GuangHao Huang

Overtaking path planning is a key technology for Connected and Autonomous Vehicles (CAVs). However, existing path planning methods have limitations, such as safety and comfort. This study proposes a driving potential field model to improve the autonomy, safety, and comfort of CAVs during overtaking. The method builds an improved gravitational and repulsive potential field by considering the relative velocity and relative acceleration of the CAV and the leading vehicle. The influence distances of the forces are also specified to make the magnitude of the forces more specific. Road boundary potential fields are also established to regulate the driving area of the CAV. The overtaking path planning algorithm is tested by cosimulation of MATLAB/Simulink, CarSim, and PreScan based on four existing overtaking scenarios in the Chongqing Inner Ring Expressway. The results show that the proposed algorithm can significantly reduce the curvature of the overtaking path by 62.69%, 83.86%, 85.07%, and 91.18%, respectively, compared to the traditional artificial potential field method. The lateral acceleration is also significantly reduced by 36.33%, 62.97%, 45.77%, and 53.38%, respectively, in different overtaking scenarios. The lateral acceleration is less than 0.13 m/s² in all cases. Overall, the proposed algorithm is effective in improving overtaking path planning in terms of safety, efficiency, and comfort.

Background: Understanding the spatial–temporal distribution characteristics of urban road traffic accidents is important for urban road traffic safety management. Based on the road traffic data of Wales in 2017, the spatial–temporal distribution of accidents is formed. Methods: The density analysis method is used to identify the areas with high accident incidence and the areas with high accident severity. Then, two types of spatial clustering analysis models, outlier analysis and hot spot analysis are used to further identify the regions with high accident severity. Results: The results of density analysis and cluster analysis are compared. The results of density analysis show that, in terms of accident frequency and accident severity, Swansea, Neath Port Talbot, Bridgend, Merthyr Tydfil, Cardiff, Caerphilly, Newport, Denbighshire, Vale of Glamorgan, Rhondda Cynon Taff, Flintshire and Wrexham have high accident frequency and accident severity per unit area. Cluster analysis results are similar to the density analysis. Finally, the temporal distribution characteristics of traffic accidents are analyzed according to month, week, day and hour. Accidents are concentrated in July and August, frequently in the morning rush hour and at dusk, with the most accidents occurring on Saturday. Conclusions: By comparing the two methods, it can be concluded that the density analysis is simple and easy to understand, which is conducive to understanding the spatial distribution characteristics of urban traffic accidents directly. Cluster analysis can be accurate to the accident point and obtain the clustering characteristics of road accidents.

Since traffic is a time-dependent and complicated non-linear system. Chaos Theory has been specifically designed to identify chaotic behaviour and properties for such systems. Previous researches have been restricted to single traffic parameter, which fails to get actual behaviour of the traffic systems. In this work, we have devised the multi-parameters chaos prediction method to describe the tendency of traffic from different aspects for prediction of single parameter chaotic time series. The fusion method is considered the relationship of different traffic parameters according to the phase space reconstruction. Taking the exit ramp of Xishanping expressway in Beibei district, Chongqing Municipality as the example, the feasibility and reliability of traffic state prediction were tested. The trail results indicates that more features of realistic traffic conditions are reflected by fusing multiple traffic parameters, and gain real accuracy improvements in traffic prediction. Compared with three single-parameter time series prediction methods, the mean absolute relative error of the multi-parameter prediction method decreased by 2.42, 2.39 and 0.8 respectively, the mean absolute relative error decreased by 2.33, 3.25 and 1.27, and the equal coefficient reached 0.9528 with a slight increase. Proposed method will definitely generates the opportunity for next generation of traffic control that are better able to detect the dynamic states of traffic, and therefore more effectively prevent the traffic congestion and pollution in the urban areas worldwide.