With the Society of Automotive Engineers Level 3 automation, drivers are no longer required to actively monitor driving environments, and can potentially engage in non-driving related tasks. Nevertheless, when the automation reaches its operational limits, drivers will have to take over control of vehicles at a moment’s notice. Drivers have difficulty with takeover transitions, as they become increasingly decoupled from the operational level of driving. In response to the takeover difficulty, existing literature has investigated various factors affecting takeover performance. However, not all the factors were studied comprehensively, and the results of some factors were mixed. Meanwhile, there is a lack of research on the development of computational models that predict drivers’ takeover performance using their physiological and driving environment data. Furthermore, current research on the design of in-vehicle alert systems suffers from methodological shortcomings and presents identical takeover warnings regardless of event criticality.
To address these shortcomings, the goals of this dissertation were to (1) examine the effects of drivers' cognitive load, emotions, traffic density, and takeover request lead time on their driving behavioral (takeover timeliness and quality) and psychophysiological responses (eye movements, galvanic skin responses, and heart rate activities) to takeover requests; (2) develop computational models to predict drivers’ takeover performance using their physiological and driving environment data via machine learning algorithms; and (3) design in-vehicle alert systems with different display modalities and information types and evaluate the displays in different event criticality conditions via human-subject experiments.
The results of three human-subject experiments showed that positive emotional valence led to smoother takeover behaviors. Only when drivers had low cognitive load, they had shorter takeover reaction time in high oncoming traffic conditions. High oncoming traffic led to higher collision risk. High speed led to higher collision risk and harsher takeover behaviors in lane changing scenarios, but engendered longer takeover reaction time and smoother takeover behaviors in lane keeping scenarios. Meanwhile, we developed a random forest model to predict drivers' takeover performance with an accuracy of 84.3% and an F1-score of 64.0%. Our model had finer granularity than and outperformed other machine learning models used in prior studies.
The findings of alert system design studies showed that drivers had more anxiety with the why only information compared to the why + what will information when information was presented in the speech modality. They felt more prepared to take over control of the vehicle and had more preference for the combination of augmented reality and speech conditions than others when drivers were in high event criticality situations.
This dissertation can add to the knowledge base about takeover response investigation, takeover performance prediction, and in-vehicle alert system design. The results will enhance the understanding of how drivers’ emotions, cognitive load, traffic density, and scenario type influence their takeover responses. The computational models for takeover performance prediction are underlying algorithms of in-vehicle monitoring systems in real-world applications.
The findings will provide design recommendations to automated vehicle manufacturers on in-vehicle alert systems. This will ultimately enhance the interaction between drivers and automated vehicles and improve driving safety in intelligent transportation systems.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169727/1/nadu_1.pd
