科研之友
微信
新浪微博
Facebook
分享链接
摘要
The machine learning-based car-following models are widely adopted to control the longitudinal movements of automated vehicles, such as Google Car and Apple Car, by mimicking the human drivers' car-following maneuver. However, like human drivers, the models easily produce unsafe maneuvers for automated vehicles and has low robustness, especially in uncommon situations. To improve the machine learning-based car-following models, this paper proposes to combine the machine learning models with the kinematics-based car-following models that can overcome the shortcomings of machine learning models, using an optimal combination prediction method, which is called the combination car-following model in the paper. The selected kinematics-based car-following model is the Gipps model that has an intrinsic crash-avoidance mechanism, and the used machine learning-based models are the Back-Propagation Neural Networks (BPNN) model and Random Forest (RF) model, producing the two CCF models, the Gipps-RF model and Gipps-BPNN model. The real vehicle trajectory data sets are applied to calibrate and validate the proposed models, and simulations are conducted to evaluate the model performances. The results display that the proposed CCF models can enhance safety level and robustness of the car-following control of automated vehicles. Both the two CCF models have better performance than the BPNN and RF car-following models in reducing congestion, stabilizing traffic, and avoiding crashes, especially the Gipps-BPNN model.
