Driver Modeling through Deep Reinforcement Learning and Behavioral Game Theory
This work addresses the need for efficient testing of autonomous vehicles by providing a modeling framework to simulate human decision-making in traffic, potentially reducing the millions of miles required for safety validation.
The paper tackles the problem of modeling driver behavior for autonomous vehicle validation by combining deep reinforcement learning and hierarchical game theory, demonstrating fidelity through comparison with real human driver data.
In this paper, a synergistic combination of deep reinforcement learning and hierarchical game theory is proposed as a modeling framework for behavioral predictions of drivers in highway driving scenarios. The need for a modeling framework that can address multiple human-human and human-automation interactions, where all the agents can be modeled as decision makers simultaneously, is the main motivation behind this work. Such a modeling framework may be utilized for the validation and verification of autonomous vehicles: It is estimated that for an autonomous vehicle to reach the same safety level of cars with drivers, millions of miles of driving tests are required. The modeling framework presented in this paper may be used in a high-fidelity traffic simulator consisting of multiple human decision makers to reduce the time and effort spent for testing by allowing safe and quick assessment of self-driving algorithms. To demonstrate the fidelity of the proposed modeling framework, game theoretical driver models are compared with real human driver behavior patterns extracted from traffic data.