Adaptive Robust Game-Theoretic Decision Making for Autonomous Vehicles

TL;DR

This paper introduces an adaptive robust decision-making framework for autonomous vehicles that manages uncertainties and driver behavior confidence to improve lane change safety and efficiency.

Contribution

It presents a novel adaptive robust decision-making strategy that accounts for model mismatches and driver behavior confidence in traffic scenarios.

Findings

Enhanced safety in lane change maneuvers.

Reduced conservativeness compared to traditional robust methods.

Effective handling of model uncertainties and driver behavior variability.

Abstract

In a typical traffic scenario, autonomous vehicles are required to share the road with other road participants, e.g., human driven vehicles, pedestrians, etc. To successfully navigate the traffic, a cognitive hierarchy theory such as level-k framework, can be used by the autonomous agents to categorize the agents based on their depth of strategic thought and act accordingly. However, mismatch between the vehicle dynamics and its predictions, and improper classification of the agents can lead to undesirable behavior or collision. Robust approaches can handle the uncertainties, however, might result in a conservative behavior of the autonomous vehicle. This paper proposes an adaptive robust decision making strategy for autonomous vehicles to handle model mismatches in the prediction model while utilizing the confidence of the driver behavior to obtain less conservative actions. The effectiveness of the proposed approach is validated for a lane change maneuver in a highway driving scenario.