AIBA: An AI Model for Behavior Arbitration in Autonomous Driving
This addresses the problem of safe and understandable decision-making for autonomous driving systems, though it appears incremental as it builds on existing simulation and modeling approaches.
The paper tackles the challenge of behavior arbitration for autonomous vehicles in dynamic traffic by proposing AIBA, an AI model that mimics human cognition and uses formal modeling to ensure functional safety, achieving performance validated in simulation environments VTD and GridSim.
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.