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Driver-assistance systems are becoming more commonplace; however, the realized safety benefits of these technologies depend on whether a person accepts and adopts automated driving aids. One challenge to adoption could be a preference-performance dissociation (PPD), which is a mismatch between a self-perceived desire and an objective need for assistance. Research has reported PPD in driving but has not extensively leveraged driving performance data to confirm its existence. Thus, the goal of this study was to compare drivers’ self-reported need for vehicle assistance to their objective driving performance. Twenty-one participants drove on a simulated road and traversed challenging, real-world roadway obstacles. Afterwards, they were asked about their preference for automated vehicle assistance (e.g., steering and braking) during their drive. Overall, some participants exhibited PPD that included both over- and underestimating their need for a particular type of automated assistance. Findings can be used to develop shared control and adaptive automation strategies tailored to particular users and contexts across various safety-critical environments. 

As of early 2023, only a limited number of Society of Automotive Engineers (SAE) Level 3 (L3) automated driving systems are available on the market, and they are primarily offered by luxury vehicle brands. SAE L3 automated driving systems are classified as conditional automation (CA), meaning that the vehicle can undertake some well-defined driving tasks under specific conditions, but the driver must be ready to assume control of the vehicle when prompted by the system. It is anticipated that an increasing number of L3 CA systems will be introduced on public roads in the next few years. However, L3 systems pose unique Human Factors (HF) challenges that require thoughtful consideration to ensure that production systems are feasible without compromising driver or road safety. This panel discussion brings together HF researchers and practitioners with expertise in human behavior and usability design for automotive applications to discuss and delineate key issues specifically related to L3 systems, as well as potential approaches to tackle these issues. 

Advanced systems that require shared control are becoming increasingly pervasive. One advantage of a shared control approach is that the human and machine work together to accomplish safe operations. However, data about the human is needed to implement successful strategies. The goal of this study was to quantify naturalistic driving by collecting performance and physiological data during manual, open-loop driving. Sixteen participants performed a single drive that included four sudden obstacles of increasing difficulty (road debris, construction, inclement weather, and an animal). Participants were asked to traverse each obstacle using self-employed judgement and strategies. Action selection, lane deviation, speed, and heart rate data were recorded. Results showed two distinct driving strategies for avoiding the moving obstacle/animal (left vs. right lane navigation). Also, maximum speed was affected by obstacle type, but heart rate variability was not. Results can be used to inform shared control algorithms designed to combat poor driving performance.