5 research outputs found
AIBA: An AI Model for Behavior Arbitration in Autonomous Driving
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.Comment: 12 page