A driver’s car-following behavior prediction model based on multi-sensors data

Abstract The prerequisite for the effective operation of vehicle collision warning system is that the necessary operation is not implemented. Therefore, the behavior prediction that the driver should perform when the preceding vehicle braking is the key to improve the effectiveness of the warning sy...

Full description

Bibliographic Details
Main Authors: Hui Wang, Menglu Gu, Shengbo Wu, Chang Wang
Format: Article
Language:English
Published: SpringerOpen 2020-01-01
Series:EURASIP Journal on Wireless Communications and Networking
Subjects:
Online Access:https://doi.org/10.1186/s13638-020-1639-2
id doaj-b113259ba04b40ff9474a77b6aa96525
record_format Article
spelling doaj-b113259ba04b40ff9474a77b6aa965252021-01-10T12:43:50ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992020-01-012020111210.1186/s13638-020-1639-2A driver’s car-following behavior prediction model based on multi-sensors dataHui Wang0Menglu Gu1Shengbo Wu2Chang Wang3School of Automobile, Chang’an UniversitySchool of Automobile, Chang’an UniversitySchool of Automobile, Chang’an UniversitySchool of Automobile, Chang’an UniversityAbstract The prerequisite for the effective operation of vehicle collision warning system is that the necessary operation is not implemented. Therefore, the behavior prediction that the driver should perform when the preceding vehicle braking is the key to improve the effectiveness of the warning system. This study was conducted to acquire characteristics in the car-following behavior when confronted by the braking of the preceding vehicle, including the reaction time and operation behavior, and establish a behavior prediction model. A driving experiment on the expressway was conducted using devices, such as millimeter-wave radars and controller area network (CAN) bus data, to acquire 845 segments of car following when the brake lamps of the car ahead are on. Data analysis demonstrates that the mean of time distance of car following, mean of car-following distance, and time-to-collision (TTC) mean are closely related with whether or not the driver slowed the car down. The operation states of the driver were divided into keeping the unchanged state of the degree of accelerator pedal opening, loosening of accelerator pedal without braking, braking, and other special situations with the input variables of car-following distance, speed of driver’s car, relative speed, time distance, and TTC using the support vector machine (SVM) method to build a prediction model for the operation behavior of the driver. The verification result showed that the model predicts driving behavior with an accuracy rate of 80%. It reflects the actual decision-making process of the driver, especially the normal operation of the driver, to loosen the accelerator pedal without braking. This model can help to optimize the algorithm of the rear-end accident warning system and improve intelligent system acceptance.https://doi.org/10.1186/s13638-020-1639-2Machine learningCar followingSensor dataPrediction modelTime-to-collisionAbbreviationsCAN Controller area network
collection DOAJ
language English
format Article
sources DOAJ
author Hui Wang
Menglu Gu
Shengbo Wu
Chang Wang
spellingShingle Hui Wang
Menglu Gu
Shengbo Wu
Chang Wang
A driver’s car-following behavior prediction model based on multi-sensors data
EURASIP Journal on Wireless Communications and Networking
Machine learning
Car following
Sensor data
Prediction model
Time-to-collisionAbbreviations
CAN Controller area network
author_facet Hui Wang
Menglu Gu
Shengbo Wu
Chang Wang
author_sort Hui Wang
title A driver’s car-following behavior prediction model based on multi-sensors data
title_short A driver’s car-following behavior prediction model based on multi-sensors data
title_full A driver’s car-following behavior prediction model based on multi-sensors data
title_fullStr A driver’s car-following behavior prediction model based on multi-sensors data
title_full_unstemmed A driver’s car-following behavior prediction model based on multi-sensors data
title_sort driver’s car-following behavior prediction model based on multi-sensors data
publisher SpringerOpen
series EURASIP Journal on Wireless Communications and Networking
issn 1687-1499
publishDate 2020-01-01
description Abstract The prerequisite for the effective operation of vehicle collision warning system is that the necessary operation is not implemented. Therefore, the behavior prediction that the driver should perform when the preceding vehicle braking is the key to improve the effectiveness of the warning system. This study was conducted to acquire characteristics in the car-following behavior when confronted by the braking of the preceding vehicle, including the reaction time and operation behavior, and establish a behavior prediction model. A driving experiment on the expressway was conducted using devices, such as millimeter-wave radars and controller area network (CAN) bus data, to acquire 845 segments of car following when the brake lamps of the car ahead are on. Data analysis demonstrates that the mean of time distance of car following, mean of car-following distance, and time-to-collision (TTC) mean are closely related with whether or not the driver slowed the car down. The operation states of the driver were divided into keeping the unchanged state of the degree of accelerator pedal opening, loosening of accelerator pedal without braking, braking, and other special situations with the input variables of car-following distance, speed of driver’s car, relative speed, time distance, and TTC using the support vector machine (SVM) method to build a prediction model for the operation behavior of the driver. The verification result showed that the model predicts driving behavior with an accuracy rate of 80%. It reflects the actual decision-making process of the driver, especially the normal operation of the driver, to loosen the accelerator pedal without braking. This model can help to optimize the algorithm of the rear-end accident warning system and improve intelligent system acceptance.
topic Machine learning
Car following
Sensor data
Prediction model
Time-to-collisionAbbreviations
CAN Controller area network
url https://doi.org/10.1186/s13638-020-1639-2
work_keys_str_mv AT huiwang adriverscarfollowingbehaviorpredictionmodelbasedonmultisensorsdata
AT menglugu adriverscarfollowingbehaviorpredictionmodelbasedonmultisensorsdata
AT shengbowu adriverscarfollowingbehaviorpredictionmodelbasedonmultisensorsdata
AT changwang adriverscarfollowingbehaviorpredictionmodelbasedonmultisensorsdata
AT huiwang driverscarfollowingbehaviorpredictionmodelbasedonmultisensorsdata
AT menglugu driverscarfollowingbehaviorpredictionmodelbasedonmultisensorsdata
AT shengbowu driverscarfollowingbehaviorpredictionmodelbasedonmultisensorsdata
AT changwang driverscarfollowingbehaviorpredictionmodelbasedonmultisensorsdata
_version_ 1724342351656124416