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 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.

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“…With electronics shrinking in size and requisite reductions in unit cost, a larger set of vehicle operation parameters are being stored onboard for diagnostic purposes. Furthermore, the CAN Bus enables the possibility to extract data from specific ECUs for the purposes of monitoring driver behaviour (Wang et al, 2020;Evin et al, 2020). This opens up the possibility of collecting data on vehicle usage as well as degradation over time.…”

Conceptual Modeling of Extended Collision Warning System from the Perspective of Smart Product-Service System

“…Liu et al proposed a comprehensive evaluation method based on the rough best-worst method (BWM) and TODIM (an acronym in Portuguese of Interactive and Multi-Criteria Decision Making) from the sustainability perspective of smart PSS and considered the bounded rationality and the judgment vagueness of decision makers [34]. Wu et al (2020) built a conceptual model of smart PSS based on the TRIZ function model [15].…”

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“…• • Fuel consumption prediction The collision alert system was proposed in Wang et al [7] and Lee, Lee and Kim [25]. According to Wang, hitting a vehicle rear is the most common traffic accident.…”

“…They could be close to each other and drivers do not have enough reaction time, or the rear vehicle driver shows lack of attention and does not notice the front vehicle braking. The work proposed by Wang et al [7] discussed an improvement in the collision alert system based on vehicle's network existing data, such as vehicle speed, use of brake pedal position, use of acceleration pedal position and steering angle. Their improved proposal consists of anticipating possible braking moments and increase alert sound signal in order to prevent driver inattention moments.…”