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...
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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 |
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