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.
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.
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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 Computer Vision
Aug 31
iOSPointMapper: RealTime Pedestrian and Accessibility Mapping with Mobile AI
iOSPointMapper is a mobile app that performs real‑time, privacy‑conscious sidewalk mapping using on‑device semantic segmentation, LiDAR depth estimation, and fused GPS/IMU data on recent iPhones and iPads. It detects and localizes sidewalk‑relevant features such as traffic signs, traffic lights, and poles, and includes a user‑guided annotation interface for validating outputs before submission. The anonymized data is transmitted to the Transportation Data Exchange Initiative (TDEI), where it integrates with broader multimodal transportation datasets, and evaluations show the app’s potential for enhanced pedestrian mapping.
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 16
Is Your Trajectory Displacement Safe in Long-tail?
arXiv:2606. 16313v1 Announce Type: cross Abstract: Long-tail scenarios remain a major bottleneck for autonomous driving evaluation, even as datasets grow by orders of magnitude.
arXiv AI
Jul 28
OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows
arXiv:2510. 24411v3 Announce Type: replace Abstract: Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobile platforms.
arXiv AI
Jun 2
DeepIPCv3: Event-Aware Multi-Modal Sensor Fusion for Sudden Pedestrian Crossing Avoidance
arXiv:2606. 01277v1 Announce Type: cross Abstract: Current end-to-end autonomous driving systems predominantly rely on frame-based sensors, which suffer from inherent perception latency and motion blur during highly dynamic encounters, specifically sudden pedestrian crossings.
arXiv Machine Learning
Sep 3
PRISM: An Agentic Multi-Model Architecture for Proactive Safety in Autonomous Transportation Systems
PRISM (Proactive Risk Intelligence and Safety Management) is an agentic multi-model architecture designed to shift autonomous transportation safety from reactive crash avoidance to proactive, continuous risk management. It uses inverse crash‑probability modeling to transform binary crash classifiers into dynamic safety scores, and runs three specialized models—trajectory kinematics, environmental risk, and VRU interaction—coordinated by a reinforcement‑learning reasoning layer. Across 1,296 naturalistic driving scenarios, PRISM achieved a mean safety score of 68/100, classified 77.6% of situations as advisory, and flagged 3.8% as near‑misses, with 11% requiring intervention or emergency response, highlighting trajectory risk and VRU proximity as key safety factors.
arXiv AI
Jul 7
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 AI
Jun 30
When Stopping Fails: Rethinking Minimal Risk Conditions through Human-Interactive Autonomous Driving for Safe Transportation Systems
arXiv:2606. 29115v1 Announce Type: cross Abstract: Autonomous vehicles (AVs) are increasingly deployed in urban environments, yet their safety frameworks remain primarily designed around collision avoidance and minimal risk condition (MRC) behaviors such as slowing or stopping when uncertainty arises.
arXiv AI
3d ago
TACTIC: Temporal and Context-Aware LLM Tactical Planning for Roadside LiDAR Attacks
arXiv:2609.39969v1 Announce Type: cross Abstract: Physical LiDAR attacks are often evaluated using fixed primitives and manually selected parameters, despite their strong dependence on surrounding tr...
arXiv Machine Learning
Sep 17
Can VLMs Reliably Assess Sidewalk Accessibility Attributes from Pedestrian-Level Imagery?
The study evaluates whether vision‑language models (VLMs) can reliably assess sidewalk accessibility attributes—effective width, longitudinal slope, cross slope, and pavement condition—from pedestrian‑level images. Using sampling‑based conformal prediction on 514 images from Seoul, the authors find that calibrated models achieve nominal 90% coverage, but only effective width yields informative estimates; other attributes remain too uncertain for compliance assessment. The work also demonstrates that raw sampling dispersion is not a trustworthy uncertainty measure without calibration and releases annotated images with ground‑truth measurements.