# Selecting Non-Line of Sight Critical Scenarios for Connected Autonomous Vehicle Testing
## 1 Introduction  
Connected autonomous vehicles (CAVs) utilise sensors to perceive their environment. This data is brought together using sensor fusion algorithms and integrated onto the maps available within the vehicle or in the cloud to support navigation. These sensors can perceive up to a certain range (150-250 m at a maximum speed of 120 km/h [1; 2; 3]). This perception range can be further limited due to sensor occlusion, sensor noise or scenarios with blind spots, such as corners and intersections [4; 5]. Such scenarios in which a vehicle's perception is occluded due to a blind-spot or geometry of the road are referred to as non-line of sight scenarios (NLOS). Such scenarios are the main focus of this paper. In such cases, scenario perception range of autonomous vehicles (AVs) needs to be extended by enabling the receipt of information about obstacles far in advance of being detected by on-board sensors in order to avoid a collision. The need to increase vehicle perception range in the presence of blind spots has been a focus of research within both academia and industry [6; 7; 8; 9; 10; 11; 12; 13]. The most common method within the literature for extending perception range in NLOS scenarios is through vehicle-to-everything (V2X) communication [14; 15; 16; 17; 18; 19]. There are a number of studies that have looked into developing algorithms (obstacle detection, trajectory planning, collision avoidance, etc.) and communication protocols (e.g., routing protocols [19]) for such NLOS scenarios. These algorithms need to be tested to ensure quality and adherence to system requirements, paving the way for safe deployment of autonomous vehicles. Testing is primarily conducted through on-road testing, simulation or scenario-based methods. On-road testing incurs enormous resources in terms of time,cost and human resources, while simulation-based testing requires a large volume of data; both methods cannot ensure coverage of all critical scenarios. A scenario-based approach facilitates the development of suitable driving scenarios that can incorporate specific requirements, such as critical scenarios. [PERSON] et al. [20] proposed a three-terminology framework along V-model-based scenario development of the ISO26262 standard for the development of scenarios to test and validate the functionalities of autonomous vehicles. This consists of functional scenarios, which are the most abstract way of describing a scenario and involve the description of on-road scenario concepts (e.g., road segments) and their relation (e.g., road segment has a relation to lane segment) in a linguistic manner, as well as logical scenarios, which specify a value range for each attribute of a concept in a scenario state space (e.g., a road segment has an attribute length which consists of a range of values for possible lengths), and concrete scenarios which specify a specific value for each parameter from within the respective value range. The scenario development process can be visualised in Figure 1. However, the scenarios generated from functional and logical scenarios are a substantial number, equivalent to the number of combinations of every value of each attribute (for example, the speed and position of the roads in the scenario) making up the scenario. Not all scenarios within this scenario space will be relevant for desired functionality testing and testing against each one will be costly and time consuming. Therefore, there is a need to select concrete scenarios which are relevant to certain functional requirements, for example, scenarios that are critical in nature. There have been numerous studies on concrete scenario generation for various applications, for example, free driving, following, lane keeping, traffic scenarios, overtaking obstacle detection, etc. [21; 22; 23; 24]. However, there are none that cover the selection of NLOS critical scenarios. Subsequently, the focus of this paper is the selection of critical concrete NLOS scenarios.