title: Resolving conflict in decision-making for autonomous driving
Creators: name: Geary, Jack
Publisher: name: The University of Edinburgh
Year: 2022
Subjects: subject: autonomous vehicles
subject: interactive decision-making
subject: egoistic models
subject: non-egoistic models
subject: Game Theoretic methods
subject: Game Theoretic approaches
subject: altrusim
subject: Stackelberg Game
subject: leader-follower
subject: Conflict
subject: Area of Conflict
subject: Active Preference Learning

description: As technology improves, we are fast approaching a time when autonomous vehicles could become a common occurrence on the road. As a consequence of this, it will be necessary for autonomous vehicles to be able to coordinate with other road vehicles, both autonomous- and human-controlled. Coordination between human-controlled ve- hicles benefits from human drivers knowing to use and being able to interpret the al- truistic inclinations of the other drivers, based on their observed behaviours. This can include being inclined to let another car merge ahead, or the inclination to let another car cross through an intersection. This awareness is especially important in congested environments, where the interactions must be completed efficiently and the intentions must be unambiguously understood. In order for autonomously-controlled vehicles to be able to behave appropriately in these environments, such altruistic inclinations must be accounted for when planning their behaviour. In this work we propose two classes of methods for interactive decision-making; egoistic models, which are decision-making models that are parametrised by the de- gree of the decision-maker’s altruistic tendency, and non-egoistic models, in which the altruistic inclinations of all agents involved in the interaction parametrise the decision- making. Recent approaches to interactive decision-making have adapted Game Theo- retic methods in order to address the interaction in an interpretable fashion. Conven-tional Game Theoretic approaches often neglect the altruistic inclinations of players in addressing interactions. We adapt a concept, altrusim, from the Game Theory lit- erature to define egoistic and non-egoistic decision-making models, and demonstrate how such models can be designed so as to motivate the execution of actions that both exploit and provide information about the altruistic intentions of another driver. We further demonstrate the efficacy of each method in modelling human decision-making behaviour in simulated interactive driving scenarios. One assumption that is necessary for the implementation of a popular type of Game Theoretic method, the Stackelberg Game, is a “leader-follower” assumption. In the first part of this work we define an issue, Conflict which can arise when methods of this type are applied in decision-making scenarios where this assumption does not hold. We demonstrate how such an issue can arise in common autonomous driving situations, and how this issue can compromise a vehicle’s ability to effectively coordinate with other interactive vehicles in a series of simulated experiments. In order to quantify the incidence of this issue, we introduce a measure, Area of Conflict, that measures how often we would expect this issue to arise in interactions between vehicles controlled by randomly parametrised egoistic models. We demonstrate how our proposed non- egoistic model minimises the expected incidence of Conflict when compared against alternative conventional models. We further demonstrate how Active Preference Learning methods can be used to extend our proposed model definition in order to motivate choosing actions that in- duce predictable behaviour in another vehicle in an interactive setting so as to gather information about the altruistic intentions of the driver. By observing the behaviours chosen in a simulated lane merge scenario, we demonstrate how our proposed method of motivating actions by the information they would provide about the reward values in the game matrix achieves more preferable outcomes than conventional alternatives from the literature. In the second part of our work we examine how well egoistic and non-egoistic models can model human decision-making in simulated interactive driving scenar- ios. We collect data from human subjects in a simulation experiment and demonstrate that altruism-based decision-making models capture the qualitative features of human decision-making better than conventional Game Theoretic models. Our investigation provides evidence that egoistic models can approximate human decisions in 2-person interactive driving scenarios where the altruistic tendency of the other driver is known. We further provide evidence that non-egoistic models can better approximate human decision-making in 2-person interactive driving situations when the altruistic tendency of the other driver is unknown.