Risk and Safety-Based Behavioural Adaptation Towards Automated Vehicles: Emerging Advances, Effects, Challenges and Techniques

Automation systems, artificial intelligence (AI) and intelligent systems (IS) are a trendy topic in the automated vehicle (AV) domain. Numerous partial AV (pAV) and imminent conditional AV (cAV) systems studies have paved the way in closing the knowledge gap on achieving safety assurance. With highly AV (hAV) systems envisioned the imminent future, it is thus, imperative that we evaluate possible micro and macro effects on behavioural adaptations (BA) and user behaviour over long-term repeated exposure. For example, considering neurocognitive and neurophysiological effects on BA. We sampled N = 20 industry experts and tapped into their levels of understanding and knowledge models. We scrutinised experts’ mental models on emerging advances, effects, challenges and techniques. This is in order to prolifically draw knowledge sets for resilient engineering principles for behaviour-based safety, safety proactivity in long-term adaptations, with careful consideration on mitigating behaviour-based risk adaptations. Moreover, derive safety-based (desirable) and risk-based (undesirable) behavioural adaptation knowledge from the experience perspectives of experts, as well as, bridge the knowledge gap in the field of automated driving, automated trucking, automated flying and automated farming, making it highly relevant to industry stakeholders. The results illustrate nuances involved in user behaviour towards vehicle automation systems (VAS) and risk mitigation. The lessons learned contribute to the development or modification of existing safety protocols in the AV domain.