Challenges for automated vehicle driver training: A thematic analysis from manual and automated driving

培训(气象学) 集合(抽象数据类型) 运输工程 钥匙(锁) 自动化 高级驾驶员辅助系统 计算机科学 工程类 计算机安全 人工智能 机械工程 物理 气象学 程序设计语言
作者
Siobhán E. Merriman,Katherine L. Plant,Kirsten M. A. Revell,Neville A. Stanton
出处
期刊:Transportation Research Part F-traffic Psychology and Behaviour [Elsevier]
卷期号:76: 238-268 被引量:31
标识
DOI:10.1016/j.trf.2020.10.011
摘要

Considerable research and resources are going into the development and testing of Automated Vehicles. They are expected to bring society a huge number of benefits (such as: improved safety, increased capacity, reduced fuel use and emissions). Notwithstanding these potential benefits, there have also been a number of high-profile collisions involving Automated Vehicles on the road. In the majority of these cases, the driver's inattention to the vehicle and road environment was blamed as a significant causal factor. This suggests that solutions need to be developed in order to enhance the benefits and address the challenges associated with Automated Vehicles. One such solution is driver training. As drivers still require manual driving skills when operating Automated Vehicles on the road, this paper applied the grounded theory approach to identify eight "key" themes and interconnections that exist in current manual vehicle driver training. These themes were then applied to the limited literature available on Automated Vehicle driver training, and a ninth theme of trust emerged. This helped to identify a set of training requirements for drivers of Automated Vehicles, which suggests that a multifaceted approach (covering all nine themes and manual and Automated Vehicle driving skills) to driver training is required. This framework can be used to develop and test a training programme for drivers of Automated Vehicles.
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