# A Survey of Scenario Generation for Automated Vehicle Testing and Validation
## 1 Introduction  
Reducing traffic accidents has long been a key focus of research and development. Studies have shown that a significant proportion of vehicle accidents are caused by human errors [1; 2]. To mitigate these errors, various advanced driver assistance systems (ADAS) have been developed and integrated into vehicles over the past several decades. As automation technology continues to evolve, fully autonomous vehicles (AVs) are becoming a reality. As a result, major car manufacturers and new companies have been actively developing AV technology [3]. The primary goal of AV testing is to ensure safety. Testing AV systems in various traffic conditions and scenarios is crucial to verify that AVs can operate safely in complex environments [4]. Apart from identifying potential risks and ensuring the reliability of AVs, rigorous testing establishes public trust, removing concerns that limit their widespread adoption. In addition to ensuring safety, testing must also evaluate a vehicle's ability to perform specific driving tasks, thereby assessing its overall capabilities. There are three primary methods of conducting AV testing--on-road tests, closed-field tests, and simulation tests [5]. On-road testing evaluates AVs on real-world roads, providing realistic and unpredictable scenarios [6]. However, safety-critical situations are rare in real-world driving, making on-road testing inefficient for comprehensive safety validation. One study [5] showed that proving that AVs are as safe as human-driven cars would require driving hundreds of millions of miles, or even billions of miles, of on-road testing. Closed-field testing takes place in controlled environments specifically designed to simulate real-world traffic and road conditions. This method offers a controlled experimental setting where hazardous scenarios can be tested safely, improving efficiency [6]. However, this cannot fully replicate the complexities of real-world conditions, such as traffic dynamics,weather variations, and road environments, which may limit its ability to fully evaluate AV performance. Simulation testing, also known as scenario-based testing, uses computer-generated environments to test AV systems. Simulation testing allows autonomous driving systems to be tested in a safe and controlled simulation of a wide range of traffic conditions, road scenarios, and vehicle behaviors. Compared with closed-field testing and on-road testing, simulation testing does not require physical vehicles, making it more resource-efficient. It enables early detection of issues during algorithm development and allows for the testing of highly dangerous situations [6]. Among the methods, simulation testing stands out as the most convenient and advantageous. It can generate a variety of driving scenarios, enables real-time parameter adjustments to suit different AV systems, and supports rapid iteration and optimization of autonomous driving systems. Moreover, testing in a virtual environment also allows developers to reproduce specific scenarios to analyze system performance and make improvements. In addition, simulation-based scenario testing has become the most widely used and promising approach for AV testing. This survey focuses on the simulation testing method, specially the generation of test scenarios for AVs. In the generation of simulation test scenarios, many current approaches use predefined or scripted behaviors for movable objects such as the background vehicles (BVs), pedestrians, and other road users. BVs refer to all vehicles in the scenario other than the AV under test. These BVs follow preset behaviors and do not make real-time decisions based on the current environment, leading to what are referred to as non-adaptive scenarios. In contrast, adaptive scenarios are those that allow movable objects to make real-time decisions based on changing conditions. To ensure comprehensive, effective, and realistic testing of AVs, non-adaptive scenarios are no longer sufficient, and the generation of adaptive test scenarios has become the primary focus.