Masello Leandro, Sheehan Barry, Castignani German, Shannon Darren, Murphy Finbarr
University of Limerick, Limerick KB3-040, Ireland; Motion-S S.A., Mondorf-les-Bains, L-5610, Luxembourg.
University of Limerick, Limerick KB3-040, Ireland.
Accid Anal Prev. 2023 Apr;183:106969. doi: 10.1016/j.aap.2023.106969. Epub 2023 Jan 23.
Advanced driver assistance systems (ADAS) present promising benefits in mitigating road collisions. However, these benefits are limited when risky drivers continue engaging in distraction events. While there is evidence that real-time warnings help improve driving behaviour, the sustained benefits of warning-based ADAS on reducing driving distraction in light commercial vehicle (LCV) drivers remain unclear. This research determines the effect of receiving instant distraction warnings over two years using a naturalistic driving dataset comprising around one million trips from 373 LCV drivers in the Republic of Ireland. Furthermore, the study applies Association Rule Mining (ARM) to find the contextual variables (e.g., speed limit, road type, traffic conditions) that increase the likelihood of distraction events. The results show that warning-based ADAS providing real-time warnings helps reduce distraction events triggering driver inattention, forward collision, and lane departure warnings. Over half of the studied fleet reduced these warnings by at least 50% - lane departure after two months and driver inattention and forward collision after six months. It is found that both passive and active monitoring systems, coupled with coaching and rewards, significantly reduce aggressive driving behaviours tied to harsh acceleration (by 76%) and harsh braking (by 65%). The results of ARM show that the driving context introduces explanatory information for road safety programs. Low-speed urban roads and the summer season increase the likelihood of driver inattention and forward collision warnings. In contrast, high-speed rural roads increase the likelihood of lane departure warnings. These research findings support road safety stakeholders in developing risk assessments based on warning-based ADAS, targeted campaigns to reduce driving distraction, and driving coaching programs.
先进驾驶辅助系统(ADAS)在减轻道路碰撞方面具有显著益处。然而,当危险驾驶者持续进行分心行为时,这些益处就会受到限制。虽然有证据表明实时警告有助于改善驾驶行为,但基于警告的ADAS对减少轻型商用车(LCV)驾驶者分心的持续益处仍不明确。本研究利用来自爱尔兰共和国373名LCV驾驶者约100万次行程的自然驾驶数据集,确定了在两年时间内接收即时分心警告的效果。此外,该研究应用关联规则挖掘(ARM)来找出增加分心事件可能性的情境变量(例如,限速、道路类型、交通状况)。结果表明,基于警告的ADAS提供实时警告有助于减少引发驾驶员注意力不集中、前方碰撞和车道偏离警告的分心事件。超过一半的研究车队将这些警告减少了至少50%——两个月后减少车道偏离警告,六个月后减少驾驶员注意力不集中和前方碰撞警告。研究发现,被动和主动监测系统,再加上指导和奖励,能显著减少与急加速(减少76%)和急刹车(减少65%)相关的攻击性驾驶行为。ARM的结果表明,驾驶情境为道路安全计划引入了解释性信息。低速城市道路和夏季会增加驾驶员注意力不集中和前方碰撞警告的可能性。相比之下,高速农村道路会增加车道偏离警告的可能性。这些研究结果有助于道路安全利益相关者制定基于警告的ADAS的风险评估、减少驾驶分心的针对性活动以及驾驶指导计划。