Background
Vehicle Automation technologies have been making rapid advances in terms of development and deployment in the past decades (NHTSA, 2017). The Society of Automotive Engineers (SAE) has classified Vehicle Automation into six levels (SAE, 2021), ranging from no automation (Level 0) to full automation (Level 5). Although fully automated driving (Level 4 and 5) requiring no driver or operator dependency has not yet been achieved, and there are significant technical and human factors challenges to deploying Level 3, there have been positive strides in the development of partially automated driving features (Level 1 and 2), otherwise known as Advanced Driving Assistance Systems (ADAS). These systems carry out various automated functions such as collision avoidance, vehicle speed regulation and gap distance maintenance (Adaptive Cruise Control), or lane position/centering assistance (Lane Centering Assist), etc. ADAS promises safety and convenience benefits to drivers. However, because these systems are designed to take over some of the traditional driving tasks, the introduction of these systems has changed how drivers interact with their vehicles. In vehicles with these systems, drivers assume a new role, i.e., supervising the functions of these systems and monitoring their driving environment for possible intervention situations.
This new role requires drivers to have appropriate knowledge and awareness about their systems' capabilities and, potentially more importantly, their limitations. However, the literature suggests that drivers are unaware or uninformed about their vehicles' systems (Jenness et al., 2008; McDonald et al., 2018). There may be various reasons for this, with a potentially important one related to the resources available for drivers to understand these systems. While studies have shown that many drivers choose to obtain their knowledge about these systems from owner's manuals (McDonald et al., 2016), they only partially or incompletely read these manuals (Mehlenbacher et al., 2002). This is of particular importance since new ADAS users have difficulties understanding their systems (Larsson, 2012). Moreover, the quality of the material presented in the owner's manual can also affect the driver's knowledge and shape their perceptions of these systems (Singer & Jenness, 2020). Also, there are variances and inconsistencies in the reporting of system limitations across different manufacturers (Pradhan et al., 2021), and this could result in misconceptions and overestimation of the systems' capabilities.
There is evidence that a lack of knowledge or any misconceptions about such systems may affect drivers' mental models, which could potentially manifest as action or response-related driver errors (Dickie & Boyle, 2009; McDonald et al., 2018; Pradhan et al., 2021). Mental models have been defined as ‘a representation of the typical causal interconnections involving actions and environmental factors that influence a system's functioning’ (Durso & Gronlund, 1999). Mental models continuously update knowledge stored in memory and are derived from encountering similar situations as those from past experiences. However, because the deployment of ADAS in vehicles has been recent, drivers may lack accurate mental models about ADAS given the minimal experience, as well as lack knowledge about system functions and limitations as noted earlier. Incomplete knowledge or mismatched expectations about a system's function and limitations may result in lack of mode awareness hindering its user's ability to allocate appropriate attentional resources and detect errors, failures, and miscommunications between the user and the system (Sarter & Woods, 1995). In the driving domain, such user-related errors have been known to play a critical role in motor vehicle crashes. Singh (2015) found that driver related-errors were assigned as the critical reason in about 94% of crashes, where the term ‘critical reason’ was defined as the last event in the crash causal chain. It was also found that about 41% of the crashes were caused by recognition-related errors, while decision-based errors and performance-related errors were the cause for 33% and 11% of the crashes, respectively. This inaccuracy or incompleteness of mental models could lead to mode confusion (Wilson et al., 2020), miscalibrated trust with regards to system capabilities (Beggiato & Krems, 2013; Kidd et al., 2017), and may result in operator errors during ADAS usage (Pradhan et al., 2020; Pradhan et al., 2021; Stanton & Salmon, 2009). Hence, improving drivers' mental models is a critical requirement for appropriate and safe use of vehicle technologies.
Driver's mental models could be improved through driver training. Driver education has helped in improving skills related to hazard perception, visual search, and situational awareness (Horswill et al., 2015; Vlakveld, 2014; Walker et al., 2009). Driver training has also improved drivers' cognitive driving abilities (Yamani et al., 2016), which could indicate that training and education of drivers can be used to improve novice drivers' knowledge and awareness of advanced driver assistance systems. Training can be defined as ‘a planned and systematic effort to modify or develop knowledge, skills and attitudes through learning experiences, to achieve effective performance in an activity or a range of activities’. Learning can be defined as ‘a relatively permanent change in behavior or in the behavioral potential that results from experience’ (Garavan, 1997). Learning can therefore be understood as one of the outcomes of training. Learning also consists of many aspects, such as learning environment, abilities and learning style (Koć-Januchta et al., 2017; Stern, 2017). Learning style in this case can be thought of as an individual's preferred way of learning (Plass et al., 1998). Learning styles and preferences are usually based on four major methods - visual, auditory, kinesthetic and tactile (Klašnja-Milićević et al., 2016). According to previous studies, majority of the population are visual learners (Zopf et al., 2004) and learn using pictures, videos, etc. Studies have also found that when learners were instructed to form images while reading texts or received pictorial cues, their recall accuracy and retention was high (Paivio, 2014; Sadoski & Willson, 2006; Tabbers et al., 2004).
While training leads to learning and skill development, one could seek knowledge and experiences of their own volition, which could also have similar learning outcomes and skill development. The focus of this study is to improve driver knowledge solely through training methods. The impacts of driver training have been widely examined in the literature, and there is strong evidence of improvements after training in driver behaviors such as attention maintenance (Pradhan et al., 2011), hazard perception (Pradhan et al., 2005), and hazard mitigation (Muttart, 2013). However, these training approaches have been studied and evaluated for the traditional driving domain. Since these training approaches were not designed for or targeted any vehicle technologies, there is little that can be extrapolated from them in terms of impact on mental models of advanced technologies.
The literature on driver training in the domain of vehicle automation is somewhat sparse. Some prior research has shown training to be effective in improving driver's knowledge about system limitations, but the results have been somewhat mixed. In a study where the training material provided was weak or strong (given via powerpoint presentations), it was found that drivers with strong mental models were better at operating ADAS during edge-case situations than those with weak mental models (Gaspar et al., 2020). Another study showed that user education through owner's manual and interactive tutorial led to increased understanding of driving automation systems (Forster et al., 2019). In this study, participants were given either baseline information (generic information about L3 and L2 systems) or Owner's Manual (information delivered in a four-page, short sentences and bulleted format, taken from a BMW manual) or interactive (participants completed a tutorial and answered questions).
In contrast, another study suggested that training (provided as descriptions of the ACC interface and explanations of icons displayed) improved drivers' abilities to detect system notifications and change in system status, but only moderately improved drivers' comprehension of system limitations (Mueller et al., 2020). Victor et al. (2018) also reported that of drivers who received specific instruction about vehicle limitations, as well as supervision reminders to keep eye on road and hands on wheel, 28% still collided with a conflict object in their experimental field study. While a large majority of the participants (72%) did benefit from the supervision and instruction (training), it is still critical to examine the failures, and the authors acknowledge the need for more research to examine how to communicate system limitations to drivers. Similarly, Noble et al. (2019) reported that training strategies (baseline and interactive module where participants watched videos and interacted with the system as instructed) only led to limited differences in driver knowledge and no difference in driver behaviors or attitudes.
Drivers' knowledge can contribute to their trust in a system, and thus to the appropriate use of a safety system. However, while there is much work conducted in this domain, there is still mixed evidence about the impact of training on driver trust. In study conducted by Payre et al. (2017), simple or elaborate (text and videos) training optimized drivers' trust when driving with automated features and decreased time to respond to emergency situations. Similarly, Koustanaï et al. (2012) familiarized drivers with Forward Collision Warning system (an ADAS feature) through a simulator-based training and also found improvement in trust towards the system and improvement in driver-system interactions. Three other studies (Beggiato & Krems, 2013; Kazi et al., 2017; Lee & See, 2004) also found that the correct use and knowledge of ACC depends on the level of trust in the system. However, when comparing limitation-focused training and responsibility-focused training approaches,DeGuzman and Donmez (2022) found that there were no differences between the two approaches regarding the drivers' ADAS related knowledge, but found that both approaches negatively affected trust for scenarios where ADAS may not work. Similarly, Zahabi et al. (2021) found no significant differences in driver trust and knowledge about automation when comparing between demonstration-based and video-based training programs.
Overall, we need more evidence about the impact of training on improving drivers' mental models. Traditionally, educating drivers about vehicle capabilities and features have been conducted using Owner's Manual, which has been described as tedious and too complicated (Mehlenbacher et al., 2002). In this study, we compare multiple training methods, a Text-Based training method which is based on the Owner's Manual and a Visualization method which describes the different states of Adaptive Cruise Control using state diagrams. We examine the effect of training method and content to understand the impact of different approaches on drivers' mental models.