Analysis of conflict factors between pedestrians and right-turning vehicles at signalized intersections

Due to the fact that there is no protected signal phase for right turns at most signalized intersections, the conflict between pedestrians and right-turning vehicles is one of the most common conflict types for pedestrians. A pedestrian safety analysis of the common right-turn mode at four-phase signalized intersections is presented. Relative risk is used as a measure of the effect of behaviors. The analysis mainly includes five pedestrian factors that affect the conflict process between pedestrians and right-turning vehicles. Pedestrians tend to have a higher risk of being involved in conflicts in the following six situations: crossing with others, running over the crossing, entering the intersection, being near the exit lane, crossing in the middle or at the end of a green light when the right-turn lane is shared, crossing at the beginning of a green light or red period when the right-turn lane is exclusive. It is easier for pedestrians to get priority when crossing the street in the following situations: running over a crossing, entering the intersection, being near the entrance lane, and not using the crosswalk. However, pedestrians are more inclined to yield to right-turning vehicles when pedestrians are crossing in the middle of the green light time. Some measures to alleviate the conflict are put forward according to the conclusion. Video observations also indicate that a clear pedestrian waiting area must be marked for both pedestrian safety and right-turning vehicle efficiency at major flat intersections, particularly when the arms cover the lateral dividing strips.

Self-Perception and General Perception of the Safety Impact of Autonomous Vehicles on Pedestrians, Bicyclists, and People with Ambulatory Disability

For autonomous vehicles (AVs) to receive general acceptance, society must have a positive perception about their safety impact on vulnerable road users. Using data from a statewide random-digit-dialing telephone survey of 1001 adults, this paper examines how New Jersey residents perceive the safety impact of AVs on pedestrians, bicyclists, and people with ambulatory disability. It uses a combination of confirmatory factor analysis and ordered probit models. Confirmatory factor analysis is used to create latent variables on socioeconomic status and built environment. Three ordered probit models are used to examine people’s perception of AV safety impact on each of the three population groups. The models also examine how frequent walkers, bicyclists, and people with ambulatory disability perceive their own safety as well as the safety of the other two groups. All three models examine the effect of familiarity with AV, gender, age, income, education, race, ethnicity, number of vehicles in household, political party affiliation, as well as built environment and socioeconomic status of the municipalities where the survey respondents live. The analysis showed that men, people with familiarity with the AV concept, Democrats, bicyclists, and people with high household income generally have a positive perception about the safety impact of AVs. While frequent walkers are ambivalent about their own safety as pedestrians, bicyclists have a positive perception about their own safety and the safety of pedestrians, whereas people with ambulatory disability have a strong negative perception about their own safety. The models did not show statistically significant effects of socioeconomic status or built environment of municipalities on AV safety perception.

IR-YOLO: Real-Time Infrared Vehicle and Pedestrian Detection

Road traffic safety can decrease when drivers drive in a low-visibility environment.The application of visual perception technology to detect vehicles and pedestrians in infrared images proves to be an effective means of reducing the risk of accidents.To tackle the challenges posed by the low recognition accuracy and the substan-tial computational burden associated with current infrared pedestrian-vehicle detection methods,an infrared pedestrian-vehicle detection method A proposal is presented,based on an enhanced version of You Only Look Once version 5(YOLOv5).First,A head specifically designed for detecting small targets has been integrated into the model to make full use of shallow feature information to enhance the accuracy in detecting small targets.Second,the Focal Generalized Intersection over Union(GIoU)is employed as an alternative to the original loss function to address issues related to target overlap and category imbalance.Third,the distribution shift convolution optimization feature extraction operator is used to alleviate the computational burden of the model without significantly compromising detection accuracy.The test results of the improved algorithm show that its average accuracy(mAP)reaches 90.1%.Specifically,the Giga Floating Point Operations Per second(GFLOPs)of the improved algorithm is only 9.1.In contrast,the improved algorithms outperformed the other algorithms on similar GFLOPs,such as YOLOv6n(11.9),YOLOv8n(8.7),YOLOv7t(13.2)and YOLOv5s(16.0).The mAPs that are 4.4%,3%,3.5%,and 1.7%greater than those of these algorithms show that the improved algorithm achieves higher accuracy in target detection tasks under similar computational resource overhead.On the other hand,compared with other algorithms such as YOLOv8l(91.1%),YOLOv6l(89.5%),YOLOv7(90.8%),and YOLOv3(90.1%),the improved algorithm needs only 5.5%,2.3%,8.6%,and 2.3%,respectively,of the GFLOPs.The improved algorithm has shown significant advancements in balancing accuracy and computational efficiency,making it promising for practical use in resource-limited scenarios.

A Workable Solution for Reducing the Large Number of Vehicle and Pedestrian Accidents Occurring on a Yellow Light

Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada, during 2019, the National Collision Database shows that 28% of traffic fatalities and 42% of serious injuries occurred at intersections. Likewise, the U.S. National Highway Traffic Administration (NHTSA) found that about 40% of the estimated 5,811,000 accidents in the U.S. during the year studied were intersection-related crashes. In fact, a major survey by the car insurance industry found that nearly 85% of drivers could not identify the correct action to take when approaching a yellow traffic light at an intersection. One major reason for these accidents is the “yellow light dilemma,” the ambiguous situation where a driver should stop or proceed forward when unexpectedly faced with a yellow light. This situation is even further exacerbated by the tendency of aggressive drivers to inappropriately speed up on the yellow just to get through the traffic light. A survey of Canadian drivers conducted by the Traffic Injury Research Foundation found that 9% of drivers admitted to speeding up to get through a traffic light. Another reason for these accidents is the increased danger of making a left-hand turn on yellow. According to the National Highway Traffic Safety Association (NHTSA), left turns occur in approximately 22.2% of collisions—as opposed to just 1.2% for right turns. Moreover, a study by CNN found left turns are three times as likely to kill pedestrians than right turns. The reason left turns are so much more likely to cause an accident is because they take a driver against traffic and in the path of oncoming cars. Additionally, most of these left turns occur at the driver’s discretion—as opposed to the distressingly brief left-hand arrow at busy intersections. Drive Safe Now proposes a workable solution for reducing the number of accidents occurring during a yellow light at intersections. We believe this fairly simple solution will save lives, prevent injuries, reduce damage to public and private property, and decrease insurance costs.

Investigation and Optimization of Priority for Non-motor Vehicles in the Old Urban Area of Nanchang

Under the background of"people-oriented"thought and"green transportation",the idea of"priority for non-motor vehicles"came into being,which can improve the riding environment of non-motor vehicle riders to a certain extent.According to the current situation of priority for non-motor vehicles in the old urban area of Nanchang,through field investigation,questionnaire investigation and interview,this study summarized the existing problems,and put forward optimization suggestions for these problems,in order to provide reference for areas with similar conditions.

Designing Right-Turn Vehicle Box as a Supplemental Treatment to Eliminate Conflicts with Pedestrians and Bicycles

Field observations illustrated that, right-turn vehicles stopped at various positions when proceeding within the right-turn lanes, while some of them trespassed on the crosswalks with multiple stops. In this case, pedestrians and bikes (ped/bike) are encountered unsmooth and hazardous crossings when right-turn vehicles encroaching their lanes. Meanwhile, this also causes conflicts between right-turn and through vehicles at the crossing street. To better protect ped/bike at crossings with right-turn vehicles, this paper proposes a concept of “right-turn vehicle box” (RTVB) as a supplemental treatment within right-turn lanes. Sight distance, geometric conditions, and behaviors of vehicles and ped/bike are key factors to consider so as to set up the criteria and to design the suitable treatment. A case study was conducted at an intersection pair in Houston, USA to shape the idea of RTVB, together with driving simulator tests under relevant scenarios. The preliminary crosscheck examination shows that the right-turn vehicle box could possibly provide ped/ bike with smoother and safer crossings. In the interim, the safety and efficiency of right-turn operations were also improved. To further validate the effects, implementation studies should be conducted before the RTVB can make its debut in practice. Future works will focus on the complete warrants and design details of this treatment. Moreover, the concept of “vehicle box” could also be transplanted to other places where turning movement(s) needs assistance or improvements.

Connected and Autonomous Vehicles (CAVs) Challenges with Non-motorized Amenities Environments

With the deployment of Connected and Automated Vehicles in the coming decades,road transportation will experience a significant upheaval.CAVs(Connected and Autonomous Vehicles)have been a main emphasis of Transportation and the automotive sector,and the future of transportation system analysis is widely anticipated.The examination and future devel­opment of CAVs technology has been the subject of numerous researches.However,as three essential kinds of road users,pedestrians,bicyclists,and motorcyclists have experienced little to no handling.We explored the influ­ence of CAVs on non-motorized mobility in this article and seven various issues that CAVs face in the environment.

Pedestrian Facilities Capacity and Level of Service at Intersections in a Connected and Autonomous Vehicle Environment

It is inevitable that Connected and Autonomous Vehicles (CAVs) will be a major focus of transportation and the automotive industry with increased use in future traffic system analysis. Numerous studies have focused on the evaluation and potential development of CAVs technology;however, pedestrians and bicyclists, as two essential and important modes of the road users have seen little to no coverage. In response to the need for analyzing the impact of CAVs on non-motorized transportation, this paper develops a new model for the evaluation of the Level of Service (LOS) for pedestrians in a CAVs environment based on the Highway Capacity Manual (HCM). The HCM provides a methodology to assess the level of service for pedestrians and bicyclists on various types of intersections in urban areas. Five scenarios were created for simulation via VISSIM (a software) that corresponds to the different proportions of the CAVs and different signal systems in a typical traffic environment. Alternatively, the Surrogate Safety Assessment Model (SSAM) was selected for analyzing the safety performance of the five scenarios. Through computing and analyzing the results of simulation and SSAM, the latter portion of this paper focuses on the development of a new model for evaluating pedestrian LOS in urban areas which are based upon HCM standards which are suitable for CAVs environments. The results of this study are intended to inform the future efforts of engineers and/or policymakers and to provide them with a tool to conduct a comparison of capacity and LOS related to the impact of CAVs on pedestrians during the process of a transportation system transition to CAVs.

Pedestrian and Vehicle Detection Based on Pruning YOLOv4 with Cloud-Edge Collaboration

Nowadays,the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network,such as pedestrian and vehicle detection,to provide efficient intelligent services to mobile users.However,as the accuracy requirements continue to increase,the components of deep learning models for pedestrian and vehicle detection,such as YOLOv4,become more sophisticated and the computing resources required for model training are increasing dramatically,which in turn leads to significant challenges in achieving effective deployment on resource-constrained edge devices while ensuring the high accuracy performance.For addressing this challenge,a cloud-edge collaboration-based pedestrian and vehicle detection framework is proposed in this paper,which enables sufficient training of models by utilizing the abundant computing resources in the cloud,and then deploying the well-trained models on edge devices,thus reducing the computing resource requirements for model training on edge devices.Furthermore,to reduce the size of the model deployed on edge devices,an automatic pruning method combines the convolution layer and BN layer is proposed to compress the pedestrian and vehicle detection model size.Experimental results show that the framework proposed in this paper is able to deploy the pruned model on a real edge device,Jetson TX2,with 6.72 times higher FPS.Meanwhile,the channel pruning reduces the volume and the number of parameters to 96.77%for the model,and the computing amount is reduced to 81.37%.

Factors Influence Hanoians’ Choice of Non-Motorized Transport Mode to Access Bus Stations

Hanoi’s rapid urbanization has led to a surge in private vehicle ownership, particularly motorcycles, amidst inadequate public transportation infrastructure. Despite government efforts, many still prefer motorized transport, citing mobility and safety concerns, exacerbated by insufficient pedestrian infrastructure. This study examines the motivations behind this reliance on motorized vehicles, particularly motorcycles, in Hanoi. Findings reveal safety and convenience as primary factors driving motorized transport use, especially for accessing bus stations. Economic incentives could promote non-motorized travel and public transport adoption. Policy implications highlight the importance of addressing economic factors and improving access infrastructure to manage motorized vehicle reliance and foster sustainable urban mobility in Hanoi.

考虑行人-汽车-列车-桥耦合振动的大跨度公铁平层桥梁行人响应分析

随着城市桥梁不断并入多种交通形式,各种交通工具与行人之间相互干扰,对行人行走的安全与舒适均产生影响。为研究公铁平层桥梁上的行人响应,建立了行人-汽车-列车-桥梁耦合振动模型。对比分析了桥面板加速度与行人SMD(spring-mass-damp)模型的差异,计算了列车、汽车以不同的方式通过桥梁时的行人响应。结果表明,相较于桥面板加速度,行人SMD模型加速度的波动幅度更小,但行走的行人模型会出现突变的远大于波动区间的加速度峰值。行人-汽车-列车系统中各子系统均存在速度敏感区间,该区间内行人加速度响应显著增加。汽车与列车离桥梁中心线越远,引起的行人加速度响应越大。列车与汽车同时行驶通过桥梁时,二者引起的行人的加速度峰值会相互叠加,列车对汽车引起的行人加速度的峰值提升显著。

基于车载视觉传感器的多目标检测跟踪技术研究

汽车在现代社会生活中扮演着举足轻重的角色,其极大地便利人们的出行,但与此同时,也引发诸如环境污染和交通拥堵等社会问题。为应对这些挑战,国内外学者正积极投身于无人驾驶技术的研究。其中,车辆与行人检测技术是无人驾驶技术的关键环节,但鉴于车辆与行人特征的多样性,使用单一的特征提取和分类方法进行检测变得异常困难。然而,深度学习的目标检测方案凭借其复杂的神经网络结构,成功攻克这一难题,因此备受国内外学者的关注。该文旨在通过深入剖析基于深度学习的目标检测以及基于SORT的多目标跟踪原理,为车载视觉传感器的研发创新提供有益的参考和启示。

Hybrid cooperative intersection management for connected automated vehicles and pedestrians

Connected Automated Vehicles(CAVs)have drawn much attention in recent years.High reliable automatic technologies can help CAVs to follow given trajectories well.However,safety and efficiency are hard to be ensured since the interactions between CAVs and pedestrians are complex problems.Thus,this study focuses on cooperative intersection management for CAVs and pedestrians.To avoid the effects of uncertainty about pedestrian behaviors,an indirect way is to use pedestrians’signal lights to guide the movements of pedestrians,and such lights with communication devices can share information with CAVs to make decisions together.In time domains,a general conflict-free rule is established depending on the positions of CAVs and crosswalks.Geometric analysis with coordinate calculation is used to accurately determine the feasible vehicle trajectories and the reasonable periods for signal lights turning green.Four control strategies for the same conditions are compared in simulation experiments,and their performances are analyzed.We demonstrate that the proposed cooperative strategy not only balances the benefits of vehicles and pedestrians but also improves the traffic efficiency at the intersection.

Multi-Dimensional Pedestrian System on the New Campus of University of Macao

The Multi-dimensional Pedestrian System( MPS) is an integral part of the new campus of University of Macao. It observes the principle of "pedestrian first " and features a pedestrian-vehicle dividing system.Based on a comprehensive analysis of the location,climate,ecology and other factors of the project site,the conception of the idea of MPS and the related researches are illustrated. The transportation features of the MPS,as summarized,include multi-dimensions,short-distance and weather-resistance. Its features for the sake of livability include integration of nature, respect for the environment and sharing of landscape. Upon the completion of the project, the effects on its users were tested. Finally, some constructive rules for the construction of similar campus pedestrian systems were proposed.

Delays for Both Pedestrians Classified and Vehicles at a Signalized Crosswalk

Delays of both pedestrians,who are classified according to whether complying with traffic law,and vehicles at a signalized crosswalk are analyzed in this paper.The truncated Adams' model is applied to generate the probability and mean of delay of pedestrians non-complying with traffic law.Using the section-based traffic queuing-theory and the stochastic decomposition property of M/G/1vacation system with exhaustive service,the mean delay of vehicles is formulated.A multi-objective optimization model simultaneously minimizing the delays of pedestrians and vehicles during a signal period is proposed.The effects,which several model parameters have on the delays and the optimal solution of the model,are illustrated by numerical examples.

行人过街模拟及车辆右转避障路径规划方法

为解决无信号十字路口右转车辆与同侧过街行人的交互冲突问题,提出一种模拟过街行为的行人过街运动模型,设计了车辆横纵向解耦避障路径规划算法,并进行了仿真实验。使车辆面向动、静态行人时能合理切换避障路径规划策略;同时,将过街运动模型驱动下的行人作为车辆避障对象,以过街模型输出的行人未来轨迹生成车辆纵向速度规划障碍位移—时间区域,从而让行人未来运动状态反馈到车辆避障中。结果表明:本文的行人过街运动模型相对观测值的准确率达到了90%,因此,该模型复现了行人过街过程;能根据行人运动状态切换避障方案,使车辆安全避让过街行人。

Simulation Evaluation of Filtering Method for Improving Pedestrian Positioning Accuracy Using Signal Strengths

In recent years, we have been able to use various services using the position information of smartphones and tablets. In addition, research on intelligent transport systems (ITS) has been actively conducted. To consider reducing traffic accidents by exchanging position information between pedestrians and vehicles by vehicle-to-pedestrian communication, we require accurate position information for pedestrians and vehicles. The GPS (global positioning system) is the most widely used method for acquiring position information. However, in urban areas, the GPS signal is affected by the surrounding buildings, which increases the positioning error. In this study, a method to improve the positioning accuracy of pedestrians using the signal strengths from vehicles and beacons was proposed. First, a Kalman filter was applied to the signal strength. Then, the path loss index was dynamically calculated using vehicle-to-vehicle communication. Finally, the position of a pedestrian was obtained using weighted centroid localization (WCL) after filtering the nodes. The positioning accuracy was evaluated using a simulator and demonstrated the superiority of the proposed method.

基于动作条件交互的高效行人过街意图预测

城市化的进程不断加速,人车冲突问题已成为现代社会亟待解决的重大难题。复杂交通场景下,行人横穿马路行为导致交通事故频发,准确、实时地预测行人过街意图对避免人车冲突、提高驾驶安全系数和保障行人安全至关重要。本文提出基于动作条件交互的高效行人过街意图预测框架(efficient action-conditioned interaction pedestrian crossing intention anticipation framework,EAIPF)来预测行人过街意图。EAIPF引入行人动作编码模块增强多模态动作模式下的表征能力,挖掘深层骨架上下文信息。同时,引入场景对象交互模块挖掘与对象交互信息,理解交通场景中高级语义线索。最后,意图预测模块融合行人动作特征和对象交互特征,实现行人过街意图的鲁棒预测。所提出的方法在两个公共数据集JAAD和PIE上验证算法性能,准确率分别达到了89%和90%,表明本文方法可以在复杂交通场景下准确预测行人穿越意图。

车人碰撞中人地接触损伤仿真及防护方法鲁棒性分析

人车事故中行人主要与车辆和地面接触而遭受损伤。此前已有大量研究在车辆接触阶段对多体行人模型进行了验证,但对地面接触阶段的验证较少。本文中评估了用于预测车辆撞击后行人运动和地面接触的4个PC-Crash行人模型。通过车辆和地面接触的HIC伤害与最近研究中的6个尸体实验数据对比显示,其中PC2014行人模型能很好地预测行人头部损伤,车辆和地面接触HIC的平均误差分别为6.07%和5.85%,数据说明PC2014行人模型可以用于再现人车碰撞事故以及开发未来地面接触伤害防护对策。控制制动防护方法鲁棒性研究显示,在3种行人姿态(奔跑、步行和应急)扰动下,HIC平均降低比例分别为63.2%、57.9%和67.8%,说明控制制动防护方法具有很好的抗行人姿态干扰能力。进一步分析不同姿态下的行人损伤发现,3种步态序列之间均有显著性差异,在应急步态下控制制动防护方法有着更好的抗行人姿态干扰能力;而在奔跑步态序列下,往往可以通过控制制动防护方法产生最低的头地碰撞损伤,表明在使用控制制动防护方法对行人进行保护时须考虑到不同姿态产生的影响,以进一步提高控制制动防护方法的地面伤防护效果。

基于多信息融合网络的行人轨迹预测方法

随着自动驾驶技术的不断发展,准确预测行人的未来轨迹已经成为确保系统安全和可靠的关键要素。然而,现有行人轨迹预测研究多数依赖于固定摄像头视角,进而限制了对行人运动的全面观测,因此难以直接应用于自动驾驶车辆自车视角(ego-vehicle)下的行人轨迹预测。针对该问题,本文提出了一种基于多行人信息融合网络(MPIFN)的自车视角行人轨迹预测方法。该方法通过融合社会信息、局部环境信息和行人时间信息,实现了对行人未来轨迹的准确预测。本文构建了一个局部环境信息提取模块,结合了可形变卷积与传统卷积和池化操作,旨在更有效地提取复杂环境中的局部信息。该模块通过动态调整卷积核的位置,增强了模型对不规则和复杂形状的适应能力。同时,构建了行人时空信息提取模块和多模态特征融合模块,以实现对社会信息与环境信息的充分融合。实验结果表明,该方法在JAAD和PSI两个自车视角下驾驶数据集上均取得了先进的性能。在JAAD数据集上,累积均方误差(CF_MSE)为4 063,累积平均均方误差(C_MSE)为829。在PSI数据集上平均相对偏差(ARB)和最终相对偏差(FRB)也分别在预测时间为0.5、1.0、1.5 s时取得了18.08、29.21、44.98和25.27、54.62、93.09的优异表现。