A Novel Car-Following Control Model Combining Machine Learning and Kinematics Models for Automated Vehicles

Yang, Da; Zhu, Lei; Liu, Yalong; Wu, Danhong; Ran, Bin

doi:10.1109/tits.2018.2854827

Cited by 97 publications

(40 citation statements)

References 36 publications

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“…Expected speed means the maximum safe driving speed that the driver wants to achieve when the vehicle is running free from constraints of other vehicles [ 19 ]. Expected distance is a concept in the car-following theory [ 20 , 21 , 22 , 23 ]. The expected distance mentioned in this study was the distance from the head of the following car to the rear of the leading car in the car-following scene.…”

Section: Study 1-a: Driving Intention Prediction Model Based On Hmmentioning

confidence: 99%

Differences in Driving Intention Transitions Caused by Driver’s Emotion Evolutions

Liu

Wang

2020

IJERPH

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Joining worldwide efforts to understand the relationship between driving emotion and behavior, the current study aimed at examining the influence of emotions on driving intention transition. In Study 1, taking a car-following scene as an example, we designed the driving experiments to obtain the driving data in drivers’ natural states, and a driving intention prediction model was constructed based on the HMM. Then, we analyzed the probability distribution and transition probability of driving intentions. In Study 2, we designed a series of emotion-induction experiments for eight typical driving emotions, and the drivers with induced emotion participated in the driving experiments similar to Study 1. Then, we obtained the driving data of the drivers in eight typical emotional states, and the driving intention prediction models adapted to the driver’s different emotional states were constructed based on the HMM severally. Finally, we analyzed the probabilistic differences of driving intention in divers’ natural states and different emotional states, and the findings showed the changing law of driving intention probability distribution and transfer probability caused by emotion evolution. The findings of this study can promote the development of driving behavior prediction technology and an active safety early warning system.

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Section: Study 1-a: Driving Intention Prediction Model Based On Hmmentioning

confidence: 99%

Differences in Driving Intention Transitions Caused by Driver’s Emotion Evolutions

Liu

Wang

2020

IJERPH

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“…We use the open-source NGSIM dataset to verify the performance of the proposed hybrid method; the dataset was provided by the FHWA NGSIM project. This dataset has been widely used to develop and test various models [ 28 , 33 , 40 , 56 , 57 , 58 ]. A literature review indicated that most scholars investigated lane changes using the US-101 and I-80 dataset, but few had analyzed the turning behavior at intersections using this dataset.…”

Section: Experimental Datamentioning

confidence: 99%

A Hybrid Approach for Turning Intention Prediction Based on Time Series Forecasting and Deep Learning

Zhang

2020

Sensors

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At an intersection with complex traffic flow, the early detection of the intention of drivers in surrounding vehicles can enable advanced driver assistance systems (ADAS) to warn the driver in advance or prompt its subsystems to assess the risk and intervene early. Although different drivers show various driving characteristics, the kinematic parameters of human-driven vehicles can be used as a predictor for predicting the driver’s intention within a short time. In this paper, we propose a new hybrid approach for vehicle behavior recognition at intersections based on time series prediction and deep learning networks. First, the lateral position, longitudinal position, speed, and acceleration of the vehicle are predicted using the online autoregressive integrated moving average (ARIMA) algorithm. Next, a variant of the long short-term memory network, called the bidirectional long short-term memory (Bi-LSTM) network, is used to detect the vehicle’s turning behavior using the predicted parameters, as well as the derived parameters, i.e., the lateral velocity, lateral acceleration, and heading angle. The validity of the proposed method is verified at real intersections using the public driving data of the next generation simulation (NGSIM) project. The results of the turning behavior detection show that the proposed hybrid approach exhibits significant improvement over a conventional algorithm; the average recognition rates are 94.2% and 93.5% at 2 s and 1 s, respectively, before initiating the turning maneuver.

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“…Recurrent neural network (RNN) [16] is a class of neural network with memory capability. Yang [17] proposed a carfollowing model based on recurrent neural network (RNN) to effectively describe the state changes of vehicles while driving and road traffic congestion.…”

Section: Introductionmentioning

confidence: 99%

The Driver Time Memory Car-Following Model Simulating in Apollo Platform with GRU and Real Road Traffic Data

Fei

Hei

et al. 2020

Mathematical Problems in Engineering

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Car following is the most common phenomenon in single-lane traffic. The accuracy of acceleration prediction can be effectively improved by the driver’s memory in car-following behaviour. In addition, the Apollo autonomous driving platform launched by Baidu Inc. provides fast test vehicle following vehicle models. Therefore, this paper proposes a car-following model (CFDT) with driver time memory based on real-world traffic data. The CFDT model is firstly constructed by embedded gantry control unit storage capacity (GRU assisted) network. Secondly, the NGSIM dataset will be used to obtain the tracking data of small vehicles with similar driving behaviours from the common real road vehicle driving tracks for data preprocessing according to the response time of drivers. Then, the model is calibrated to obtain the driver’s driving memory and the optimal parameters of the model and structure. Finally, the Apollo simulation platform with high-speed automatic driving technology is used for 3D visualization interface verification. Comparative experiments on vehicle tracking characteristics show that the CFDT model is effective and robust, which improves the simulation accuracy. Meanwhile, the model is tested and validated using the Apollo simulation platform to ensure accuracy and utility of the model.

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A Novel Car-Following Control Model Combining Machine Learning and Kinematics Models for Automated Vehicles

Cited by 97 publications

References 36 publications

Differences in Driving Intention Transitions Caused by Driver’s Emotion Evolutions

Differences in Driving Intention Transitions Caused by Driver’s Emotion Evolutions

A Hybrid Approach for Turning Intention Prediction Based on Time Series Forecasting and Deep Learning

The Driver Time Memory Car-Following Model Simulating in Apollo Platform with GRU and Real Road Traffic Data

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