# Selecting Non-Line of Sight Critical Scenarios for Connected Autonomous Vehicle Testing
## 6 Conclusions and Future Work  
This paper discusses a methodology for selecting NLOS critical scenarios. This has been achieved using a GA. The fitness function in the GA uses the criticality metric discussed in Section 3 to search the scenario space and filter out identified scenarios. From Figure 11, results demonstrating how effectively the genetic algorithm searched the scenario space can be seen. Several simulations of the genetic algorithm were run (with setting parameters as discussed in Section 4), and results are visualised in the graphs below:  
Figure 10: Comparison of distances from collision point.  
Figure 9: Scenario categories (orange dots indicate Category 3 scenarios and red dots Category 2).  
From the graphs (A and B) in Figure 11, it can be seen that the genetic algorithm was searching the scenario space effectively by having a population with a lower fitness after each generation. Based on the empirical data collated when testing the scenario, we can assume that there is high probability of obtaining a collision scenario. From this data, it can be noted that the GA is able to effectively locate valid scenarios. From the categories defined in Section 3.3, most scenarios seem to fall between Category 2 and 3. This is perhaps because the GA is getting stuck in a local minima and hence Category 1 is not identified. An improvement to the GA would be to locate Category 1 scenarios by coming out of the local minima. Another possibility is that they are not sufficiently complex because the current test scenarios are not Category 1. A more complex scenario would include more traffic participants, various driving manoeuvres and more underlying parameters, such as weather and road surface conditions. Furthermore, this research also addresses the need for vehicle-to-vehicle communication, as shown from the results. When the vehicle receives information beforehand, the distance to the collision point increases and thus reverts the criticality of the scenario.  
The methodology outlined within this paper can be easily expanded to a crossroad, developed into a roundabout or adapted to be a simpler curve (with blind corners) on the same road by removing one outgoing road. Once the underlying road network is selected, additional genes (to represent additional road user parameters, weather conditions, road surface conditions, etc.) can be added to an individual chromosome. Much of the GA will remain the same, with a need to review the fitness function so that it incorporates additional factors such as road surface conditions, thus identifying critical NLOS test scenarios which can be used to validate algorithms as discussed.  
Conceptualization, T.A., L.D. and D.P.; methodology, T.A. and L.D.; software, T.A. and D.P.; validation, T.A., L.D. and D.P.; investigation, T.A.; resources, T.A., L.D. and D.P.; data curation, T.A.; writing--original draft preparation, T.A.; writing--review and editing, T.A., L.D. and D.P.; visualization, T.A., L.D. and D.P.; supervision, D.E., L.D. and D.P.; project administration, L.D.; funding acquisition, No external funding. PhD project. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Can be made available upon request.  
The authors declare no conflict of interest.  
Figure 11: Fitness value convergence curves of two example scenarios (Scenario 19 in (**A**) and Scenario 8 in (**B**) of Table 2).