面向智能驾驶测试的仿真场景构建技术综述

With the continuous improvement of vehicle intelligence, the interaction of vehicles with the surrounding environment through perception is increasing. The environment that needs to be dealt with, including many factors such as roads, surrounding traffic and weather conditions, is becoming increasing complex. Limited by the development cycle and cost, especially safety factors and the consideration of complex and diverse working conditions, traditional open road or closed field tests are difficult to meet the requirements of intelligent driving testing. Therefore, simulation test based on digital virtual technology has become a new important means for intelligent driving testing and verification. The simulation test mainly adopts a combination of accurate physical modeling, efficient numerical simulation, and high-fidelity image rendering to realistically construct human-vehicle environment models, including vehicles, roads, weather and lighting, and traffic, and various types of vehicles. The construction of virtual scenarios is a key technology simulation and is particularly important for improving the pressure and acceleration of intelligent driving testing. The virtual scenarios can meet the needs of a large number of diverse test samples to reflect the complex and changeable application environment of intelligent driving. They can also provide a large number of labeled datasets for machine learning that can contain rich data with boundary feature scenario content and lay a solid data foundation for deep learning perception and reinforcement learning planning algorithms. Therefore, the simulation scenario construction technology for intelligent driving test has been investigated worldwide in the current automotive intelligence. As an emerging technology, it still faces many challenges, and its methods need to be studied in depth. This paper systematically expounds the progress and current situation of domestic and foreign studies in simulation scenario construction technology, including automatic scenario construction methods and traffic simulation modeling methods, and focuses on some issues worthy of in-depth study. In the research of scenario construction methods, the key elements and characteristics of the limited scenarios can reflect the infinite richness and complex driving environment. A deep understanding of the network structure and mutual coupling of the scenario is essential for the research of virtual scenario construction. Establishing a description method of the scenario limit and boundary characteristics to form the scenario automated generation method can maximize the potential of accelerated testing of intelligent driving. Researchers have promoted the rapid development of scenario generation technology from different perspectives. However, they often use parameter traversal search ideas to determine the system state space. The development and testing is time consuming and labor intensive due to the unlimited expansion of scenario search. The construction of a scenario with dangerous characteristics requires in-depth exploration of the safety boundary of the ego vehicle driving. Thus, the constructed corner scenario can provide effective information corresponding to real driving for realizing the enhanced generation of the corner characteristics of the scenario. This condition responds to the accelerated testing of intelligent driving systems above level four. In terms of traffic modeling methods, a deep understanding of the driving behavior and interaction characteristics of vehicles is the basis and primary task. Determining the influence law of vehicle driving motion in data information and establishing the traffic model with random dangerous characteristics are the key to realize intelligent driving testing. The current data-driven traffic simulation modeling research mainly describes the microscopic behavior of traffic, but the accurate and true description of driving behavior characteristics is insufficient. The model input is the mutual movement relationship between the vehicle and the surrounding vehicles, and the model output is the speed or trajectory of the vehicle's movement. However, the diversity of results mainly depends on the amount of input data. If the amount of input data is small, the simulation results are monotonous, relying excessively on input data, and lack versatility. Simulating the motion and interaction behavior of different types of agents in a heterogeneous environment is difficult, especially at traffic intersections. At present, replacing the role of physical mechanism models in generality is difficult. This paper introduces the application of PanoSim simulation platform developed by our team and the related research in 2020 China Intelligent Driving Challenge and World Intelligent Driving Challenge. The intelligent driving challenge is based on a variety of scenarios and traffic environments built by the PanoSim simulation environment. This condition allows participating teams to access the simulation scenario database for obtaining vehicle-mounted sensor information in the simulation environment, such as camera video streams, millimeter wave radar data, lidar point cloud data, and true value information. The simulation scenarios of the intelligent driving challenge are mainly divided into two categories: decision- control and perception- decision- control groups. With the continuous development of computer software and hardware, real-time graphics and image processing, virtual reality, especially parallel processing and image rendering and other simulation technologies, and the environment simulation, and sensor modeling technology, the simulation technology for vehicle testing will become the key factor of vehicle intelligent driving technology, product development, and core competency of technology and products.