Empirical Game Theoretic Models for Autonomous Driving: Methods and Applications

In recent years, there has been enormous public interest in autonomous vehicles (AV), with more than 80 billion dollars invested in self-driving car technology. However, for the foreseeable future, self-driving cars will interact with human driven vehicles and other human road users, such as pedestrians and cyclists. Therefore, in order to ensure safe operation of AVs, there is need for computational models of humans traffic behaviour that can be used for testing and verification of autonomous vehicles. Game theoretic models of human driving behaviour is a promising computational tool that can be used in many phases of AV development. However, traditional game theoretic models are typically built around the idea of rationality, i.e., selection of the most optimal action based on individual preferences. In reality, not only is it hard to infer diverse human preferences from observational data, but real-world traffic shows that humans rarely choose the most optimal action that a computational model suggests. The thesis makes a set of methodological contributions towards modelling sub-optimality in driving behaviour within a game theoretic framework. These include solution concepts that account for boundedly rational behaviour in hierarchical games, addressing challenges of bounded rationality in dynamic games, and estimation of multi-objective utility aggregation from observational data. Each of these contributions are evaluated based on a novel multi-agent traffic dataset. Building on the game theoretic models, the second part of the thesis demonstrates the application of the models by developing novel safety validation methodologies for testing AV planners. The first application is an automated generation of interpretable variations of lane change behaviour based on Quantal Best Response model. The proposed model is shown to be effective for generating both rare-event situations and to replicate the typical behaviour distribution observed in naturalistic data. The second application is safety validation of strategic planners in situations of dynamic occlusion. Using the concept of hypergames, in which different agents have different views of the game, the thesis develops a new safety surrogate metric, dynamic occlusion risk (DOR), that can be used to evaluate the risk associated with each action in situations of dynamic occlusion. The thesis concludes with a taxonomy of strategic interactions that maps complex design specific strategies in a game to a simpler taxonomy of traffic interactions. Regulations around what strategies an AV should execute in traffic can be developed over the simpler taxonomy, and a process of automated mapping can protect the proprietary design decisions of an AV manufacturer.