Driving style imitation in simulated car racing using style evaluators and multi-objective evolution of a fuzzy logic controller
Tsaipei Wang, Keng-Te Liaw · 2014
This paper describes a new approach to driving style imitation in simulated car racing games. Our goal is to be able to create non-personal characters (NPCs) that both run competitively and exhibit some driving style traits of the player being imitated. We introduce a style evaluator function that can measure the style similarity between driving records even from different tracks. The effectiveness of such style evaluators are verified using driving records of both NPCs and human players. To build NPC drivers that can imitate particular human players, we use a base driver AI based on a fuzzy logic controller and optimizes its parameters using multi-objective evolution. This is the first work on driver imitation that actually allows several human players to drive in their only natural, not instructed, styles. Our results show evidences that the created imitator NPCs do possess traits of styles of the respective human players being modeled.