智能汽车的侵权责任研究

智能汽车是指借助人工智能、大数据、卫星导航等软件系统,结合传感器、摄像机、雷达等硬件系统组合而成的集感知、决策、控制等功能于一体的智能交通工具。根据《汽车驾驶自动化分级》(GB/T40429—2021)，智能汽车专指具备L2~L5级自动驾驶系统的汽车。智能汽车相较于传统汽车,在发生交通事故时,对责任的认定划分将不再单纯局限于驾驶员的操作,而应当综合考虑自动驾驶汽车产品的责任及提醒驾驶员注意的义务。因此,需要在完善现行《道路交通安全法》《产品质量法》的基础上,对因智能汽车侵权所承担的责任主体和责任范围进行分析,弥补智能汽车应承担相应责任的立法空白,确保技术发展与社会公共利益相结合。     

智能汽车的侵权责任研究

智能汽车是指借助人工智能、大数据、卫星导航等软件系统,结合传感器、摄像机、雷达等硬件系统组合而成的集感知、决策、控制等功能于一体的智能交通工具。根据《汽车驾驶自动化分级》(GB/T40429—2021),智能汽车专指具备L2~L5级自动驾驶系统的汽车。智能汽车相较于传统汽车,在发生交通事故时,对责任的认定划分将不再单纯局限于驾驶员的操作,而应当综合考虑自动驾驶汽车产品的责任及提醒驾驶员注意的义务。因此,需要在完善现行《道路交通安全法》《产品质量法》的基础上,对因智能汽车侵权所承担的责任主体和责任范围进行分析,弥补智能汽车应承担相应责任的立法空白,确保技术发展与社会公共利益相结合。     

L3级自动驾驶汽车致人损害的责任主体研究

为了明确现阶段L3级自动驾驶汽车致他人损害的责任主体,本文通过对L3级自动驾驶汽车致人损害责任主体的域外考察以及分别对系统、驾驶员、生产者是否应作为责任主体进行批判性分析,得出如果自动驾驶汽车在L3级自动驾驶模式开启时致人损害,由生产者承担无过错责任,如果驾驶员在不应当开启自动驾驶模式的情形下开启自动驾驶模式,造成他人损害由驾驶员负责,但是有证据证明汽车存在明显缺陷则由生产者负责的结论。     

自动驾驶汽车产品缺陷侵权责任研究

自动驾驶汽车的交通事故原因将由人类驾驶员操作失误逐渐向驾驶系统缺陷所致转变。根据自动驾驶汽车的技术特性,产品责任框架的构设有助于促进生产者提高产品安全质量,也有助于受害者的索赔和分散风险,目前我国没有统一的技术标准,产品责任面临如何认定自动驾驶汽车产品缺陷困境。本文通过分析自动驾驶汽车的产品责任和产品缺陷认定的法律困境,希望可以为产品缺陷责任法律框架提供可行路径。     

自动驾驶工况下汽车虚拟人人机交互探索

对于传统汽车,驾驶员在环控制操控模式下,驾驶员搜集信息并完全掌控车辆行为。随着汽车自动驾驶系统的不断演进, L3层级自动驾驶系统的不断普及,汽车驾驶操纵模式正在悄然发生着变化。可以预见随着L4-L5层级自动驾驶系统的量产销售,驾驶员将从车辆中彻底消失,人车交互的虚拟驾驶员与车辆之间如何完成交互互动呢?本文将综述目前ADAS系统支持下车辆驾驶操控情况,并对未来L4-L5层级自动驾驶模式下的人车交互系统进行探讨。     

新型汽车主动避撞安全距离模型

针对现有模型的不足，以驾驶员车间距保持目的假设为基础建立了一种新型汽车主动避撞安全距离模型，通过驾驶员试验获得了反映驾驶员驾驶特点的模型参数。经仿真及试验对比，该模型计算结果体现了驾驶员的驾驶特点，能够适用于多种交通状况，满足了汽车主动避撞系统的要求。     

安全行车注意事项

汽车作为一种交通工具,关系着社会的稳定与和谐,驾驶员养成良好的驾驶习惯,不仅仅是驾驶技术问题,更是一种社会责任。为此,驾驶员朋友应养成良好的驾驶习惯,采取正确措施,确保行车安全。     

汽车安全驾驶警告系统的研究

汽车安全驾驶警告系统能根据驾驶员的驾驶状态、驾驶时间和驾驶环境等因素，对驾驶员进行监控，确保行车安全。     

基于驾驶行为的汽车节油提醒系统的应用

本文主要提出一种汽车节油提醒的方法及系统的应用,通过实时监测驾驶员的驾驶行为并对驾驶员的驾驶行为进行判断,当驾驶员处于非节油驾驶状态时进行警示并给出驾驶建议,从而帮助驾驶员改善驾驶习惯,达到汽车节油降耗的目的。本文针对不同的驾驶阶段采取的判断逻辑和提醒措施进行阐述。     

汽车安全辅助驾驶支持系统信息感知技术综述

随着我国汽车保有量的激增,汽车驾驶安全日渐重要.围绕人-车-路系统中的人和路开发车载汽车安全驾驶支持系统受到广泛的关注.在智能运输系统的交通安全研究中,与汽车安全辅助驾驶系统相关的信息感知技术研究主要有:车辆状况检测、交通环境的检测、非常规条件下驾驶员视觉增强和对驾驶员的检测等.文中将对上述汽车安全辅助驾驶支持技术国内、外最新研究现状进行综述,并展望该领域研究动向.     

智能汽车的人机共驾技术研究现状和发展趋势

智能汽车的人机共驾技术（HMIoIV）是解决其智能化级别难以快速跨越至高度自动化水平的有效过渡手段。HMIoIV涉及了L0~L3级别的智能汽车的多种自动化技术,包括先进辅助驾驶系统。针对当前国内外智能汽车人机共驾技术的研究现状,对其概念、结构和研究内容进行总结,根据独立驾驶人参与的数量和驾驶操作方参与的数量将现有的人机共驾技术分成3类：单驾双控结构、串联型双驾单控结构（Traded Control）和并联型双驾双控结构（Shared Control）;并对驾驶人为因素、驾驶人模型、自然驾驶人状态监测和驾驶意图识别、串联型双驾单控结构和并联型双驾双控结构的研究方法以及权限与责任的关系进行全面综述。最后,分析总结当前智能汽车的人机共驾技术所面临的问题和挑战,并对该技术的发展趋势做出展望。     

Development of a Driving Behavior-Based Collision Warning System Using a Neural Network

An advanced driver assistance system (ADAS) uses radar, visual information, and laser sensors to calculate variables representing driving conditions, such as time-to-collision (TTC) and time headway (THW), and to determine collision risk using empirically set thresholds. However, the empirically set threshold can generate differences in performance that are detected by the driver. It is appropriate to quickly relay collision risk to drivers whose response speed to dangerous situations is relatively slow and who drive defensively. However, for drivers whose response speed is relatively fast and who drive actively, it may be better not to provide a warning if they are aware of the collision risk in advance, because giving collision warnings too frequently can lower the reliability of the warnings and cause dissatisfaction in the driver, or promote disregard. To solve this problem, this study proposes a collision warning system (CWS) based on an individual driver’s driving behavior. In particular, a driver behavior model was created using an artificial neural network learning algorithm so that the collision risk could be determined according to the driving characteristics of the driver. Finally, the driver behavior model was learned using actual vehicle driving data and the applicability of the proposed CWS was verified through simulation.     

In-vehicle display HMI safety evaluation using a driving simulator

Recently, telematics services and in-vehicle display devices such as the CNS (Car Navigation System) have become new causes of traffic accidents. These accidents are caused by ‘Inattention’ from the increase of the driver’s mental workload while he/she is driving. The driver of a vehicle (except for emergency or police vehicles) must not use a hand-held mobile phone while the vehicle is moving. To address this problem, Australia, England, Italy, Brazil and some states in the US have banned the use of hand-held mobile devices during driving. However, there are no restrictions on the use of in-vehicle displays or on the display’s positions. The position of a navigation system in a vehicle should be assessed objectively, and the effect of the position on the driver’s attention should be studied. Some existing research reports that in-vehicle distraction not only leads to reduced speeds and more frequent lane switching, but also more gazing by the driver to the centre of the road. In this study, to develop an assessment method and to propose the proper position of a CNS, an experiment is carried out in a driving simulator environment. Different methods to track the gaze and physical parameters of the driver are used for HMI (Human-Machine Interface) assessment. The experiment is carried out in a driving simulator to observe the glancing distribution during driving according to the position of the navigation system. Fourteen subjects participated in this experiment. Changes in subjects’ physiological signals and glancing distribution rates were collected.     

Study of in-vehicle route guidance systems for improvement of right-side drivers in the Japanese traffic system

A country can adopt one of two standards for traffic flow — cars may travel on the left or right side of the road. When drivers who are accustomed to driving on the right side of the road drive on the left side, and vice versa, the mental workload is likely increased due to the driver’s unfamiliarity with a new language, the position of the driver’s seat, different driving directions, and other factors that differ from those of their home country. One method of doing this is to make sure that the in-vehicle route guidance information (RGI) is not overly complicated — thereby assisting drivers in improving their safety. Consequently, the aim of this study was to facilitate mobility and improve safety for natural right-side drivers driving temporarily in left-side traffic. In this study, driver behavior and workload — given various types of RGI — were evaluated in a driving simulator with a variety of prescribable test conditions. This research was composed of two experiments. In the first, various types of in-vehicle route guidance systems were tested and evaluated in terms of their characteristics and associated driver behaviors (while driving). In the second experiment, systemic factors and effectiveness were evaluated by two combined systems, arrow and map-type information, based on the results of the first experiment. In light of both experiments, the various types of route guidance systems were discussed in terms of their results. A navigation system was proposed to alleviate some of the secondary tasks such as route selection.     

节能减排汽车驾驶技术浅析

驾驶员的驾驶技术和责任心会直接影响最终的油耗,驾驶节油的关键在于看驾驶人员是否按照车辆运行要求,采用相应的驾驶操作,使人车之间合理搭配保持车辆最佳运行状态,驾驶员的驾驶技术和驾驶习惯会对节油产生非常大的影响,因此,在日常行车过程中,驾驶员需要逐渐提升自身的驾驶水平,时刻牢记驾车注意事项,这样才能达到最终的节约目标。     

Reliability and safety analysis methodology for identification of drivers’ erroneous actions

Owing to significantly individual differences in everyday driving behavior, it is quite difficult to assess the relative importance of driver errors compared with vehicle faults or road environment anomalies. This paper briefly presents several basic concepts for analysis of driving dependability including driving errors, driving reliability, driver recovery from erroneous actions, and key factors that shape driving behavior. This presentation is followed by construction of a shaping architecture for driving behavior that consists of a perception stage, a decision-making stage, an execution stage and correlativity among stages, in addition to internal feedback from complex traffic states. The causation classification of driving errors is then discussed in the context of three elemental types: perception error, decision-making error and execution error. The emphasis of this paper is on how to quantify driving dependability in order to identify various erroneous driver actions during traffic accidents. Specifically, this paper proposes a methodology to measure the probability of driving errors by considering the driver recovery from erroneous actions. The purpose of model-based driving dependability analysis is to quantitatively and qualitatively analyze the relationship between driving errors and traffic accidents causations.     

城市水下道路隧道驾驶安全影响因素分析和改善思路

总结城市水下道路隧道事故分布规律，从驾驶人、隧道光环境及道路条件3方面分析隧道驾驶安全影响因素，对现有安全改善措施及其优缺点进行剖析，并指出城市水下道路隧道驾驶安全优化研究趋势： 应以提升隧道光环境质量为主，考虑交通事故形态、事故致因及影响因素，在确保交通安全的基础上，考虑不同隧道路段驾驶人差异化视觉需求。驾驶人视觉需求可以分为功能性、安全性与舒适性需求，对应的隧道行车环境可分为基本型、安全型与舒适型视觉参照系。提出以构建城市水下道路隧道舒适型视觉参照系为目标，通过隧道照明与隧道视线诱导技术相结合，缓解隧道出入口参照系的剧烈过渡，加强中间段弱视觉参照的城市水下道路隧道驾驶安全优化方法。构建基于空间路权、人因与驾驶任务、差异性与韵律性的隧道驾驶安全优化评价指标体系，为城市水下道路隧道驾驶安全优化提供新思路。     

汽车驾驶安全隐患预防与对策探索

司机是一个比较特殊的工作,通常在驾驶汽车时,司机要确保整个驾驶过程的安全,这就要求司机要具备较高的驾驶素质。如果驾驶员自身的安全意识不高,对车辆驾驶工作没有太深的安全意识,就会给行车工作带来很大的安全隐患。如果司机对驾驶工作安全性意识较高,则行车就会更加安全,司机也会提高自己的综合素质和驾驶技术来保证车辆行驶的安全,避免在行驶中出现一系列安全隐患。因此,在驾驶汽车时,要重点突出安全隐患的预防,并制定一系列的对策,才可能减少车辆的安全事故产生。     

关于L3自动驾驶的人机交互设计研究

随着雷达、摄像头、处理平台的软硬件提升,加上高精地图的辅助,全球越来越多的汽车公司都推出了自动驾驶车辆,覆盖Level 2~Level 3的自动驾驶场景(SAE J3016),但这些场景都需要在必要的时候由驾驶员接管车辆,因此自动驾驶汽车对人机交互设计提出了新的挑战,比如合理的接管流程,驾驶操作的监测,权责划分的提示。     

超速语音提醒风格对驾驶员干预效果分析

文章从驾驶员的气质类型出发,提出了针对不同气质类型的超速语音干预体系。针对不同气质类型的驾驶员,分析并研究其对干预时刻以及干预风格的反应规律,从而得出了不同气质类型的驾驶员对待干预措施的反应的相关性规律。且通过数据统计进一步的分析得出不同气质类型的驾驶员应优先选用的提醒时刻,以及在不同提醒时段应优先选用的语音提醒风格。