Abstract: Safe and smooth interaction between 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) without the
understanding about the dynamic interaction process. By investigating 4300 video clips of traffic accidents, it is found that
the limited dynamic visual field of drivers is one leading factor in inter-vehicle interaction accidents. 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 improve both safety and time efficiency, and 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.

Key words: Automated driving · Social compatibility · Game theory · Interactive driving · Unsignalized intersection