arXiv Machine Learning August 7, 2026 By Ahmed Hossain, Xiaoduan Sun

Assessing the Role of Intersection Proximity in Pedestrian Crashes: Insights from Data Mining Approach

Read the original on arXiv Machine Learning →

arXiv:2604. 28065v2 Announce Type: replace-cross Abstract: Although intersections are the most complex parts of the roadway network, pedestrian crashes at non-intersection locations are disproportionately frequent, highlighting a serious traffic safety concern.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

safety

arXiv AI

Jun 12

Improving Crash Frequency Prediction from Simulated Traffic Conflicts Using Machine Learning Based Microsimulation

arXiv:2606. 12500v1 Announce Type: cross Abstract: Traffic microsimulation combined with surrogate safety measures has increasingly been used as a proactive alternative to historical crash data for predicting crash frequency for current or planned road infrastructure designs.

By Xian Liu, Carlo G. Prato, Gustav Markkula

benchmarks safety

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

Jul 24

HERMES: Heterogeneous Edge-Relational Multi-Head Embedded SSM Attention for Traffic Conflict Prediction at Signalized Intersections

arXiv:2607. 20505v1 Announce Type: cross Abstract: Surrogate safety measures (SSMs) enable proactive traffic safety assessment, but many existing methods evaluate pairwise interactions independently or flatten multi-agent scenes into fixed feature vectors, limiting their ability to represent heterogeneous interaction structure and evolving scene-level risk.

By Md Monzurul Islam, Subasish Das

llms agents safety

arXiv Machine Learning

Jul 14

Comparison-Based Ordinal Learning for Proactive Driving Risk Assessment

arXiv:2607. 11128v1 Announce Type: cross Abstract: Real-time driving risk assessment provides an essential basis for proactive safety by identifying and quantifying the danger of ongoing road interactions before adverse outcomes occur.

By Zhuoren Li, Yi Zhong, Weiqi Zhang, Xinrui Zhang, Lu Xiong, Chongfeng Wei, Bo Leng

safety

arXiv AI

Aug 3

Benchmarking Frontier Large Language Models Against Official Crash Database Coding Using Police Crash Narratives

arXiv:2607. 29064v1 Announce Type: cross Abstract: Police crash narratives contain information that may supplement structured crash databases, but manual review is labor-intensive and it remains unclear how well large language models (LLMs) reproduce official crash coding.

By Sudhir Bharati, Rajendra K C Khatri, Sudip Bharati

llms benchmarks

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.

By Xinyi Ning, Zilin Bian, Dachuan Zuo, Semiha Ergan, Kaan Ozbay

agents robotics safety

Related stories

Improving Crash Frequency Prediction from Simulated Traffic Conflicts Using Machine Learning Based Microsimulation

Real-Time Driver Safety Scoring Through Inverse Crash Probability Modeling

HERMES: Heterogeneous Edge-Relational Multi-Head Embedded SSM Attention for Traffic Conflict Prediction at Signalized Intersections

Comparison-Based Ordinal Learning for Proactive Driving Risk Assessment

Benchmarking Frontier Large Language Models Against Official Crash Database Coding Using Police Crash Narratives

From Cues to Horizons: Dynamic Risk Horizon Profiling for Trajectory Prediction