Vehicular ad hoc networks (VANETs) are a type of mobile ad hoc network that forms a unified wireless communication network between vehicles and roadside nodes. Roadside units (RSUs), as the infrastructure and key component of VANETs, play a critical role in improving the performance of VANETs. Their deployment can effectively improve the communication performance of the network. The goal of the RSU deployment (RSUD) problem is to install as few RSUs as possible on both sides of roads or intersections so that they can cover most areas and achieve better communication performance. This paper proposes a multi-objective optimization model to solve the RSUD problem using the characteristics of RSUDs on urban roads. Our optimized target includes three performance indicators, which are the deployment cost of RSUs, their coverage area, and communications. Since these three indicators cannot achieve consensus, this paper proposes a multi-objective evolutionary algorithm, the NSGA-II Pareto optimal solution, to solve the RSUD problem. The simulation results show the effectiveness of the multi-objective model and method. Finally, we present an experiment featuring RSU deployment that is based on a real traffic environment and uses OpenStreetMap; this experiment proves that our proposed method is practical.

Mobile edge computing (MEC) provides a new solution to meet the latency-sensitive and computation-intensive application requirements of vehicle networks. Different from existing work, a novel multi-leader single-follower game model is designed, in which each leader can both compete and cooperate with each other. In the case of the leader, a profit maximization optimization problem is proposed. Since the problem is a convex function, there is a Nash equilibrium of the game. Each leader sets the optimal unit price by the amount of computational resources required by the follower. For the followers, a multi-objective optimization problem is formulated with the objective of minimizing the task processing delay and cost. Then, the task offloading and resource allocation based on selection optimization(TRSO) algorithm is proposed to achieve a tradeoff between latency and cost. Specifically, the computational resources are fixed and the Karush-Kuhn-Tucker (KKT) algorithm is used to jointly optimize the task division ratio and the bandwidth allocation ratio to minimize the task processing delay. In addition, the dichotomous method is used to optimize the computational resources of edge servers (ESs) under the task latency constraint so that the total cost is minimized. Simulation results confirm that the proposed TRSO is superior to the task offloading and resource allocation optimization (TRO) algorithm. Under the same delay, compared with TRO, the task processing cost of TRSO is reduced by approximately 83.3%, significantly reducing the overall task processing cost.

Developing a high-quality scenario library is crucial for evaluating more reliable autonomous driving systems. A fundamental prerequisite for constructing such a library is the generation of safety-critical scenarios. In this paper, we propose a nested game algorithm to assign trajectories and their time series to multiple background vehicles. This method aimed to generate safety-critical scenarios with varying degrees of interference for the tested vehicle. To ensure the realism of the generated scenarios, we extracted a series of natural trajectories from an existing dataset as the input for the algorithm. We then analyzed multiple types of scenarios generated by this method and evaluated their danger using generalized metrics, such as the Time-to-Collision (TTC) and Minimum Safe Distance Factor (MSDF), which demonstrated the effectiveness of our approach. The experimental results demonstrate that the nested game-based approach could efficiently construct safety-critical scenarios, contributing to the development of high-quality test scenario libraries.

In February 2020, Beijing, China andCalifornia, USA respectively released road test reports of 2019 for autonomous vehicles. Beijing and California respectively represent the highest level of testing and application of autonomous vehicles in the two countries. This article will compare the test items, evaluation criteria and technical defects of each autonomous vehicle company in the road test reports of China and the United States, also analyze the existing problems, and propose an idea for the construction of a comprehensive test site for autonomous vehicles. This article aims to solve the prominently exposed problems in decision-making and planning in autonomous vehicles with DQN algorithm-base vehicle fleet, and to look forward to the future development trend of autonomous driving testing.