Car-following Model with Adaptive Expected Driver's Following Distance and Behavior

To satisfy the personalized demand of intelligent vehicles and to improve the satisfaction and acceptance of intelligent vehicle human-computer interaction, a two-layer driver car-following model framework was constructed, and a personalized driver car-following model was proposed. The models can adapt the driver's expected following distance and behavior. Firstly, the equilibrious car-following data was extracted. The first layer model was established by using the Gaussian Mixture Model (GMM) and Probability Density Function (PDF), which was the driver's expected following distance model. Then, the expected following distance parameter was introduced into the model, and the driving behavior was learned based on Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM). The second layer model, i.e., the personalized car-following model, was established to predict the acceleration of future time. Next, the effect of different numbers of GMM components on the model performance was studied, and the comparison was made among the two-layer driver car-following model, the Gipps model, the optimal distance model (ODM), monolayer model and the general model. Finally, the results of the 8 drivers' naturalistic driving behavior data show that the number of GMM components is positively correlated with the model performance. Under the optimal Gaussian model component, the mean predictive deviation of 8 drivers in the training set is 0.101 m•s-2, and 0.123 m•s-2 in the test set. The model calculation results of randomly selecting one of the drivers' experimental data show that the mean absolute deviation of acceleration is 0.087 m•s-2 and 0.096 m•s-2, and the prediction results are better than that of the Gipps model, the ODM model, monolayer model and the general model by more than 30%, which is moreconsistent with the actual car-following behavior of the driver. Copyright © 2022 by Science Press.