Sometimes right, sometimes wrong: Drivers' responses to inconsistently accurate automated vehicle system confidence information [0.03%]
时对时错：驾驶员对自动化车辆系统信心信息的不一致准确性的响应
Myeongkyu Lee,Brandon J Pitts
Myeongkyu Lee
Automated vehicles (AVs) are becoming increasingly equipped with intelligent functions that support drivers' decision-making. Human-machine interfaces (HMIs) that communicate an AV's confidence in its ability to navigate challenges in the d...
Proactive safety at CVIS-enabled intersections: a framework based on high-fidelity trajectory reconstruction and dynamic risk assessment [0.03%]
基于高保真轨迹重建和动态风险评估的CVIS交叉口主动安全框架
Yunxuan Li,Shihao Wang,Lishengsa Yue et al.
Yunxuan Li et al.
High-fidelity vehicle trajectories are critical for proactive safety at intersections, where traditional reconstruction methods based on linear models or interpolation fail to capture complex nonlinear dynamics like sudden stops and sharp t...
Dynamic dilemma zone at signalized intersection: attention allocation patterns using cure survival analysis for male riders [0.03%]
信号交叉口动态危险区域：使用CURE生存分析的男性骑行者注意力分配模式
Monik Gupta,Nagendra R Velaga
Monik Gupta
The design of the signal at the intersection considers the constant speed of the riders and the dilemma zone to be static. However, these assumptions may not hold true in complex environments with multiple users. This study explores the dyn...
Safety-oriented facility design and operation management for transportation hub station [0.03%]
以安全为导向的交通枢纽站设施设计与运营管理
Yixin Shen,Hongfei Jia,Xin Ye et al.
Yixin Shen et al.
Given the high efficiency and punctuality, transportation hub station are widely used by citizens, travelers daily. The large volume of passengers tends to cause overcrowding in transportation hub stations. Therefore, passenger movement eff...
Beyond the norm: Identifying rare and high-risk pedestrian crash patterns using unsupervised learning [0.03%]
超越常规：使用无监督学习识别罕见和高风险的行人碰撞模式
Zeinab Bayati,Asad J Khattak
Zeinab Bayati
Pedestrian safety remains a major concern, with fatalities rising despite infrastructure and safety improvements. To make meaningful progress, efforts should focus more intensely on reducing the most dangerous and fatal cases, given the gro...
Xiaolu Jia,Claudio Feliciani,Hisashi Murakami et al.
Xiaolu Jia et al.
In transportation hubs, pedestrian flows form complex network structures, leading to serious congestion at peak hours. Understanding their dynamics is crucial to managing safe and efficient transportation. Although many experimental and the...
A graph-based spatio-temporal framework for predicting safety-critical pedestrian-vehicle interactions at unsignalized crosswalks [0.03%]
一种基于图的时空框架预测无信号控制人行横道上行人与车辆关键安全交互事件
Kaliprasana Muduli,Indrajit Ghosh,Satish V Ukkusuri
Kaliprasana Muduli
Pedestrian safety remains a critical global concern, especially in countries like India, where unsignalized crossings with limited traffic control contribute to high pedestrian fatality rates. This study proposes a novel graph-based framewo...
DrowsyDG-Phys: Generalizable driver drowsiness estimation in conditional automated vehicles using physiological signals [0.03%]
基于生理信号的条件自动驾驶中的泛化驾驶员困倦估计(DrowsyDG-Phys)
Jiyao Wang,Wenbo Li,Zhenyu Wang et al.
Jiyao Wang et al.
Driver drowsiness is one of the leading causes of crashes, injuries, and fatalities on the road. Traditional drowsiness detection models relied on manually extracted physiological features processed through machine learning algorithms. Howe...
Cooperative or competitive? Resolving social dilemmas in autonomous vehicles through evolutionary game theory [0.03%]
合作还是竞争？通过进化博弈理论解决自动驾驶车辆的社会困境问题
Rui Li,Yiru Liu,Jian Sun et al.
Rui Li et al.
With the large-scale deployment of autonomous vehicles (AVs), AV-human-driven vehicle (HV) interactions are increasingly common. AVs face a social dilemma: competitive behavior raises ethical and public acceptance concerns, whereas cooperat...
ROAR: Robust accident recognition and anticipation for autonomous driving [0.03%]
鲁棒的自主驾驶事故识别与预判(ROAR)
Xingcheng Liu,Yanchen Guan,Haicheng Liao et al.
Xingcheng Liu et al.
Accurate accident anticipation is essential for enhancing the safety of autonomous vehicles (AVs). However, existing methods often assume ideal conditions, overlooking challenges such as sensor failures, environmental disturbances, and data...