MAVERIC: A Data-Driven Approach to Personalized Autonomous Driving

Personalization of autonomous vehicles (AVs) may significantly increase acceptance. In particular, we hypothesize that the similarity of an AV's driving style compared to a user's driving style, the level of aggressiveness of the driving style, and other subjective factors (e.g., personality) will have a major impact on user's willingness to use the AV. In this work, we 1) develop a data-driven approach to personalize driving style and calibrate the level of aggressiveness and 2) investigate the subjective factors that impact user preference. Across two human subject studies (n = 54), we demonstrate that our approach can mimic the driving styles and tune the level of aggressiveness. Second, we leverage our framework to investigate the factors that impact homophily. We demonstrate that our approach generates driving styles objectively ($p < . 001$) and subjectively ($p =. 002$) consistent with end-user styles ($p < . 001$) and can effectively isolate and modulate a dimension of style (i.e., aggressiveness) ($p < . 001$). Furthermore, we find that personality ($p < . 001$), perceived similarity ($p < . 001$), and high-velocity driving style ($p =. 0031$) significantly modulate the effect of homophily.