Assessment of Latent Pedestrian--Vehicle Interaction Risk Profiles at Midblock Crossing in VR
- URL: http://arxiv.org/abs/2603.16688v1
- Date: Tue, 17 Mar 2026 15:45:42 GMT
- Title: Assessment of Latent Pedestrian--Vehicle Interaction Risk Profiles at Midblock Crossing in VR
- Abstract summary: This study investigates whether virtual reality (VR) crossing sessions separate into distinct interaction risk profiles.<n>Key findings show that Newcastle exhibits a higher prevalence of high-urgency risk profiles in AV-only sessions, indicating that AVs contribute to higher-risk encounters.<n>In contrast, Toronto shows no significant difference between AV-only and HDV-only sessions, suggesting that contextual factors influence the impact of AVs on pedestrian safety.
- Score: 1.624926714741352
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Pedestrian safety at midblock crossings is a critical concern in mixed traffic environments where autonomous vehicles (AVs) and human-driven vehicles (HDVs) share the road. Pedestrians often infer intent from vehicle motion in AV encounters, making them vulnerable to small shifts in conflict margins. This study investigates whether virtual reality (VR) crossing sessions separate into distinct interaction risk profiles and whether AV-only sessions shift profile prevalence compared to HDV-only sessions. Using large-scale immersive VR experiments from Toronto, Canada, and Newcastle, England, we compute surrogate safety measures (SSMs) and apply latent profile analysis (LPA) to identify distinct pedestrian crossing stances, ranging from risk-accepting to highly cautious. Key findings show that Newcastle exhibits a higher prevalence of high-urgency risk profiles in AV-only sessions, indicating that AVs contribute to higher-risk encounters. In contrast, Toronto shows no significant difference between AV-only and HDV-only sessions, suggesting that contextual factors influence the impact of AVs on pedestrian safety.
Related papers
- Mask What Matters: Saliency-Guided Video Self-Supervised Learning for Autonomous Driving [57.56568641990586]
We introduce V-JEPA4A, a domain-specialized variant of V-JEPA for autonomous driving that is pre-trained on publicly available driving videos with a novel saliency-driven masking policy.<n>V-JEPA4A reduces identity switches on BDD100k MOT by 25% over V-JEPA with random masking, achieves 73.2 mIoU on City, and 3.75 RMSE on KITTI-2015 depth, while incurring only 14% additional pre-training overhead.
arXiv Detail & Related papers (2026-08-17T22:39:49Z) - Modeling Vehicle-Type-Specific Pedestrian Crash Avoidance Behavior in Safety-Critical Interactions Using Smooth-Mamba Deep Reinforcement Learning [6.169445656675287]
This study extracts safety-critical pedestrian-vehicle interactions from the Argoverse 2 dataset.<n>To model vehicle-type-specific pedestrian crash avoidance behavior, we develop a Smooth-Mamba Deep Deterministic Policy Gradient framework.<n>Reconstructed trajectories demonstrate strong behavioral realism, accurately reproducing crash avoidance kinematics in both AV and HDV scenarios.
arXiv Detail & Related papers (2026-05-27T14:44:10Z) - Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving [65.61999354218628]
We take the first step toward designing black-box adversarial attacks specifically targeting vision-language models (VLMs) in autonomous driving systems.<n>We propose Cascading Adversarial Disruption (CAD), which targets low-level reasoning breakdown by generating and injecting semantics.<n>We present Risky Scene Induction, which addresses dynamic adaptation by leveraging a surrogate VLM to understand and construct high-level risky scenarios.
arXiv Detail & Related papers (2025-01-23T11:10:02Z) - Automated Vehicles at Unsignalized Intersections: Safety and Efficiency Implications of Mixed Human and Automated Traffic [10.095348373137952]
The integration of automated vehicles (AVs) into transportation systems presents an unprecedented opportunity to enhance road safety and efficiency.<n>This study aims to bridge the gap by examining behavioral differences and adaptations of AVs and human-driven vehicles (HVs) at unsignalized intersections.<n>The findings reveal a paradox: while AVs maintain larger safety margins, their conservative behavior can lead to unexpected situations for human drivers.
arXiv Detail & Related papers (2024-10-16T13:19:32Z) - Development and Assessment of Autonomous Vehicles in Both Fully
Automated and Mixed Traffic Conditions [0.0]
The paper presents a multi-stage approach, starting with the development of a single AV and progressing to connected AVs.
A survey is conducted to validate the driving performance of the AV and will be utilized for a mixed traffic case study.
Results show that using deep reinforcement learning, the AV acquired driving behavior that reached human driving performance.
The adoption of sharing and caring based V2V communication within AV networks enhances their driving behavior, aids in more effective action planning, and promotes collaborative behavior amongst the AVs.
arXiv Detail & Related papers (2023-12-08T02:40:11Z) - A Counterfactual Safety Margin Perspective on the Scoring of Autonomous
Vehicles' Riskiness [52.27309191283943]
This paper presents a data-driven framework for assessing the risk of different AVs' behaviors.
We propose the notion of counterfactual safety margin, which represents the minimum deviation from nominal behavior that could cause a collision.
arXiv Detail & Related papers (2023-08-02T09:48:08Z) - The risk ethics of autonomous vehicles: a continuous trolley problem in
regular road traffic [0.0]
We argue that autonomous vehicles (AVs) distribute risks between road users in regular traffic situations, either explicitly or implicitly.
Using an interactive, graphical representation of different traffic situations, we measured participants' preferences on driving maneuvers of AVs.
Our participants were willing to take risks themselves for the benefit of other road users suggesting that the social dilemma of AVs may lessen in a context of risk.
arXiv Detail & Related papers (2022-05-24T20:49:28Z) - Adversarial Visual Robustness by Causal Intervention [56.766342028800445]
Adversarial training is the de facto most promising defense against adversarial examples.
Yet, its passive nature inevitably prevents it from being immune to unknown attackers.
We provide a causal viewpoint of adversarial vulnerability: the cause is the confounder ubiquitously existing in learning.
arXiv Detail & Related papers (2021-06-17T14:23:54Z) - Voting for the right answer: Adversarial defense for speaker
verification [79.10523688806852]
ASV is under the radar of adversarial attacks, which are similar to their original counterparts from human's perception.
We propose the idea of "voting for the right answer" to prevent risky decisions of ASV in blind spot areas.
Experimental results show that our proposed method improves the robustness against both the limited-knowledge attackers.
arXiv Detail & Related papers (2021-06-15T04:05:28Z) - Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting [91.69900691029908]
We advocate for predicting both the individual motions as well as the scene occupancy map.
We propose a Scene-Actor Graph Neural Network (SA-GNN) which preserves the relative spatial information of pedestrians.
On two large-scale real-world datasets, we showcase that our scene-occupancy predictions are more accurate and better calibrated than those from state-of-the-art motion forecasting methods.
arXiv Detail & Related papers (2021-01-07T06:08:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.