Enhancing Safety in Automated Ports: A Virtual Reality Study of Pedestrian-Autonomous Vehicle Interactions under Time Pressure, Visual Constraints, and Varying Vehicle Size
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911357040852992 |
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| author | Che, Yuan Wong, Mun On Gao, Xiaowei Liang, Haoyang Ye, Yun |
| author_facet | Che, Yuan Wong, Mun On Gao, Xiaowei Liang, Haoyang Ye, Yun |
| contents | Autonomous driving improves traffic efficiency but presents safety challenges in complex port environments. This study investigates how environmental factors, traffic factors, and pedestrian characteristics influence interaction safety between autonomous vehicles and pedestrians in ports. Using virtual reality (VR) simulations of typical port scenarios, 33 participants completed pedestrian crossing tasks under varying visibility, vehicle sizes, and time pressure conditions. Results indicate that low-visibility conditions, partial occlusions and larger vehicle sizes significantly increase perceived risk, prompting pedestrians to wait longer and accept larger gaps. Specifically, pedestrians tended to accept larger gaps and waited longer when interacting with large autonomous truck platoons, reflecting heightened caution due to their perceived threat. However, local obstructions also reduce post-encroachment time, compressing safety margins. Individual attributes such as age, gender, and driving experience further shape decision-making, while time pressure undermines compensatory behaviors and increases risk. Based on these findings, safety strategies are proposed, including installing wide-angle cameras at multiple viewpoints, enabling real-time vehicle-infrastructure communication, enhancing port lighting and signage, and strengthening pedestrian safety training. This study offers practical recommendations for improving the safety and deployment of vision-based autonomous systems in port settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03218 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Enhancing Safety in Automated Ports: A Virtual Reality Study of Pedestrian-Autonomous Vehicle Interactions under Time Pressure, Visual Constraints, and Varying Vehicle Size Che, Yuan Wong, Mun On Gao, Xiaowei Liang, Haoyang Ye, Yun Human-Computer Interaction Autonomous driving improves traffic efficiency but presents safety challenges in complex port environments. This study investigates how environmental factors, traffic factors, and pedestrian characteristics influence interaction safety between autonomous vehicles and pedestrians in ports. Using virtual reality (VR) simulations of typical port scenarios, 33 participants completed pedestrian crossing tasks under varying visibility, vehicle sizes, and time pressure conditions. Results indicate that low-visibility conditions, partial occlusions and larger vehicle sizes significantly increase perceived risk, prompting pedestrians to wait longer and accept larger gaps. Specifically, pedestrians tended to accept larger gaps and waited longer when interacting with large autonomous truck platoons, reflecting heightened caution due to their perceived threat. However, local obstructions also reduce post-encroachment time, compressing safety margins. Individual attributes such as age, gender, and driving experience further shape decision-making, while time pressure undermines compensatory behaviors and increases risk. Based on these findings, safety strategies are proposed, including installing wide-angle cameras at multiple viewpoints, enabling real-time vehicle-infrastructure communication, enhancing port lighting and signage, and strengthening pedestrian safety training. This study offers practical recommendations for improving the safety and deployment of vision-based autonomous systems in port settings. |
| title | Enhancing Safety in Automated Ports: A Virtual Reality Study of Pedestrian-Autonomous Vehicle Interactions under Time Pressure, Visual Constraints, and Varying Vehicle Size |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2601.03218 |