Brain-Based Indicators of Passenger Trust During Open-Road Driving

Autonomous Driving has been continuously improving with the integration of bio-physiological data and deep learning systems. Passenger-driver behavior from psychological processes can provide insight into the trust levels of the dyad dynamic during an open-road drive. Measuring passenger trust can benefit in speeding up the adoption of self-driving cars. This area has not been fully explored from the passenger's point of view and can yield results that could potentially propel the progress of driver safety systems in autonomous driving. A novel approach to correlating the passenger-driver trust levels can be elicited from brain-based indicators that are extracted from time-locked electroencephalogram (EEG) signals that capture both sensory and cognitive processes. In this paper, we propose a trust identification technique utilizing evoked response potential (ERP) events such as P300 along with beta/alpha frequency ratios from specific passengers during braking, aggressive acceleration, and lane changing scenarios. The results obtained from the decomposition of these EEG signals into the frequency bands and application of machine learning techniques on data collected from frontal and parietal electrodes during these driving events prove the feasibility of this study. By examining additional passenger-driver pairs with varying social interaction and trust profiles in the future, we can strengthen the existence of a cognitive correlation between passenger-driver behavior and thus improve the efficiency of driver safety systems.