AIBA: An AI Model for Behavior Arbitration in Autonomous Driving

TL;DR

AIBA introduces a novel AI behavior arbitration algorithm for autonomous driving that mimics human cognition and formalizes scene understanding to enhance decision-making and ensure safety in complex traffic environments.

Contribution

The paper presents a new AI arbitration method that combines human-like scene understanding with formal modeling for autonomous vehicles.

Findings

Effective in virtual traffic simulations

Enables analytical safety inference

Improves decision-making in complex scenarios

Abstract

Driving in dynamically changing traffic is a highly challenging task for autonomous vehicles, especially in crowded urban roadways. The Artificial Intelligence (AI) system of a driverless car must be able to arbitrate between different driving strategies in order to properly plan the car's path, based on an understandable traffic scene model. In this paper, an AI behavior arbitration algorithm for Autonomous Driving (AD) is proposed. The method, coined AIBA (AI Behavior Arbitration), has been developed in two stages: (i) human driving scene description and understanding and (ii) formal modelling. The description of the scene is achieved by mimicking a human cognition model, while the modelling part is based on a formal representation which approximates the human driver understanding process. The advantage of the formal representation is that the functional safety of the system can be analytically inferred. The performance of the algorithm has been evaluated in Virtual Test Drive (VTD), a comprehensive traffic simulator, and in GridSim, a vehicle kinematics engine for prototypes.