To optimize a vehicle platoon system in terms of car-following behavior, a decentralized model predictive control (MPC) strategy for longitudinal velocity control was established (namely, CF-MPC). Firstly, considering the influence of car-following behavior on vehicle states, a longitudinal velocity control model for platoons of connected and automated vehicles (CAV) was designed. Based on that model, an upper-level MPC controller was built to obtain the desired acceleration of the vehicles. Secondly, a lower-level controller received the desired acceleration signal and converted it into the expected throttle opening/braking pressure, to control acceleration/deceleration. Then, the Lyapunov stability method was used to detect the stability conditions that the model should satisfy. Finally, three simulation procedures—constant speed, acceleration, and deceleration were tested, and the validity of the CF-MPC method was verified from the perspectives of a model strategy and a control strategy. The simulation results show that with the proposed CF-MPC method, CAV platoons quickly completed velocity tracking and maintained a safe distance, thereby improving traffic efficiency, fuel economy, driving safety, and transportation capacity.

Based on the conditional heteroskedasticity of following vehicle acceleration fluctuation in car-following behavior, this paper combines the idea of driving force, Intelligent Driving Model (IDM) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH model) to establish a novel car-following model. First, the concept of safety driving force and efficiency driving force are recommended, and both of them are defined by the idea of IDM, and the dynamic balance of them are the reason for acceleration fluctuation. Then, according to the heteroskedasticity of acceleration sequence, the classic GARCH model is introduced to establish the relationship among the variance of acceleration fluctuation items, driving force items and fluctuating memory items. On this basis, a novel car-following model is established, and the relational properties are studied. Finally, an example is used to verify our model, the results show that the novel car-following model is more accurate than IDM model, and the forward prediction result is close to the actual acceleration change value, which can predict the occurrence of dangerous driving behavior.

Connected and automated vehicles (CAV) utilize technologies such as vehicle-to-everything (V2X) to acquire information from neighboring vehicles, effectively enhancing driving safety and road traffic efficiency. However, the inherent communication delays in V2X technology can impact the speed control methods of connected and autonomous vehicle platoons. In this paper, we propose a novel speed control method for CAV platoons with time-varying delays to mitigate the effects of communication delays. First, considering the impact of communication delays on vehicle speed, we construct a linear control law for CAV platoons with time-varying delays based on a vehicle speed prediction model. Next, time-domain analysis and Lyapunov stability theory are used to prove the stability of the vehicle speed prediction model. Finally, numerical simulations are conducted under scenarios with and without time-varying delays, and the effects of the delay function form, control law form, and their parameters on the proposed speed control method are explored. The experimental results demonstrate that the proposed linear control law for CAV platoons with time-varying delays effectively reduces the impact of communication delays, improving driving stability and enhancing road traffic safety.

The Mahalanobis–Taguchi system (MTS) is a multivariate data diagnosis and prediction technology, which is widely used to optimize large sample data or unbalanced data, but it is rarely used for high-dimensional small sample data. In this paper, the optimized MTS for the classification of high-dimensional small sample data is discussed from two aspects, namely, the inverse matrix instability of the covariance matrix and the instability of feature selection. Firstly, based on regularization and smoothing techniques, this paper proposes a modified Mahalanobis metric to calculate the Mahalanobis distance, which is aimed at reducing the influence of the inverse matrix instability under small sample conditions. Secondly, the minimum redundancy-maximum relevance (mRMR) algorithm is introduced into the MTS for the instability problem of feature selection. By using the mRMR algorithm and signal-to-noise ratio (SNR), a two-stage feature selection method is proposed: the mRMR algorithm is first used to remove noise and redundant variables; the orthogonal table and SNR are then used to screen the combination of variables that make great contribution to classification. Then, the feasibility and simplicity of the optimized MTS are shown in five datasets from the UCI database. The Mahalanobis distance based on regularization and smoothing techniques (RS-MD) is more robust than the traditional Mahalanobis distance. The two-stage feature selection method improves the effectiveness of feature selection for MTS. Finally, the optimized MTS is applied to email classification of the Spambase dataset. The results show that the optimized MTS outperforms the classical MTS and the other 3 machine learning algorithms.