Driver Modeling through Deep Reinforcement Learning and Behavioral Game Theory

by   Berat Mert Albaba, et al.

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.


page 13

page 14

page 15

page 16

page 17

page 18

page 19

page 20


Energy-Efficient Autonomous Driving Using Cognitive Driver Behavioral Models and Reinforcement Learning

Autonomous driving technologies are expected to not only improve mobilit...

Modeling Human Driver Interactions Using an Infinite Policy Space Through Gaussian Processes

This paper proposes a method for modeling human driver interactions that...

A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learning

This paper serves as an introduction and overview of the potentially use...

Game-Theoretic Modeling of Multi-Vehicle Interactions at Uncontrolled Intersections

Motivated by the need to develop simulation tools for verification and v...

Augmented Driver Behavior Models for High-Fidelity Simulation Study of Crash Detection Algorithms

Developing safety and efficiency applications for Connected and Automate...

Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Traffic

Multi-task intersection navigation including the unprotected turning lef...

A cognitive process approach to modeling gap acceptance in overtaking

Driving automation holds significant potential for enhancing traffic saf...

Please sign up or login with your details

Forgot password? Click here to reset