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.
Edge-Assisted Crowdsensing for Explainable Cyclist Safety Perception
The paper introduces an edge‑assisted crowdsensing framework that uses handlebar‑mounted cameras to capture street‑level video, a vision‑language model to extract structured traffic attributes, and an explainable boosting machine to combine these with OpenStreetMap data and on‑bike kinematics for a 1‑9 safety score. On 1,362 annotated frames from two London routes, the model achieves 72.2 % binary accuracy and a mean absolute error of 1.011, with feature attribution revealing the impact of lane separation, painted lanes, and construction sites on safety ratings. The system operates efficiently at the network edge, requiring only 2.49 s per frame for extraction and 0.4 ms for scoring, and supports continuous monitoring at a cost of 1.4 accuracy points for 1,000 riders over a 20‑second interval.
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arXiv AI
Jul 28
From Camera-Based Sensing to Reasoning: A Comprehensive Review Toward Proactive Vulnerable Road User Safety
arXiv:2510. 03314v2 Announce Type: replace-cross Abstract: Ensuring the safety of vulnerable road users (VRUs), such as pedestrians and cyclists, remains a critical challenge, as conventional infrastructure-based measures are often insufficient in dynamic urban environments.
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.
arXiv Machine Learning
Sep 14
Estimating Pedestrian Volumes from GIS-Derived Built-Environment Features: A Machine Learning Framework
The paper introduces a machine learning pipeline that predicts 2‑hour peak pedestrian volumes at 101 intersections in Portland, Oregon, using built‑environment, land‑use, and street‑network features from open GIS data. Starting from a Negative Binomial GLM, the authors add feature selection, count‑aware gradient boosting, and repeated cross‑validation, ultimately selecting a histogram‑based gradient boosting model with Poisson loss and L1 Lasso feature selection. This model reduces cross‑validated RMSE by 12% and holdout RMSE by 19% compared to the GLM baseline.
arXiv Machine Learning
Jul 24
Safety-oriented sidewalk and road segmentation for smartphone-based assistive navigation
arXiv:2607. 21137v1 Announce Type: cross Abstract: Independent sidewalk mobility is essential for blind and visually impaired pedestrians (BVIPs), yet smartphone-based assistive navigation requires perception models that distinguish walkable sidewalks from adjacent unsafe regions.
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.
arXiv AI
Jun 10
TaCarla: A comprehensive benchmarking dataset for end-to-end autonomous driving
arXiv:2602. 23499v4 Announce Type: replace-cross Abstract: Collecting a high-quality dataset is a critical task that demands meticulous attention to detail, as overlooking certain aspects can render the entire dataset unusable.
arXiv Computer Vision
Aug 21
CAViAR: A Causal Video Dataset for Fine-Grained Accident Reasoning in Real-World Scenarios
arXiv:2608. 19380v1 Announce Type: new Abstract: While modern autonomous driving systems excel at perception tasks such as object detection and trajectory prediction, they lack the high-level causal reasoning required to interpret traffic accidents.
arXiv Computer Vision
2d ago
MotoVerse: A Benchmark for Predictive Perception of Motorized Two-Wheelers in Dense Unstructured Traffic
arXiv:2611.06007v1 Announce Type: new Abstract: Motorized Two-Wheelers (MTWs) dominate personal transport across much of the Global South, yet remain largely overlooked in predictive perception resea...
arXiv AI
2d ago
CoSafe: Cooperative Runtime Safety Assurance for Mixed-Traffic Autonomous Driving
CoSafe is a modular, ADS-agnostic framework that extends runtime safety assurance from ego-centric takeover to scene-level cooperative intervention in mixed-traffic autonomous driving. It uses vehicle-to-everything communication to allow roadside units to monitor hazards and jointly mitigate risks involving multiple vehicles. Evaluation on the InterDrive benchmark shows that CoSafe improves driving scores by 70.03%, reduces safety violations by 87.80%, and accurately identifies unsafe autonomous vehicles with an F3 score of 0.930 while meeting real-time constraints.
arXiv Computer Vision
Oct 26
SAGE: A hybrid vision-language and geometry framework for work-zone pathway prediction under temporary traffic control
arXiv:2610.31835v1 Announce Type: new Abstract: Temporary traffic control redirects vehicles through work zones, requiring automated vehicles to connect an interpretation of the visible devices to a...
arXiv AI
Oct 2
Towards Reliable Vision-Language Models for Autonomous Driving
The paper evaluates five vision‑language models on autonomous driving tasks under various visual input conditions, finding that visual corruption affects accuracy and confidence differently across models and datasets. It then tests Visual Evidence Augmentation (VEA) as an inference‑time technique to enhance reliability, observing mixed improvements depending on the model and setting.