StylePredict: Machine Theory of Mind for Human Driver Behavior From Trajectories
This addresses the need for socially aware autonomous vehicles to adapt to human drivers, though it is incremental as it builds on existing trajectory analysis methods.
The paper tackles the problem of autonomous vehicles behaving conservatively by introducing StylePredict, a Machine Theory of Mind approach that infers human driver behaviors from vehicle trajectories, and it finds an inverse correlation between overspeeding and overtaking/lane-changing styles across cultures.
Studies have shown that autonomous vehicles (AVs) behave conservatively in a traffic environment composed of human drivers and do not adapt to local conditions and socio-cultural norms. It is known that socially aware AVs can be designed if there exist a mechanism to understand the behaviors of human drivers. We present a notion of Machine Theory of Mind (M-ToM) to infer the behaviors of human drivers by observing the trajectory of their vehicles. Our M-ToM approach, called StylePredict, is based on trajectory analysis of vehicles, which has been investigated in robotics and computer vision. StylePredict mimics human ToM to infer driver behaviors, or styles, using a computational mapping between the extracted trajectory of a vehicle in traffic and the driver behaviors using graph-theoretic techniques, including spectral analysis and centrality functions. We use StylePredict to analyze driver behavior in different cultures in the USA, China, India, and Singapore, based on traffic density, heterogeneity, and conformity to traffic rules and observe an inverse correlation between longitudinal (overspeeding) and lateral (overtaking, lane-changes) driving styles.