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We present a new method for multi-agent planning involving human drivers andautonomous vehicles (AVs) in unsignaled intersections, roundabouts, and duringmerging. In multi-agent planning, the main challenge is to predict the actionsof other agents, especially human drivers, as their intentions are hidden fromother agents. Our algorithm uses game theory to develop a new auction, calledGamePlan, that directly determines the optimal action for each agent based ontheir driving style (which is observable via commonly available sensors).GamePlan assigns a higher priority to more aggressive or impatient drivers anda lower priority to more conservative or patient drivers; we theoreticallyprove that such an approach is game-theoretically optimal prevents collisionsand deadlocks. We compare our approach with prior state-of-the-art auctiontechniques including economic auctions, time-based auctions (first-infirst-out), and random bidding and show that each of these methods result incollisions among agents when taking into account driver behavior. We comparewith methods based on DRL, deep learning, and game theory and present ourbenefits over these approaches. Finally, we show that our approach can beimplemented in the real-world with human drivers.

GamePlan: Game-Theoretic Multi-Agent Planning with Human Drivers at Intersections, Roundabouts, and Merging