llms efficiency multimodal safety

arXiv Machine Learning

Jul 13

Multimodal Scenario Similarity Search for Autonomous Driving

arXiv:2607. 09428v1 Announce Type: cross Abstract: Large-scale autonomous-driving datasets contain vast numbers of recorded scenarios, creating a need for efficient retrieval methods that can identify situations similar to a given query.

By Tam\'as Matuszka, Andr\'as Tam\'asy, Bal\'azs Szol\'ar

llms rag agents robotics multimodal

arXiv AI

1d ago

NARRATE: A Multimodal Real-World Australian Driving Dataset for Human-Centred Explanations in Automated Driving

arXiv:2608. 14767v1 Announce Type: cross Abstract: Automated vehicles must explain their decisions in ways that passengers can understand, monitor, and trust.

By Ashkan Yousefi Zadeh, Zishuo Zhu, Xiaomeng Li, Andry Rakotonirainy, Sebastien Glaser, Ronald Schroeter, Patricia Delhomme, Zahra Mehraban

multimodal benchmarks

arXiv AI

Jun 30

VISTA-DZ: Visual Semantic Trajectory Adaptation for Personalized Dilemma Zone Prediction

arXiv:2606. 29548v1 Announce Type: cross Abstract: Driver decision making in the dilemma zone at signalized intersections is safety critical, as vehicles approaching a yellow signal must decide whether to stop or proceed within limited time and distance margins.

By Chuheng Wei, Ziye Qin, Ziran Wang, Guoyuan Wu

llms rag multimodal safety

arXiv AI

Jun 12

PersonaDrive: Human-Style Retrieval-Augmented VLA Agents for Closed-Loop Driving Simulation

arXiv:2606. 12616v1 Announce Type: new Abstract: Closed-loop driving simulators typically populate their environments with non-ego traffic agents that behave largely the same way, produced either by rule-based traffic managers or by learned models trained toward a single behavioral mode.

By Mahmoud Srewa, Praneetsai Iddamsetty, Mohammad Abdullah Al Faruque, Salma Elmalaki

llms rag agents fine-tuning multimodal benchmarks

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.

By Joyjit Roy, Samaresh Kumar Singh, Sushanta Das

agents safety

arXiv Machine Learning

Jun 4

An Open-Source Two-Stage Computer Vision Pipeline for Fine-Grained Vehicle Classification using Vision Transformers

arXiv:2606. 05149v1 Announce Type: cross Abstract: Vehicle body type is a significant determinant of cyclist injury severity in overtaking crashes, yet automated tools for classifying vehicles into injury-risk-relevant categories from naturalistic roadway video do not exist in the open literature.

By Gandhimathi Padmanaban, Fred Feng

llms computer-vision fine-tuning benchmarks safety

arXiv AI

Jul 21

What Do They See? Interpreting Complex Road Scenarios Through the Eyes of Vision-Language-Action Models for Safe and Trustworthy Autonomous Vehicle Learning

arXiv:2607. 16938v1 Announce Type: cross Abstract: End-to-end autonomous driving models are now able to navigate complex road scenarios, mapping raw sensor observations directly to observed paths for open-loop evaluation and often effective driving in closed-loop evaluation.

By Kalpana Panda, Wesley Maia, Vinti Agarwal, Ross Greer

agents robotics multimodal benchmarks safety

arXiv Machine Learning

Aug 4

Asleep at the Wheel: JEPA's Limitations in Evaluating Novel Driving Data

arXiv:2608. 01336v1 Announce Type: cross Abstract: Modern autonomous-driving fleets record far more video than human reviewers can inspect.

By Advait Pavuluri, Shamik Karkhanis, Uzma Mushtaque

rag agents fine-tuning benchmarks

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