Virtual simulation testing of Autonomous Vehicles (AVs) is gradually being accepted as a mandatory way to test the feasibility of driving strategies for AVs. Mainstream methods focus on improving testing efficiency by extracting critical scenarios from naturalistic driving datasets. However, the criticalities defined in their testing tasks are based on fixed assumptions, the obtained scenarios cannot pose a challenge to AVs with different strategies. To fill this gap, we propose an intelligent testing method based on operable testing tasks. We found that the driving behavior of Surrounding Vehicles (SVs) has a critical impact on AV, which can be used to adjust the testing task difficulty to find more challenging scenarios. To model different driving behaviors, we utilize behavioral utility functions with binary driving strategies. Further, we construct a vehicle interaction model, based on which we theoretically analyze the impact of changing the driving behaviors on the testing task difficulty. Finally, by adjusting SV’s strategies, we can generate more corner cases when testing different AVs in a finite number of simulations.

As one of the most promising communication technologies for vehicular networks, LTE-V has the advantages of wide coverage and a high transmission rate. 3GPP released the technical specification of LTE-V in March 2017, launching a spate of related research and industrialization. In this paper, we propose a communication model based on Markov process to evaluate the reliability of LTE-V. We derived the Packet Delivery Rate (PDR) of LTE-V based on the model. Moreover, we use Poisson process to model the distribution of vehicles on a highway, then combine the communication model with the vehicles’ distribution to derive the PDR under this scenario. To verify the correctness of the proposed model, we established a simulation program on the MATLAB platform. By comparing the simulation results and the mathematical results, we found that simulation results are a very good fit for the model.

We analyzed and improved a collision avoidance strategy, which was supported by Long Term Evolution-Vehicle (LTE-V)-based Vehicle-to-Vehicle (V2V) communication, for automated vehicles. This work was completed in two steps. In the first step, we analyzed the probability distribution of message transmission time, which was conditional on transmission distance and vehicle density. Our analysis revealed that transmission time exhibited a near-linear increase with distance and density. We also quantified the trade-off between high/low resource reselection probabilities to improve the setting of media access parameters. In the second step, we studied the required safety distance in accordance with the response time, i.e., the transmission time, derived on the basis of a novel concept of Responsibility-Sensitive Safety (RSS). We improved the strategy by considering the uncertainty of response time and its dependence on vehicle distance and density. We performed theoretical analysis and numerical testing to illustrate the effectiveness of the improved robust RSS strategy. Our results enhance the practicability of building driverless highways with special lanes reserved for the exclusive use of LTE-V vehicles.

A campus bus network design and evaluation, taking Tsinghua University as an example, is investigated in this paper. To minimize the total cost for both passengers and operator, the campus bus system planning in a sequential approach is discussed, including the route network design, headway (i.e., the inverse of service frequency) optimization, and system evaluation. The improved genetic algorithm is proposed to optimize the route network based on the route property, and the impacts of the fluctuation of passenger demand and average traveling time are analyzed. The identity proportion in the headway optimization is then introduced with full consideration of its impacts. Based on the actual variety of passenger demand, a non-fixed schedule demonstrates its efficiency. VISSIM is finally adopted to simulate the campus bus system and a comprehensive evaluation system for the campus bus is developed. Compared with the current bus network and the one without considering the route property, the evaluation of the proposed approach shows an improvement of 18.7% and 10.1%, respectively. Moreover, the sequential approach shows an efficiency improvement over the alternative method. It is of great significance for the development of public transit systems in large industrial parks to decrease the total cost for both passengers and operator.

Cyber-Physical System (CPS) and Cyber-Physical-Social System (CPSS) computing are now challenging existing research in many realms, including Intelligent Transportation Systems (ITS). In this survey, we highlight some advances in the coevolution of CPS, CPSS, and ITS, with an emphasis on traffic data. We first explain the hierarchical architecture of CPS-ITS in terms of five layers: perception, communication, computing, control, and application. Then, we analyze the characteristics of traffic data in CPS-ITS, and enumerate some new technologies for data operation and management. Two typical cases of CPS-ITS, vehicular-communication-based traffic control systems and smart parking systems, are illustrated to describe how CPS is changing our lives and influencing the development of future ITS.