# Selecting Non-Line of Sight Critical Scenarios for Connected Autonomous Vehicle Testing
## 3 Methodology  
To test autonomous vehicle algorithms against test scenarios requires the selection of a set of scenarios complying with testing criteria from the entire range of scenarios which would help with evaluation of the algorithm [29]. It must be noted that selecting scenarios is an essential step as the number of possible test scenarios a vehicle can encounter is very large and it would be very time consuming to test against each scenario. In addition, many scenarios are trivial in that they will not lead to a safety critical event. Furthermore, many scenarios are essentially similar and hence testing against one is enough for the cluster of similar scenarios. The research presented in this paper will select scenarios that have the potential to lead to a collision as a result of the ego vehicle (vehicle under test) not being aware of the obstacle until it is too late to avoid collision. These scenarios are referenced to as _critical scenarios_ in this paper. The selection of critical scenarios comes under concrete scenarios within the development process of functional, logical and concrete scenarios along the V-model based development process of ISO26262. Concrete scenarios correspond to the selection of a scenario from the scenario space (logical scenario). For the test case, we have used a T-junction scenario to determine critical NLOS scenarios; statistically T-junctions have a high number of collisions as discussed in [81; 82; 83]. This simple yet powerful scenario can be easily expanded to include more roads (forming crossroads and roundabouts) or adapted to form blind bends on the same road. Similarly, more cars and road users can be added. Figure 2 illustrates an example of a NLOS T-junction scenario.  
As illustrated in Figure 2, both vehicles (ego and CAV) have a range of NLOS positions they can occupy, as represented by the black bounding box and speed as detailed in Section 3.3. The bounding box position was determined through simulation and represents locations from where the ego vehicle's on-board sensors do not detect the CAV, even though it is within the range of the sensors, as a result of obstacles present in its line-of-sight. This creates a large scenario space where a scenario is made up of a combination of attribute  
Figure 2: Example NLOS scenario (T-junction).  
values over the underlying T-junction layout. Figure 3 (with each vertical line representing an attribute and where the attributes included are the starting position and speed of both the ego and CAV) shows a sample of 1000 possible scenarios (represented by the blue and red lines that connect the values of the attributes for that particular scenario) within the scenario space. Not all scenarios within this scenario space will lead to a collision and not all collision scenarios are critical in nature. In this Figure, the red and blue lines are mapped to the corresponding fitness value (detailed in the next section) and subsequently to its collision status; red lines indicate parameter value combinations that result in a collision and blue lines indicate those scenarios where collision is not possible.