Multi-parameter prediction of drivers' lane-changing behaviour with neural network model

Peng, Jinshuan; Guo, Yingshi; Fu, Rui; Yuan, Wei; Wang, Chang

doi:10.1016/j.apergo.2015.03.017

Cited by 139 publications

(58 citation statements)

References 33 publications

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“…Furthermore, the acceleration behavior during the lane change was investigated. The results validated those of a previous study [40], which reported that the driving speed is kept constant during the lane change. Therefore, we simplified the lane-change decision model by ignoring the car's speed change during the lane-change process.…”

Section: Discussionsupporting

confidence: 89%

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Human-Like Lane Change Decision Model for Autonomous Vehicles that Considers the Risk Perception of Drivers in Mixed Traffic

Wang

Sun

et al. 2020

Sensors

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Determining an appropriate time to execute a lane change is a critical issue for the development of Autonomous Vehicles (AVs).However, few studies have considered the rear and the front vehicle-driver's risk perception while developing a human-like lane-change decision model. This paper aims to develop a lane-change decision model for AVs and to identify a two level threshold that conforms to a driver's perception of the ability to safely change lanes with a rear vehicle approaching fast. Based on the signal detection theory and extreme moment trials on a real highway, two thresholds of safe lane change were determined with consideration of risk perception of the rear and the subject vehicle drivers, respectively. The rear vehicle's Minimum Safe Deceleration (MSD) during the lane change maneuver of the subject vehicle was selected as the lane change safety indicator, and was calculated using the proposed human-like lane-change decision model. The results showed that, compared with the driver in the front extreme moment trial, the driver in the rear extreme moment trial is more conservative during the lane change process. To meet the safety expectations of the subject and rear vehicle drivers, the primary and secondary safe thresholds were determined to be 0.85 m/s 2 and 1.76 m/s 2 , respectively. The decision model can help make AVs safer and more polite during lane changes, as it not only improves acceptance of the intelligent driving system, but also further ensures the rear vehicle's driver's safety.interval from 3 m to 25 m, from 25 m to 45 m, and from 45 m to 70 m. Wakasugi [20] recommended a two-level TTC threshold of 3 s and 5 s, respectively. However, the computed TTC threshold would be easily influenced by the relative speed and distance between the rear vehicle and the subject vehicle.The remainder of the paper is organized as follows. Related works on lane-change decision models and safety indicator thresholds are introduced in Section 2. Section 3 introduces the naturalistic lane-change trial and extreme moment trials. Section 4 presents the lane-change decision model and the calibration parameters of the model. The two-level threshold based on the proposed model is determined in Section 5. Finally, a discussion and conclusion are presented in Section 6. MethodOn-road experiment is the main research method used in this paper, and the experiments include the naturalistic lane-change trial and extreme moment trials. The purpose of the naturalistic trial is to calibrate the parameters of the proposed lane-change decision model. The extreme moment trials are used to accurately capture the variation of driver cognition characteristics of lane-change safety. In this section, we willintroduce the required equipment, participants, test route and procedures for the experiments in detail. ApparatusThe test vehicle is depicted in Figure 1. The test vehicle used in our experiments was a 2008 Volkswagen Touran, equipped with a Lane Mark Recognition system (Mobileye C2-170, made by Mobileye Company, Jerusalem, Isra...

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Section: Discussionsupporting

confidence: 89%

“…A video monitoring system recorded the driver during the duration of the experiment. The intention to change lanes was detected by observing the driver's eye and head movement, the use of turn signal lamp, and the driving environment [40][41][42][43], which was recorded by the monitoring system.…”

Section: Naturalistic Lane-change Trialmentioning

confidence: 99%

Human-Like Lane Change Decision Model for Autonomous Vehicles that Considers the Risk Perception of Drivers in Mixed Traffic

Wang

Sun

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

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“…The model uses the standard BP algorithm, the transfer function from the input layer to the hidden layer is the bipolar function ( ) = 1/(1 + − ), and the transfer function from the hidden layer to the output layer is the linear function ( ) = . Many literature references show that the BP neural network algorithm is mature [40,41], so the specific LV SV Figure 7: The rear-end collision scenario.…”

Section: Estimation Of the Driver's Reaction Timementioning

confidence: 99%

Key Parameters Estimation and Adaptive Warning Strategy for Rear-End Collision of Vehicle

Song

Zhang

2015

Mathematical Problems in Engineering

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The rear-end collision warning system requires reliable warning decision mechanism to adapt the actual driving situation. To overcome the shortcomings of existing warning methods, an adaptive strategy is proposed to address the practical aspects of the collision warning problem. The proposed strategy is based on the parameter-adaptive and variable-threshold approaches. First, several key parameter estimation algorithms are developed to provide more accurate and reliable information for subsequent warning method. They include a two-stage algorithm which contains a Kalman filter and a Luenberger observer for relative acceleration estimation, a Bayesian theory-based algorithm of estimating the road friction coefficient, and an artificial neural network for estimating the driver’s reaction time. Further, the variable-threshold warning method is designed to achieve the global warning decision. In the method, the safety distance is employed to judge the dangerous state. The calculation method of the safety distance in this paper can be adaptively adjusted according to the different driving conditions of the leading vehicle. Due to the real-time estimation of the key parameters and the adaptive calculation of the warning threshold, the strategy can adapt to various road and driving conditions. Finally, the proposed strategy is evaluated through simulation and field tests. The experimental results validate the feasibility and effectiveness of the proposed strategy.

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“…The variables of road curvature, lane position, steering wheel angle, lateral acceleration, collision time, and so on were combined, and the artificial neural network model and support vector machine were used to predict the driver's behavior. In the research of J Peng et al, 23 a prediction index system for left lane change was constructed by considering drivers' visual search behaviors, vehicle operation behaviors, vehicle motion states, and driving conditions. And a back-propagation neural network model was developed to predict lane-changing behavior.…”

Section: Introductionmentioning

confidence: 99%

Effect analysis of emotions on driving intention in two-lane environment

Wang

Liu

Han

et al. 2019

Advances in Mechanical Engineering

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Driving emotion plays an important role in intention generation and behavior decision. It is an important premise to understand the influencing mechanism of driving emotion on intention correctly for the realization of driving intention identification, active vehicle security warning, and intelligent driving. In the case of two-lane, multi-source dynamic data of human-vehicle-environment under drivers' different emotional states is obtained through actual and virtual driving experiment in this article. Considering the influence of emotion on intention, the probability distributions of the driving intention in different emotions are obtained by means of the immune algorithm. And the priming effect of vehicle driving emotion on intention was analyzed deeply. The research results can provide theoretical foundation for the research of driving intention identification, active vehicle security warning system, and intelligent driving command system under the condition of Internet of things.

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Multi-parameter prediction of drivers' lane-changing behaviour with neural network model

Cited by 139 publications

References 33 publications

Human-Like Lane Change Decision Model for Autonomous Vehicles that Considers the Risk Perception of Drivers in Mixed Traffic

Human-Like Lane Change Decision Model for Autonomous Vehicles that Considers the Risk Perception of Drivers in Mixed Traffic

Key Parameters Estimation and Adaptive Warning Strategy for Rear-End Collision of Vehicle

Effect analysis of emotions on driving intention in two-lane environment

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