  Safe and smooth interacting with other vehicles is one of the ultimate goals
of driving automation. However, recent reports of demonstrative deployments of
automated vehicles (AVs) indicate that AVs are still difficult to meet the
expectation of other interacting drivers, which leads to several AV accidents
involving human-driven vehicles (HVs). This is most likely due to the lack of
understanding about the dynamic interaction process, especially about the human
drivers. By investigating the causes of 4,300 video clips of traffic accidents,
we find that the limited dynamic visual field of drivers is one leading factor
in inter-vehicle interaction accidents, especially in those involving trucks. A
game-theoretic decision algorithm considering social compatibility is proposed
to handle the interaction with a human-driven truck at an unsignalized
intersection. Starting from a probabilistic model for the visual field
characteristics of truck drivers, social fitness and reciprocal altruism in the
decision are incorporated in the game payoff design. Human-in-the-loop
experiments are carried out, in which 24 subjects are invited to drive and
interact with AVs deployed with the proposed algorithm and two comparison
algorithms. Totally 207 cases of intersection interactions are obtained and
analyzed, which shows that the proposed decision-making algorithm can not only
improve both safety and time efficiency, but also make AV decisions more in
line with the expectation of interacting human drivers. These findings can help
inform the design of automated driving decision algorithms, to ensure that AVs
can be safely and efficiently integrated into the human-dominated traffic.
