The dynamics of driving performance in non-optimal human states: Drowsiness, high cognitive load, and acute stress

OBJECTIVES: Non-optimal driver states, such as drowsiness, high cognitive load, and stress, constitute significant factors contributing to risks on the road and traffic accidents. In this work, we aimed to describe the dynamics of driving performance on highway and urban roads across non-optimal states viewed on the unified arousal scale, from drowsiness to stress. METHODS: = 41, SD = 14.40) drivers took part in experiments modeling states of decreased (fatigue, drowsiness) or increased (high cognitive load, stress) arousal in a simulated driving task. A set of metrics was selected to evaluate driving performance in highway and urban environments in terms of decline and improvement, which included measures of lateral control, longitudinal control, and response properties. RESULTS: Our analyses have shown that different non-optimal driver states can have similar negative effects on performance measures, and the results mostly fit the predictions of the Yerkes-Dodson Law. At the same time, we observed that changes in performance can be multidirectional: a drop in one metric may be accompanied by an improvement of another. Thus, patterns of driving performance in non-optimal states vary and can be adaptive, allowing a driver to maintain safe and efficient driving behavior. However, overall, low arousal on highway was associated with declines in lateral control measures and response properties. Additional tasks increasing arousal on highway improved lane keeping performance but were associated with more over speeding events and reduced response measures. Increased cognitive load and acute stress on urban roads reduced lane keeping performance. CONCLUSIONS: Our results highlight the importance of looking at combinations of metrics and their patterns that are characteristic for different non-optimal driver states and road types. Identifying maladaptive driving patterns that can be observed across human states on the arousal scale and specific for different road environments is pivotal for developing and validating driver monitoring systems.