elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

A Bayesian Approach with Prior Mixed Strategy Nash Equilibrium for Vehicle Intention Prediction

Lucente, Giovanni and Dariani, Reza and Schindler, Julian and Ortgiese, Michael (2023) A Bayesian Approach with Prior Mixed Strategy Nash Equilibrium for Vehicle Intention Prediction. Automotive Innovation. Springer Nature. ISSN 2096-4250.

WarningThere is a more recent version of this item available.

[img] PDF - Only accessible within DLR - Postprint version (accepted manuscript)
1MB

Abstract

The state-of-the-art technology in the field of vehicle automation will lead to a mixed traffic environment in the coming years where Connected and Automated Vehicles (CAVs) have to interact with Human-Driven ones (HVs). In this context, it is necessary to have intention prediction models with the capability of forecasting how the traffic scenario is going to evolve with respect to the physical state of vehicles, the possible maneuvers and the interactions between the traffic participants within the seconds to come. This article presents a Bayesian approach for vehicle intention forecasting, proposing as prior estimate a game-theoretic framework in the form of a Mixed Strategy Nash Equilibrium (MSNE) to model the reciprocal influence between traffic participants. The likelihood is then computed based on the Kullback-Leibler divergence. The game is modeled as a static nonzero-sum polymatrix game with individual preferences, a well known strategic game. Finding the MSNE for these games is in the PPAD \ PLS complexity class, with polynomial-time tractability. The approach shows good results in simulation in the long term horizon (10s), with its computational complexity allowing for online applications.

Item URL in elib:https://elib.dlr.de/195305/
Document Type:Article
Title:A Bayesian Approach with Prior Mixed Strategy Nash Equilibrium for Vehicle Intention Prediction
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Lucente, GiovanniUNSPECIFIEDhttps://orcid.org/0000-0002-7844-853XUNSPECIFIED
Dariani, RezaUNSPECIFIEDhttps://orcid.org/0000-0002-1091-8793UNSPECIFIED
Schindler, JulianUNSPECIFIEDhttps://orcid.org/0000-0001-5398-8217UNSPECIFIED
Ortgiese, MichaelUNSPECIFIEDhttps://orcid.org/0000-0003-4616-7327UNSPECIFIED
Date:2023
Journal or Publication Title:Automotive Innovation
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Publisher:Springer Nature
ISSN:2096-4250
Status:Accepted
Keywords:Vehicle Intention Prediction · Trajectory Prediction · Bayesian Approach · Mixed Strategy Nash Equilibrium
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - NGC KoFiF (old)
Location: Berlin-Adlershof , Braunschweig
Institutes and Institutions:Institute of Transportation Systems
Institute of Transportation Systems > Cooperative Systems, BS
Institute of Transportation Systems > Administration TS, BA
Deposited By: Lucente, Giovanni
Deposited On:21 Jun 2023 10:46
Last Modified:22 Jun 2023 10:24

Available Versions of this Item

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Website and database design: Copyright © German Aerospace Center (DLR). All rights reserved.