What can we learn from Automated Vehicle collisions? A deductive thematic analysis of five Automated Vehicle collisions

• Published in: Safety science 2021-09, Vol.141, p.105320, Article 105320
• Main Authors: Merriman, Siobhan E., Plant, Katherine L., Revell, Kirsten M.A., Stanton, Neville A.
• Format: Article
• Language: English
• Subjects: Accident Analysis; Accident prevention; Automated Vehicles; Automation; Automobile drivers; Automobile industry; Autonomous vehicles; Collisions; Deductive Thematic Analysis; Driver behavior; Driver Training; Hazard identification; Hazard mitigation; Literature reviews; Safety; Situational awareness; Training
• ISSN: 0925-7535; 1879-1042
• DOI: 10.1016/j.ssci.2021.105320

•A Deductive Thematic Analysis was conducted on five Automated Vehicle Collisions.•Interconnection models were created, and consistent patterns emerged.•Links were made with the drivers’ attitudes, mental models and trust in automation.•The drivers were underloaded and did not effectively monitor the road environment.•This impaired their ability to identify and avoid hazards in their path. There have been a number of high-profile collisions involving Automated Vehicles on the road. Although car manufacturers are making considerable investments into the development of Automated Vehicles, these collisions may deter the public from purchasing and using them. Therefore, solutions need to be developed to prevent these collisions from occurring in the future. One such solution is driver training. A previous literature review identified nine themes which are essential in Automated Vehicle driver training. In this article, a deductive thematic analysis was conducted on five high-profile Automated Vehicle collisions in order to demonstrate the relevance of these themes and to gain insights into how the driver’s behaviour contributed to each collision, thus understand the potential role of training in reducing collisions of this nature. By creating interconnection models for each collision, a consistent pattern emerged. A link was made with the drivers’ attitudes, the accuracy of their mental models and their level of trust in the automation. The automation caused the drivers to become underloaded, which impaired their ability to effectively monitor the automation and the road environment. This could have impaired their situation awareness and their ability to identify and avoid hazards in the path of their vehicle. This analysis suggests that future Automated Vehicle driver training programmes should be multifaceted and cover all nine themes. This analysis has validated these nine driver training themes, so these themes and interconnections can help in the development of a comprehensive training programme for drivers of Automated Vehicles in the future.