Predicting Driver Takeover Performance in Conditional Automation (Level 3) through Physiological Sensing
Carol Menassa, Associate Professor & John L. Tishman CM Faculty Scholar of Civil and Environmental Engineering – The University of Michigan
Vineet Kamat, Professor & John L. Tishman CM Faculty Scholar of Civil and Environmental Engineering – The University of Michigan
Da Li, Assistant Professor of Civil Engineering – Clemson University
Julian Brinkley, Assistant Professor of Computing – Clemson University
The National Highway Traffic Safety Administration (NHTSA) calls for fundamental research on “the driver performance profile over time in sustained and short-cycle automation … and driver-vehicle interface to allow safe operation and transition between automated and nonautomated vehicle operation.” The emerging level 3 autonomous vehicle (AV) has the potential to transform driving because it can perform all aspects of the driving task and allow for complete disengagement of drivers (e.g., sit back and relax) under certain driving scenarios. The vehicle can handle situations that require an immediate response, such as emergency braking. However, this is not fully autonomous, and still requires the driver to be prepared for takeover at all times with a few seconds of warning. Being able to measure and predict the takeover performance (TOP) ahead of time and issue adequate warnings is thus critical to ensure driver comfort, trust, and safety in the system and ultimately acceptance of the technology by different stakeholders. This has not been explored to the extent of establishing complete and irrefutable trust in the autonomous vehicle system and its ability to engage the driver in safe and effective takeover under certain driving scenarios. Therefore, the objective of this project is to perform fundamental research to understand drivers’ capabilities of taking over the vehicle safely and promptly at any time in level 3 automation. This project advances fundamental research in human factors in level 3 AVs. This is achieved through an integrated treatment of the drivers’ TOP measured and predicted through physiological features and driving environment data in level 3 AVs. Thus, the main objective of this research will be to investigate the feasibility of using multimodal physiological features collected from drivers in level 3 AVs under different driving and disengagement scenarios (secondary tasks) to develop a personalized and real-time prediction of TOP. The project will engage a diverse group of students and faculty and develop a research program in an unexplored area of level 3 AVs, leading to substantial advances in how human physiological sensing can be used to understand the driver’s TOP, especially in a personalized manner. Such an understanding can eventually lead to the design of adaptive and personalized alerts that can be integrated in level 3 AVs.
Research Thrust(s): Human Factors
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