Search Results (3)

In an intelligent driving system, the rationality of driving decisions and the trajectory planning scheme directly determines the safety and stability of the system. Existing research mostly relies on high-definition maps and empirical parameters to estimate road adhesion conditions, ignoring the direct impact of real-time road status changes on the dynamic feasible domain of vehicles. This paper proposes an intelligent driving decision-making and trajectory planning method that comprehensively considers the influence factors of vehicle–road interaction. Firstly, real-time estimation of road adhesion coefficients was achieved based on the recursive least squares method, and a dynamic adhesion perception mechanism was constructed to guide the decision-making module to restrict lateral maneuvering behavior under low-adhesion conditions. A multi-objective lane evaluation function was designed for adaptive lane decision-making. Secondly, a longitudinal and lateral coupled trajectory planning framework was constructed based on the traditional lattice method to achieve smooth switching between lateral trajectory planning and longitudinal speed planning. The planned path is tracked based on a model predictive control algorithm and dual PID algorithm. Finally, the proposed method was verified on a co-simulation platform. The results show that this method has good safety, adaptability, and control stability in complex environments and dynamic adhesion conditions. Full article

In the V2X environment, intelligent connected vehicles can obtain multi-dimensional traffic flow data in real time through the vehicle–road collaborative cyber–physical fusion system. Based on this, this study proposes a multi-lane traffic flow lattice model integrating optimal traffic flow difference estimation information to effectively suppress traffic congestion. The linear stability criterion of the system is derived through linear stability analysis, proving that the optimal traffic flow difference estimation can significantly expand the stable region and suppress traffic fluctuations caused by small disturbances. Furthermore, the perturbation method is used to derive the mKdV equation near the critical stability point of the system, revealing the nonlinear characteristics of traffic congestion propagating in the form of kink solitary waves, and indicating that the new consideration effect can effectively slow down the congestion propagation speed by adjusting the parameters of solitary waves (such as wave speed and amplitude). The numerical simulation results show that compared to the traditional model, the improved model exhibits enhanced traffic flow stability and robustness. Meanwhile, it reveals the nonlinear relationship between the increase of the number of lanes and the alleviation of congestion, and there is an optimal lane configuration threshold. The research results not only provide theoretical support for the optimization of traffic flow efficiency in intelligent transportation systems, but also provide a decision-making basis for dynamic lane management strategies in the V2X environment. Full article

This paper suggests a new multi-lane lattice model that incorporates both overtaking mechanisms and drivers’ aggressive lane-changing behaviors to investigate macroscopic traffic stability in multi-lane expressway environments. To enhance the fidelity of lane-changing simulation, the proposed model reformulates lane-changing protocols by integrating empirical observations of aggressive driving patterns in real-world scenarios. Through theoretical derivation, we formulate a density wave partial differential equation that captures the spatio-temporal propagation of congestion patterns near critical stability thresholds while analytically obtaining the linear stability criterion for the proposed model. The validity of these theoretical constructs is validated through systematic numerical simulation. Key findings reveal that when overtaking passing rates are relatively low, the driver’s aggressive lane-changing strategy exhibits a pronounced stabilizing effect on multi-lane systems and effectively mitigates traffic oscillation amplitudes. Conversely, under high passing rate conditions, such aggressive driving behaviors are shown to exert detrimental effects on both traffic fluctuation suppression and system-wide stability. Notably, our findings also demonstrate that expanding the number of lanes merges as a viable strategy to enhance systemic robustness. Full article