Although autonomous vehicles have introduced a promising potential for improving traffic safety and efficiency, ensuring the safety of autonomous vehicles in complex road traffic environments is still a huge challenge to be tackled. To quickly quantify the potential risk factors of autonomous vehicles in traffic environments, this paper focuses mainly on the influence of the depth and breadth of the environment elements on the autonomous driving system, uses the potential field theory to establish a model of the impact of the environmental elements on the autonomous driving system, and combines AHP to quantify equivalent virtual electric quantity of each environment element, so as to realize the quantitative evaluation of the traffic environment complexity. The proposed method comprehensively considers the physical attributes and state parameters of the environmental elements, which compensates for the fact that the shortage of the factors considered in the traffic environment complexity assessment is not comprehensive. Finally, a series of experiments was carried out to verify the reliability of our proposed method. The results show that the complexity of the static elements is determined only by the physical attributes and shape of the obstacle; the complexity of the dynamic elements is determined by the movement of the obstacle and the movement of the autonomous vehicle, and the comprehensive complexity mainly depends on the complexity of their dynamic elements. Compared with other methods, the complexity evaluation values are generally consistent, the absolute percentage error of the majority of samples was within ±5%, and the degree of deviation was −1.143%, which provides theoretical support for autonomous vehicles on safety and the risk assessment in future.

Carbon trading is an effective measure for the road transportation to reduce energy consumption and carbon emissions. Carbon emission quotas are the primary concern to ensuring the efficiency of carbon trading. However, the existing studies have mostly focused on carbon emission quotas in different regions, i.e., countries and provinces. Few literature studies simulate carbon quota allocation in the road transportation. A novel approach from the perspective of carbon emission intensity of vehicle is proposed, on the basis of data envelopment analysis (DEA) model. Unlike other studies, the idea of allocation of baseline excitation is introduced and the intensity is included in the model as the baseline. Firstly, the Delphi method is employed to select input and output indicators. Secondly, carbon emission intensity is determined by the cumulative distribution function (CDF). Furthermore, the carbon emission quotas in road transportation in 30 provinces of China are used to validate the model. The results show that (1) the carbon emission intensity of commercial trucks and buses in Chinas road transport industry is 75.04g/t·km and 13.12g/p·km, respectively; (2) the provinces of Shanghai, Guangdong, and Xinjiang have the greatest carbon reduction potential and Henan, Hunan, and Anhui have the largest increase in emission quotas; (3) compared with traditional history responsibility and baseline methods, the proposed approach increases allocation efficiency by 19 and 14, respectively; and (4) the approach can make the carbon emission quotas play the role of incentive while taking fairness into account and can more effectively promote the implementation of carbon trading system in road transportation.

In the wake of the development of unmanned ground vehicle technologies, it is necessary to conduct a proper evaluation of the various technologies. Previous evaluation method saremainly based on the completion of the task, this may mislead most of the unmanned vehicle teams using a conservative strategy during the evaluation. In this paper, a new evaluation method is proposed. Based on typical working conditions including intersection, car-following and obstacle-avoiding, the new evaluation indicator system is established, the information entropy method is applied to the comprehensive evaluation of unmanned vehicles. Empirical results show that the proposed evaluation method can quantitatively evaluate the intelligent capacity of unmanned vehicles.

The traditional conflict resolution algorithm is designed for automated vehicles based on the premise of the right of way, but there is an unclear road right of way at unsignalized intersections, which brings trouble to the decision making of automated vehicles. The objective of this work is to provide our system with evolving cooperative and non-cooperative decisions. We achieve this by integrating game theory into the decision making. When the system decides to drive cooperatively, the joint action is planned to optimize the overall revenue of multi-vehicles based on the cooperative game, considering the conflict relationship with its neighboring vehicles. When the system fails to perform cooperative driving or response timeout, the vehicle-mounted unit would take non-cooperative driving to optimize the trajectory considering only its individual benefit. The proposed model can provide our system with stability and robustness, which effectively solved the conflict resolution problem when the right of way was not clear at intersections. We have implemented some simulation experiments of cooperative and non-cooperative conflict resolution, the results show that the revenue among various interest groups is more balanced with a cooperative conflict resolution method. Compared with the non-collaborative driving decision, the conflict resolution time is shortened, and the average delay of each vehicle at intersections is reduced by 1~2 s, with an average reduction of approximately 5%. The research can provide a reference for collaborative driving of automated vehicles at unsignalized intersections.

To promote the development of unmanned ground vehicle technologies, it is necessary to design a scientific and reasonable test method. Road is an important part of test environmental elements, and different road conditions can examine the adaptability of unmanned ground vehicles to the environment. Therefore, the scientific calculation of road complexity is of great importance. Previous studies on road are mainly based on the concept of road roughness; however due to the unicity of road feature indicators, road complexity can only be reflected to a certain extent. This paper proposes a new road-feature-based multiparameter road complexity calculation model of off-road environment to show the complexity of road more comprehensively. First, a multi-sensor-based data acquisition mobile platform is established to obtain more complete road data. Then, based on the analysis of road feature, road indicators like three-dimensional scale, average slope, and adhesion characteristics of travelable area are obtained. According to the analysis methods of road roughness, the principle of analytic hierarchy process, and the data collected from off-road environment, the calculation model of road complexity is determined. Finally, by calculating complexities of several cross-country roads, the feasibility of this model is verified, which provides a theoretical support for the scientific calculation and quantitative analysis of different road complexities.

The thermodynamic structure of the North Atlantic Oscillation (NAO), in terms of the pattern of the winter‐mean potential temperature (PT) anomaly in the troposphere, is a crucial indicator of regional climate variations and can be influenced by synoptic‐scale eddy heat (EH) fluxes. The role and mechanism of EH feedback in modulating the winter‐mean thermodynamic structure of the NAO are investigated. We show that the low‐frequency NAO‐related flow can barotropically regulate EH flux and give rise to an EH transport that is down‐gradient (up‐gradient) of the NAO‐related PT anomalies, thus creating negative (positive) EH feedback on PT anomalies in the middle‐lower (upper) troposphere. Using eddy structure decomposition, we calculated the two primary terms of anomalous EH flux: the BA term is the product of basic eddy velocity and anomalous eddy PT, and the AB term is the product of anomalous eddy velocity and basic eddy PT. The meridional BA EH flux is caused by the meridional shift of eddy PT structure under NAO. It is a major contributor of the anomalous EH flux and forcing, which weaken NAO‐related PT anomalies. The zonal AB EH flux is smaller in magnitude and is induced by the zonal slant of the eddy streamfunction structure in the negative NAO phase. It creates EH feedback upstream of the NAO flow. In addition, the positive NAO phase has a negatively stronger EH feedback than the negative phase; this is related to the higher persistence of the anomalous PT pattern under negative NAO.