A Rear-End Collision Risk Evaluation and Control Scheme Using a Bayesian Network Model

Chen Chen, Xiaomin Liu, Hsiao-Hwa Chen, Meilian Li, Liqiang Zhao

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

This paper presents a probabilistic decision-making framework for rear-end collision avoidance systems, focusing on modeling the impact of major collision-causing factors on the occurrence of accidents. Decisions on when and how to assist drivers are made using a Bayesian network approach according to collision risk evaluation results, given a prior probabilistic knowledge. The structure of the Bayesian network model is learnt using a K2 algorithm with a practical dataset. To provide adequate response time for drivers, we also predict collision probability in the next monitoring interval using a Kalman filter model. The prediction accuracy is evaluated with different use cases and compared to real scenarios with the obtained dataset. To make our framework more relevant to practical applications, we also discuss the corresponding safety control strategies by classifying collision risk into high and low levels. In addition, the proposed model is evaluated through experiments including simulations and road tests. In the simulations, the algorithms are tested in different scenarios with various configurations of weather conditions, driver response capability, and vehicular dynamics. In order to demonstrate collision avoidance performance, the proposed model is also compared to existing schemes. In the road tests, the algorithm is embedded into an unmanned vehicle with predefined parameters to evaluate the impact of computational complexity on rear-end collision avoidance. Numerical results show that the proposed model provides an accurate estimation for car-following collision risk with a relatively low complexity, taking into account the impacts of vehicle dynamics, driver reaction capacity, and external environment on rear-end collisions.

Original languageEnglish
Article number8363015
Pages (from-to)264-284
Number of pages21
JournalIEEE Transactions on Intelligent Transportation Systems
Volume20
Issue number1
DOIs
Publication statusPublished - 2019 Jan 1

Fingerprint

Bayesian networks
Collision avoidance
Unmanned vehicles
Kalman filters
Computational complexity
Accidents
Railroad cars
Decision making
Monitoring
Experiments

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

Cite this

@article{faf64839bc5549d9a6fe6f8104769c84,
title = "A Rear-End Collision Risk Evaluation and Control Scheme Using a Bayesian Network Model",
abstract = "This paper presents a probabilistic decision-making framework for rear-end collision avoidance systems, focusing on modeling the impact of major collision-causing factors on the occurrence of accidents. Decisions on when and how to assist drivers are made using a Bayesian network approach according to collision risk evaluation results, given a prior probabilistic knowledge. The structure of the Bayesian network model is learnt using a K2 algorithm with a practical dataset. To provide adequate response time for drivers, we also predict collision probability in the next monitoring interval using a Kalman filter model. The prediction accuracy is evaluated with different use cases and compared to real scenarios with the obtained dataset. To make our framework more relevant to practical applications, we also discuss the corresponding safety control strategies by classifying collision risk into high and low levels. In addition, the proposed model is evaluated through experiments including simulations and road tests. In the simulations, the algorithms are tested in different scenarios with various configurations of weather conditions, driver response capability, and vehicular dynamics. In order to demonstrate collision avoidance performance, the proposed model is also compared to existing schemes. In the road tests, the algorithm is embedded into an unmanned vehicle with predefined parameters to evaluate the impact of computational complexity on rear-end collision avoidance. Numerical results show that the proposed model provides an accurate estimation for car-following collision risk with a relatively low complexity, taking into account the impacts of vehicle dynamics, driver reaction capacity, and external environment on rear-end collisions.",
author = "Chen Chen and Xiaomin Liu and Hsiao-Hwa Chen and Meilian Li and Liqiang Zhao",
year = "2019",
month = "1",
day = "1",
doi = "10.1109/TITS.2018.2813364",
language = "English",
volume = "20",
pages = "264--284",
journal = "IEEE Transactions on Intelligent Transportation Systems",
issn = "1524-9050",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "1",

}

A Rear-End Collision Risk Evaluation and Control Scheme Using a Bayesian Network Model. / Chen, Chen; Liu, Xiaomin; Chen, Hsiao-Hwa; Li, Meilian; Zhao, Liqiang.

In: IEEE Transactions on Intelligent Transportation Systems, Vol. 20, No. 1, 8363015, 01.01.2019, p. 264-284.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A Rear-End Collision Risk Evaluation and Control Scheme Using a Bayesian Network Model

AU - Chen, Chen

AU - Liu, Xiaomin

AU - Chen, Hsiao-Hwa

AU - Li, Meilian

AU - Zhao, Liqiang

PY - 2019/1/1

Y1 - 2019/1/1

N2 - This paper presents a probabilistic decision-making framework for rear-end collision avoidance systems, focusing on modeling the impact of major collision-causing factors on the occurrence of accidents. Decisions on when and how to assist drivers are made using a Bayesian network approach according to collision risk evaluation results, given a prior probabilistic knowledge. The structure of the Bayesian network model is learnt using a K2 algorithm with a practical dataset. To provide adequate response time for drivers, we also predict collision probability in the next monitoring interval using a Kalman filter model. The prediction accuracy is evaluated with different use cases and compared to real scenarios with the obtained dataset. To make our framework more relevant to practical applications, we also discuss the corresponding safety control strategies by classifying collision risk into high and low levels. In addition, the proposed model is evaluated through experiments including simulations and road tests. In the simulations, the algorithms are tested in different scenarios with various configurations of weather conditions, driver response capability, and vehicular dynamics. In order to demonstrate collision avoidance performance, the proposed model is also compared to existing schemes. In the road tests, the algorithm is embedded into an unmanned vehicle with predefined parameters to evaluate the impact of computational complexity on rear-end collision avoidance. Numerical results show that the proposed model provides an accurate estimation for car-following collision risk with a relatively low complexity, taking into account the impacts of vehicle dynamics, driver reaction capacity, and external environment on rear-end collisions.

AB - This paper presents a probabilistic decision-making framework for rear-end collision avoidance systems, focusing on modeling the impact of major collision-causing factors on the occurrence of accidents. Decisions on when and how to assist drivers are made using a Bayesian network approach according to collision risk evaluation results, given a prior probabilistic knowledge. The structure of the Bayesian network model is learnt using a K2 algorithm with a practical dataset. To provide adequate response time for drivers, we also predict collision probability in the next monitoring interval using a Kalman filter model. The prediction accuracy is evaluated with different use cases and compared to real scenarios with the obtained dataset. To make our framework more relevant to practical applications, we also discuss the corresponding safety control strategies by classifying collision risk into high and low levels. In addition, the proposed model is evaluated through experiments including simulations and road tests. In the simulations, the algorithms are tested in different scenarios with various configurations of weather conditions, driver response capability, and vehicular dynamics. In order to demonstrate collision avoidance performance, the proposed model is also compared to existing schemes. In the road tests, the algorithm is embedded into an unmanned vehicle with predefined parameters to evaluate the impact of computational complexity on rear-end collision avoidance. Numerical results show that the proposed model provides an accurate estimation for car-following collision risk with a relatively low complexity, taking into account the impacts of vehicle dynamics, driver reaction capacity, and external environment on rear-end collisions.

UR - http://www.scopus.com/inward/record.url?scp=85047608911&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85047608911&partnerID=8YFLogxK

U2 - 10.1109/TITS.2018.2813364

DO - 10.1109/TITS.2018.2813364

M3 - Article

AN - SCOPUS:85047608911

VL - 20

SP - 264

EP - 284

JO - IEEE Transactions on Intelligent Transportation Systems

JF - IEEE Transactions on Intelligent Transportation Systems

SN - 1524-9050

IS - 1

M1 - 8363015

ER -