Understanding Driver Response Patterns to Mental Workload Increase in Typical Driving Scenarios

As vehicles become more complex and traffic increases, the associated mental workload of driving should increase, potentially compromising driving safety. As mental workload increases (as measured by the detection response time task), does how people drive (as assessed by driving performance and eye fixations) change? How does driving experience impact on such response patterns? To address those questions, data were collected in a motion-based driving simulator. Two driving scenarios were examined, a stop-controlled intersection (high workload — 16 participants, 320 trials) and speed-limited highway (low workload — 11 participants, 264 trials). In each scenario, in half of the trials, the participants were required to complete or not to complete a distracting secondary task. Hierarchical cluster analysis was used to identify driver response patterns. For highway driving, they are: (1) increased eye fixation variability and unchanged driving performance, and (2) unchanged fixation variability and increased mean speed. For intersection driving, they are: (1) increased and (2) decreased fixation variability both with decreased speed (mean and variance), and (3) increased fixation variability with increased speed. Eye fixation variability was more strongly associated with increased mental workload than other driving performance statistics. Furthermore, in contrast to prior research, changes in driving performance and eye fixations were not necessarily correlated with each other as mental workload increased. Novice drivers exhibit higher gaze variability, and they are more prone to maintain vehicle control than experienced drivers

Prediction of drivers’ performance in highly automated vehicles

Purpose: The aim of this research was to assess the predictability of driver’s response to critical hazards during the transition from automated to manual driving in highly automated vehicles using their physiological data.Method: A driving simulator experiment was conducted to collect drivers’ physiological data before, during and after the transition from automated to manual driving. A total of 33 participants between 20 and 30 years old were recruited. Participants went through a driving scenario under the influence of different non-driving related tasks. The repeated measures approach was used to assess the effect of repeatability on the driver’s physiological data. Statistical and machine learning methods were used to assess the predictability of drivers’ response quality based on their physiological data collected before responding to a critical hazard. Findings: - The results showed that the observed physiological data that was gathered before the transition formed strong indicators of the drivers’ ability to respond successfully to a potential hazard after the transition. In addition, physiological behaviour was influenced by driver’s secondary tasks engagement and correlated with the driver’s subjective measures to the difficulty of the task. The study proposes new quality measures to assess the driver’s response to critical hazards in highly automated driving. Machine learning results showed that response time is predictable using regression methods. In addition, the classification methods were able to classify drivers into low, medium and high-risk groups based on their quality measures values. Research Implications: Proposed models help increase the safety of automated driving systems by providing insights into the drivers’ ability to respond to future critical hazards. More research is required to find the influence of age, drivers’ experience of the automated vehicles and traffic density on the stability of the proposed models. Originality: The main contribution to knowledge of this study is the feasibility of predicting drivers’ ability to respond to critical hazards using the physiological behavioural data collected before the transition from automated to manual driving. With the findings, automation systems could change the transition time based on the driver’s physiological state to allow for the safest transition possible. In addition, it provides an insight into driver’s readiness and therefore, allows the automated system to adopt the correct driving strategy and plan to enhance drivers experience and make the transition phase safer for everyone.</div

Effects of cognitive tasks on car drivers’ behaviors and physiological responses

The effects of drivers’ engagement in cognitive tasks (i.e., non-visual, cognitively loading activities unrelated to the task of driving) are debated and unclear. Numerous experiments show impaired driver behaviors, yet naturalistic studies typically do not find an increased crash risk. In the future, autonomous driving (AD) is expected to improve traffic safety while allowing safe engagement in cognitive (and other) tasks. Having the opportunity to perform non-driving related tasks while traveling may then motivate drivers to use AD, provided they can actually engage in the tasks. Unfortunately, research on drivers’ engagement in cognitive tasks suffers severe methodological limitations since reliable and unintrusive measures of cognitive load are lacking.The aim of this thesis is therefore to advance the understanding of task-induced cognitive load in the context of traffic safety. This aim is split into two objectives: A) to better understand how drivers’ involvement in cognitive tasks can affect safety-relevant driver behaviors and decisions and B) to provide methodological guidance about assessing cognitive load in drivers using physiological measures.To accomplish Objective A, effects of cognitive tasks on driver behaviors were studied during routine driving and in a safety-critical event in a driving simulator. Also, drivers’ ability to engage in a non-driving related task while using AD in real traffic was explored. In line with the cognitive control hypothesis (Engstr\uf6m et al., 2017), it was found that cognitive tasks negatively affected driver behaviors in situations where cognitive control was needed, for example in intersections—but not in a lead vehicle braking scenario where responses were triggered automatically by visual looming. It was also found that although the number of off-path glances decreased during cognitive load, the timing of the remaining glances was unaffected. Clearly, cognitive load has different effects on different mechanisms. When using AD, drivers were indeed capable of engaging in a non-driving related task—suggesting that AD will be able to fulfill drivers’ desire to perform such tasks while traveling, which may motivate AD usage and thus improve traffic safety (given that AD is truly safer than manual driving). Finally, a simulator study addressing Objective B showed that the measurability of cognitive load was greatly improved by recognizing that multiple coexisting mental responses give rise to different physiological responses. This approach can provide less context-dependent measurements and allows for a better, more detailed understanding of the effects of cognitive tasks.These findings can help improve traffic safety—both by being used in system development, and as part of the systems themselves