# Quantitative Testing and Analysis of Non-Standard AEB Scenarios Extracted from Corner Cases
## 1 Introduction  
Testing and verification of intelligent connected vehicles are crucial steps in the research and development of autonomous and assisted-driving vehicles and hold great significance for their development and implementation. Currently, autonomous driving testing is primarily categorized into virtual simulation testing, closed-site testing, and open-road testing based on the testing environment. Testing methods are further divided into use case-based testing, scenario-based testing, and public road testing. Scenario-based testing has emerged as a widely accepted approach within the industry for addressing the challenges of testing autonomous driving vehicles [1]. The term \"scenario\" originally found its application in the development of software systems that served to describe system usage, requirements, and environmental elements to facilitate the creation of more feasible systems [2; 3; 4; 5; 6]. However, in the realm of autonomous driving, a clear and unified definition of \"scenario\" is currently lacking. The concept of scenario was initially introduced to autonomous driving testing by [PERSON] [7]. Subsequently, [PERSON] et al. proposed a definition of scenario as \"the continuous variation of the dynamic environment surrounding the test vehicle within a specific time range, including the behavior of the test vehicle in that environment\" [8]. [PERSON] mies suggested that \"a scenario is an informal description of a series of events that occur when a system performs a specific task\" [9]. In the PEGASUS project, functional scenarios, logic scenarios, and concrete scenarios were introduced to account for the varying requirements of scenarios during the concept development, system development, and testing phases of autonomous driving product development [10]. To ensure sufficient diversity in the tested scenarios, it is crucial to establish a comprehensive test scenario library that encompasses a wide range of typical scenarios. Currently, several countries and institutions are involved in this endeavor. For instance, Germany's PEGASUS and KITTI, the US's NHTSA Automated Driving Test Framework project, UC Berkeley's BDD100K, China's \"Kunlun Plan,\" and Baidu's Apollo Space are actively contributing to this effort [11]. These test scenario libraries draw data from three primary sources: real-world data, simulated data, and expert knowledge. Among these, real-world natural driving data stand out due to their abundant resources and widespread utilization. Presently, hazardous scenariatic assessment methods can be broadly categorized into three types: time parameters, distance parameters, and deceleration parameters [12]. In the context of rear-end scenarios, the Time to Collision (TTC) is a commonly used indicator for evaluating the risk and likelihood of collisions [13; 14; 15]. However, as the relative speeds of the vehicles approach zero, the TTC value tends towards infinity. Hence, the reciprocal of TTC is typically employed to assess the severity of following-car scenarios [16]. Time Headway (THW) refers to the time interval between the host vehicle and the preceding vehicle, calculated by dividing the distance between them by the host vehicle's speed. It represents the time required for the host vehicle to collide with the preceding vehicle along a straight line in the lane, assuming the preceding vehicle's speed is zero. THW provides a measure of potential hazards and complements TTC in characterizing obvious dangers. It is commonly used to evaluate the severity of following-car scenarios and establish the alarm threshold for Advanced Driver Assistance Systems (ADAS) [17; 18; 19]. However, relying solely on TTC or THW has certain limitations when assessing the risk of following car scenarios. TTC alone is insufficient for identifying hazardous scenarios in stable following processes with short distances and low relative speeds. Additionally, THW is influenced by factors such as vehicle type and driving ability and does not consider the preceding vehicle's speed.