Learning Design Strategies for Optimizing User Behaviour Towards Automation: Architecting Quality Interactions from Concept to Prototype

Automated vehicles are equipped with systems for task operation and furnished with complex sensor-based systems for detecting and avoiding crashes. However, because technology is unruly and prone to error due to its low maturity level, the human is required to be an active member and collaborator in the operation of the vehicle. As a result, users are required to be constantly vigilant for instances where automation may fail and request to intervene. Yet, notably, some users have a tendency to perceive automation as a holy grail and partake in undesirable user behaviours linked to misuse, over trust, and even high-level complacency, etc. Consequently, industry faces safety criticalities, resulting in either low to high-level risk taking behaviours. Thus, focus should be on taming users’ knowledge to fit the level of automated driving, trucking, flying, farming systems’ capabilities and limitations by investing in exceptionally ergonomic-inspired strategies that promote desirable user behaviours, such as the intended use of automation and interaction with its human-machine interfaces (HMIs) over the sequence of time. As a result, N = 20 air and ground vehicle industry experts views on training to use and learning strategies for optimizing safety and risk-free human-automation interaction and use (HAI/U) were considered. The paper devises ergonomically enthused learning design strategies in support of deprogramming-risky behaviours and reprogramming-safe taking behaviours towards automation, by bearing in mind long-term effects.KeywordsAutomated vehicles Learning design strategies Long-term effects Safe and risk-free behaviour Automation-induced effects Experts views. Marie Skłodowska-Curie Actions; Innovative Training Network (ITN); SHAPE-IT; Grant number 860410