NDLI: Personalized Driver/Vehicle Lane Change Models for ADAS

Content Provider	IEEE Xplore Digital Library
Author	Butakov, V. Ioannou, P.
Copyright Year	1967
Abstract	Lane changes are stressful maneuvers for drivers, particularly during high-speed traffic flows. Advanced driver-assistance systems (ADASs) aim to assist drivers during lane change maneuvers. A system that is developed for an average driver or all drivers will have to be conservative for safety reasons to cover all driver/vehicle types. Such a conservative system may not be acceptable to aggressive drivers and could be perceived as too aggressive by the more passive drivers. An ADAS that takes into account the dynamics and characteristics of each individual vehicle/driver system during lane change maneuvers will be more effective and more acceptable to drivers without sacrificing safety. In this paper, we develop a methodology that learns the characteristics of an individual driver/vehicle response before and during lane changes and under different driving environments. These characteristics are captured by a set of models whose parameters are adjusted online to fit the individual vehicle/driver response during lane changes. We develop a two-layer model to describe the maneuver kinematics. The lower layer describes lane change as a kinematic model. The higher layer model establishes the kinematic model parameter values for the particular driver and represents their dependence on the configuration of the surrounding vehicles. The proposed modeling framework can be used as a kernel component of ADAS to provide more personalized recommendations to the driver, increasing the potential for more widespread acceptance and use of ADAS. We evaluated the proposed methodology using an actual vehicle and three different drivers. We demonstrated that the method is effective in modeling individual driver/vehicle responses during lane change by showing consistency of matching between the model outputs and raw data.
Sponsorship	IEEE Vehicular Technology Society
Starting Page	4422
Ending Page	4431
Page Count	10
File Size	902168
File Format	PDF
ISSN	00189545
Volume Number	64
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vehicles Hidden Markov models Kinematics Acceleration Adaptation models Trajectory Stochastic processes man machine systems intelligent vehicles machine learning
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Automotive Engineering Computer Networks and Communications Electrical and Electronic Engineering Aerospace Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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