Decision-Making Framework for Automated Driving in Highway Environments

Abstract

This paper presents a decision-making framework for automated driving in highway environments. The framework is capable of reliably, robustly assessing a given highway situation (with respect to the possibility of collision) and of automatically determining an appropriate maneuver for the situation. It consists of two main components: situation assessment and strategy decision. The situation assessment component utilizes multiple complementary “threat measures” and Bayesian networks in its calculations of “threat levels” at the car and lane level to evaluate the possibility of collisions for a given highway traffic situation. The strategy decision component, designed to generate goal-directed and collision-free behaviors, automatically determines an appropriate maneuver in a given highway situation via a hierarchical state machine—such a machine both reduces the complexity of and extends a strategy model. The types of maneuver determined by the component include both simple maneuvers, such as slowing down to avoid collision with a vehicle in front, and complex maneuvers, such as lane changes and overtaking. The presented decision-making framework is tested and evaluated—both on a closed high-speed test track in simulated traffic with various driving scenarios and on public highways in real traffic through in-vehicle testing—to verify that it can provide sufficiently reliable performance for automated driving in highway environments in terms of safety, reliability, and robustness.