In-vehicle stereo vision system for identification of traffic conflicts between bus and pedestrian

The traffic conflict technique （TCT） was developed as ＂surrogate measure of road safety＂ to identify near-crash events by using measures of the spatial and temporal proximity of road users. Traditionally applications of TCT focus on a specific site by the way of manually or automated supervision. Nowadays the development of in-vehicle （IV） technologies pro- vides new opportunities for monitoring driver behavior and interaction with other road users directly into the traffic stream. In the paper a stereo vision and GPS system for traffic conflict investigation is presented for detecting conflicts between vehicle and pedestrian. The system is able to acquire geo-referenced sequences of stereo frames that are used to provide real time information related to conflict occurrence and severity. As case study, an urban bus was equipped with a prototype of the system and a trial in the city of Catania （Italy） was carried out analyzing conflicts with pedestrian crossing in front of the bus. Experimental results pointed out the potentialities of the system for collection of data that can be used to get suitable traffic conflict measures. Specifically, a risk index of the conflict between pedestrians and vehicles is proposed to classify collision probability and severity using data collected by the system. This information may be used to develop in-vehicle warning systems and urban network risk assessment.

平面信号控制交叉口有轨电车与机动车交通冲突风险评价模型

为改善平面信号控制交叉口有轨电车运营安全水平,基于交通冲突理论,建立考虑交通流状态条件下有轨电车与机动车转弯发生交叉冲突的概率公式,并以有轨电车和机动车的运行速度、速度差、减速度、间距等物理参数提出交叉冲突严重程度表征指标,建立交通冲突风险评价模型;基于上海市松江区有轨电车T1线某平面交叉口进行案例分析。结果表明:调整信号相位可一定程度改善交叉冲突发生的可能性;有轨电车与机动车行车轨迹形成的矩形区域可作为交叉冲突风险分析区域,以轨迹疏密程度划分为3个子区域,在最容易发生冲突的风险区域I可采取路面限速、特殊标线、反光道钉、贴地警示灯等设施降低交通冲突严重程度。研究结果可为有轨电车通行路口交通安全设计方法提供理论模型支撑。

基于交通冲突的交织区安全评价方法研究

基于事故数据进行安全评价存在局限性,以交通冲突为基础进行交织区不同角度的安全方法评价研究。考虑冲突类型及严重程度将交通冲突综合风险指数作为整体评价指标,运用灰色聚类评价法进行整体评价等级确定,以冲突概率及变道风险为评价指标对交织区行驶区域进行安全分析。结果表明交织区内各区域存在明显的安全区分,从而实现对交织区的安全评价。

融合时空指标的交叉口机非碰撞风险评价模型

为研究交叉口机动车与非机动车交通安全问题,提出交叉口机非碰撞风险评价模型。首先,基于YOLOX+DeepSORT多目标检测与追踪算法,提取无人机视频中的机动车与非机动车轨迹数据,并获取交通参数。其次,基于机非碰撞风险分析,从空间维度提出评价指标最小接近距离及机动车与非机动车的相位角,从时间维度提出评价指标到达最小接近距离的时间。最后,融合时空指标提出机非碰撞风险评价模型。选取南京市三个交叉口进行实例分析,并利用冲突时间(Time to Collision,TTC)验证CRA模型。结果表明:YOLOX+DeepSORT算法检测机动车和非机动车的准确率分别为93.5%、89.9%。CRA模型能够合理量化机非碰撞风险,用于评价交叉口安全水平是有效且可靠的。

城乡结合部行人-机动车冲突及安全性分析

为了分析我国城乡结合部的行人过街安全情况,使用交通冲突和行人安全性指标对城乡结合部行人过街进行了探索。对行人和机动车冲突的特点进行了分析,并介绍了行人和机动车冲突的形成过程。通过现场调查视频和人工提取,获取相关参数,计算得到了行人和机动车冲突的距碰撞发生的时间和后侵占时间指标,并使用行人安全性指标对行人过街的安全性展开了分析,结果表明,所调查的地点均处于不安全的状况,设施设置越齐全,车流量越少,行人的安全性越高。