Commercial driving is a complex multifaceted task influenced by personal traits and external contextual factors, such as weather, traffic, road conditions, etc. Previous intelligent commercial driver-assessment systems do not consider these factors when analysing the impact of driving behaviours on road safety, potentially producing biased, inaccurate, and unfair assessments. In this paper, we introduce a methodology (Expert-centered Driver Assessment) towards a fairer automatic road safety assessment of drivers' behaviours, taking into consideration behaviours as a response to contextual factors. The contextual moderation embedded within the intelligent decision-making process is underpinned by expert input, comprising of a range of associated stakeholders in the industry. Guided by the literature and expert input, we identify critical factors affecting driving and develop an interval-valued response-format questionnaire to capture the uncertainty of the influence of factors and variance amongst experts' views. Questionnaire data are modelled and analysed using fuzzy sets, as they provide a suitable computational approach to be incorporated into decision-making systems with uncertainty. The methodology has allowed us to identify the factors that need to be considered when moderating driver sensor data, and to effectively capture experts' opinions about the effects of the factors. An example of our methodology using Heavy Goods Vehicles professionals input is provided to demonstrate how the expert-centred moderation can be embedded in intelligent driver assessment systems.
翻译:商业驾驶是一项复杂的多方面任务,受到个人特点和天气、交通、道路条件等外部背景因素的影响。 以前的智能商业驾驶评估系统在分析驾驶行为对道路安全的影响时,不会考虑这些因素,可能会产生偏差、不准确和不公平的评估。在本文件中,我们采用了一种方法(以专家为中心的驾驶者评估),对驾驶者的行为进行更公平的自动道路安全评估,同时考虑到作为对背景因素反应的行为。智能决策过程中所包含的背景温和性得到了专家投入的支持,其中包括行业内一系列相关利益攸关方。在文献和专家投入的指导下,我们查明了影响驾驶的关键因素,并开发了一个定期估值的反应格式调查表,以了解各种因素和专家观点差异的影响的不确定性。问卷数据采用模糊的数据集进行模拟和分析,因为这些数据集提供了一种适当的计算方法,以便纳入具有不确定性的决策系统。该方法使我们能够确定在调制驱动器传感器数据时需要考虑的因素,并有效地收集专家对各种因素的影响的意见。在文献和专家投入的指引指导下,我们用智能化的驱动器评估方法的范例是:使用重力车辆专家投入的稳健性评估。