Autonomous driving paper index
Road user risk perception under adverse weather conditions: A survey-based statistical assessment from an urban mixed-traffic environment in Northeast India
One-line summary
Adverse weather substantially increases crash risk in urban mixed-traffic environments, yet road users' perceptions remain insufficiently understood in developing countries.
Engineering notes
Key topics: autonomous driving, perception. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Adverse weather substantially increases crash risk in urban mixed-traffic environments, yet road users' perceptions remain insufficiently understood in developing countries. This study presents a survey-based statistical assessment of perceived crash risk across multiple adverse weather conditions among different road user groups in Silchar, a Tier-3 city in Northeast India. A questionnaire survey was conducted with 237 respondents representing six road user groups. One-way ANOVA, Waller-Duncan post-hoc testing, Relative Risk Index estimation, and Ordered Logit Modelling were employed to compare perceived crash risk across eight weather conditions and identify key determinants. Results show significant variation in perceived risk across weather categories. Flooded streets and heavy rainfall were rated as the most hazardous conditions, with perceived relative risks nearly twice that of low rainfall. The ordered logit model indicates that two-wheeler users, respondents with prior near-miss experience, and respondents who support digital warning systems report higher perceived risk.
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