Development of a Novel Driver Model Offering Human like Longitudinal Vehicle Control in Order to Simulate Emission in Real Driving Conditions

Abstract

Toyota would like to simulate emissions in real-world conditions and support future engine development newly regulated by Real Driving Emission from 2017. A realistic driver model is necessary to simulate representative vehicle emissions. This paper presents a new driver model trained using real-world data including GPS localization and recorded engine ECU parameters. From a geolocalisation webservice, the proposed approach extracts the road attributes that influence human driving behaviour such as traffic signs, road cross, etc. The novel BiMap innovative algorithm, is then used to learn and map the driver behaviour with respect to the road properties while a regression tree algorithm is used to learn a realistic gear selection model. Experimental tests, executed within Carmaker™ vehicle simulation platform, show that the resulting model can drive along arbitrary real-world routes, generated using a map service. Moreover, it exhibits a human-like driving behaviour while being robust to different car setups. Finally, the realism of the proposed driver’s behaviour is supported by both a high similarity in Engine Operative Point usage and a less than 1.5% deviation in terms CO2 emission versus measured data.