Inverse reinforcement learning based driver behavior analysis and fuel economy assessment

Mehmet Fatih Ozkan, Yao Ma

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Human drivers have different driver behaviors when operating vehicles. These driving behaviors, including the driver's preferred speed and rate of acceleration, impose a major impact on vehicle fuel consumption consequently. In this study, we proposed a feature-based driver behavior learning model from demonstrated driving data utilizing the Inverse Reinforcement Learning (IRL) approach to analyze various driver behaviors and their impacts on vehicle fuel consumption. The proposed approach models the individual driving style as cost function which is a linear combination of the features and their corresponding weights. The proposed IRL framework is used to find the model parameters that fit the observed driving style best. By using the learned driving behavior model, the most likely trajectories are computed and the optimized feature weights are used to analyze different driver behaviors. The different driver behaviors and their impacts on vehicle fuel consumption are then analyzed in real-world driving scenarios. Results show that the proposed IRL framework can successfully learn individual driver behaviors using vehicle trajectory data demonstrated by different real drivers. The learned driver behaviors promise a significant correlation between driving behavior and fuel consumption.

Original languageEnglish
Title of host publicationAdaptive/Intelligent Sys. Control; Driver Assistance/Autonomous Tech.; Control Design Methods; Nonlinear Control; Robotics; Assistive/Rehabilitation Devices; Biomedical/Neural Systems; Building Energy Systems; Connected Vehicle Systems; Control/Estimation of Energy Systems; Control Apps.; Smart Buildings/Microgrids; Education; Human-Robot Systems; Soft Mechatronics/Robotic Components/Systems; Energy/Power Systems; Energy Storage; Estimation/Identification; Vehicle Efficiency/Emissions
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791884270
DOIs
StatePublished - 2020
EventASME 2020 Dynamic Systems and Control Conference, DSCC 2020 - Virtual, Online
Duration: Oct 5 2020Oct 7 2020

Publication series

NameASME 2020 Dynamic Systems and Control Conference, DSCC 2020
Volume1

Conference

ConferenceASME 2020 Dynamic Systems and Control Conference, DSCC 2020
CityVirtual, Online
Period10/5/2010/7/20

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