NDLI: Towards a unified traffic situation estimation model — Street-dependent behaviour and motion models

Content Provider	IEEE Xplore Digital Library
Author	Kuhnt, F. Kohlhaas, R. Schamm, T. Zollner, J.M.
Copyright Year	2015
Description	Author affiliation: Dept. of Tech. Cognitive Assistance Syst., FZI Res. Center for Inf. Technol., Karlsruhe, Germany (Kuhnt, F.; Kohlhaas, R.; Schamm, T.; Zollner, J.M.)
Abstract	For Advanced Driver Assistance Systems and Autonomous Driving, estimating and predicting traffic situations becomes more and more essential. Many approaches focus on one specific application like vehicle state estimation from sensor data or road model estimation from environment perception. To integrate single approaches to one coherent system, one unified model is needed where the existing applied algorithms can be grounded to. In this paper we propose a Unified Traffic Situation Estimation Model that describes the probabilistic dependencies between road elements. While its independence from time makes it usable for offline mapping tasks, we show that online prediction capabilities can be achieved by applying the model to a longitudinal vehicle state estimation problem: Using the Markov assumption and appropriate state spaces the general unified model can be specialized to an Interacting Multiple Model Filter. Finally, experiments show an improvement in state estimation and prediction over standard models, which only consider vehicle dynamics. Additionally the unified model allows the prediction of street related routes of vehicles.
Starting Page	1223
Ending Page	1229
File Size	464919
Page Count	7
File Format	PDF
e-ISBN	9780982443866
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	ISIF
Subject Keyword	Hidden Markov models Vehicles Bayes methods Predictive models Probabilistic logic Trajectory Vehicle dynamics
Content Type	Text
Resource Type	Article

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

A probabilistic discriminative approach for situation recognition in traffic scenarios

A probabilistic model for estimating driver behaviors and vehicle trajectories in traffic environments

A Bayesian filter for modeling traffic at stop intersections

Predictive ADAS: A predictive trajectory guidance scheme for advanced driver assistance in public traffic

Probabilistic situation recognition for vehicular traffic scenarios

Traffic interactions: Estimate driving behavior's influence

Improved tracking and behavior anticipation by combining street map information with Bayesian-filtering

Learning-based approach for online lane change intention prediction

A model-based approach to probabilistic situation assessment for driver assistance systems

Towards a unified traffic situation estimation model — Street-dependent behaviour and motion models

Similar Documents

A probabilistic discriminative approach for situation recognition in traffic scenarios

A probabilistic model for estimating driver behaviors and vehicle trajectories in traffic environments

A Bayesian filter for modeling traffic at stop intersections

Predictive ADAS: A predictive trajectory guidance scheme for advanced driver assistance in public traffic

Probabilistic situation recognition for vehicular traffic scenarios

Traffic interactions: Estimate driving behavior's influence

Improved tracking and behavior anticipation by combining street map information with Bayesian-filtering

Learning-based approach for online lane change intention prediction

A model-based approach to probabilistic situation assessment for driver assistance systems

Towards a unified traffic situation estimation model — Street-dependent behaviour and motion models