Automated driving is a promising tool for reducing traffic accidents. While some companies claim that many cutting-edge automated driving functions have been developed, how to evaluate the safety of automated vehicles remains an open question, which has become a crucial bottleneck. Scenario-based testing has been introduced to test automated vehicles, and much progress has been achieved. While data-driven and knowledge-based approaches are hot research topics, this survey is mainly about Data-Driven Scenario Generation (DDSG) for automated vehicle testing. Rather than describe the contributions of every study respectively, in this survey, methodologies from various studies are anatomized as solutions for several significant problems and compared with each other. This way, scholars and engineers can quickly find state-of-the-art approaches to the issues they might encounter. Furthermore, several critical challenges that might hinder DDSG are described, and responding solutions are presented at the end of this survey.

High-level Automated Vehicles (HAVs) are expected to improve traffic safety significantly. However, verifying and evaluating HAVs remains an open problem. Scenario-based testing is a promising method for HAV testing. Boundary scenarios exist around the performance boundary between critical and non-critical scenarios. Testing HAVs in these boundary scenarios is crucial to investigate why collisions cannot be avoided due to small changes in scenario parameters. This study proposes a methodology to generate diverse boundary scenarios to test HAVs. First, an approach is proposed to obtain at least one High-Performance Classifier (HPC) based on two classification algorithms that iteratively guide each other to find uncertain scenarios to improve their performance. Then, the HPC is exploited to find candidate scenarios highly likely to be boundary scenarios. To increase the efficiency of candidate scenario generation, a strategy based on local sampling is presented to find more diverse candidate scenarios based on a small number of them. Numerical experiments show that the HPCs acquired by the method proposed in this study can achieve a classification accuracy of 98% and 99% for random car-following and cut-in scenarios, respectively. Moreover, more than 86% of 271,744 candidate cut-in scenarios derived by local sampling are near the performance boundary.

This paper proposes a safety critical nonlinear model predictive control (NMPC) to track a target in an obstacle-existing scene for a car-like robot with a limited detection ability. A temporary artificial reference within the detection region is employed as a temporary target to reduce the meaningless trajectory prediction beyond the detection region for the vehicle. In order to satisfy the safety requirements for obstacle avoidance, the non-convex admissible set generated by obstacles is considered and a compression-expansion-inflation (CEI) algorithm is presented to map a subspace of the nonconvex admissible set into a convex one. Then, a modification NMPC with the artificial reference is proposed, where the iteration of the NMPC and the selection of the artificial reference are both achieved in the convex mapping set. It results in the appropriate selection of the artificial reference based on the Euler distance and guarantees the feasibility and stability of the NMPC. In this paper, the CEI algorithm does not introduce any conservatism but abandons the partial admissible set by an additional decision-making process. Furthermore, the proposed algorithm is applied in both simulation and real scenes with single and multiple obstacles.

This article studies a target tracking problem for a multiagent system with operation and communication safety under two kinds of relationships. A class of control protocols is designed to achieve the target tracking in the two-agent case under two relationships first as a foundation for the multiple agents, where the agents exhibit different behavioral patterns under different relationships. The control protocol employs topology and optimization theories to search for a moving direction in a mapping convex space, ensuring operation and communication safety. Furthermore, a target tracking algorithm for the multiagent system is designed to achieve target tracking with operation and communication safety, and expands to the static obstacle-existence scenario and the dynamic and static obstacle-existence scenario. By introducing weight parameters to increase the dimension of the optimized variable, the probability of the absence of a feasible control input for the multiagent system is greatly reduced. Next, to improve the feasibility and scalability of the proposed algorithm, some extended discussions and analysis are provided, including the relaxation of some assumptions, the selection of parameters, and the execution of the algorithm. Finally, some simulation results are provided to verify the advantages and the effectiveness of our algorithm.