# Selecting Non-Line of Sight Critical Scenarios for Connected Autonomous Vehicle Testing
## 5 Discussion  
This section discusses the efficacy of the proposed GA approach in identifying critical scenarios. This has been performed by discussing the validation of the GA selected scenarios on the MATLAB simulation platform and categorising them as discussed in Section 3.3. We highlight that experiments have been conducted in ideal scenarios and factored in realistic attributes, such as weather and road surface condition, that will make the selected scenarios more critical in nature. We go on to discuss that the scenarios and MATLAB platform can be utilised to test algorithms incorporating vehicular communication through another experiment and discuss a possible way to validate the success of the tested algorithms, i.e., through measurement of TTC. In Section 4, a lower bound test was conducted to ensure all scenario outputted from the GA are within the first three categories. A lower bound test is one that was conducted in the most pessimistic setting assuming that the ego vehicle does not react and apply brakes on detection of the obstacle. As discussed earlier, a critical scenario resides within Category 1 and 2. To determine this, a second experiment was conducted. This experiment differs from the initial experiment by including straightforward braking (assuming negligible algorithm processing and reaction time) such that when the sensors detect an oncoming obstacle, braking is applied. This experiment measures the distance from the point where the trajectories of the ego vehicle and CAV intersect (otherwise known as the collision point). Furthermore, a third experiment was set up which incorporated vehicular communication which was configured to send information from the CAV to the ego vehicle. The information included its current time, position, speed and heading [83]. The third experiment was set up to determine how early detection has an effect on distance from collision point. The sample results which consisted of critical scenarios from Section 4 (Table 2) were inputted into both simulation environments: a scenario with no communication (Experiment 2) and a scenario with communication (Experiment 3). Table 3 shows the stopping distance from the collision point without communication and with communication for the same underlying scenario. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{\
\begin{tabular}{c} U.K \\ **Speed** \\ **Limit** \\ **(mph)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Scenario** \\ **Speed** \\ **(m/s)** \\ \end{tabular} } & \multicolumn{4}{c}{**Best Chromosome**} \\ \cline{3-7}  & & & **Ego** & **CAV** & **Ego** & **CAV** & \\  & & **Ego** & **CAV** & **Initial** & **Initial** & **Fitness Value** \\  & & **Speed** & **Speed** & **Position** & **Position** & **Fitness Value** \\  & & **(m/s)** & **(m/s)** & **(x, y, z) (m)** & **(x, y, z) (m)** & \\ \hline \multirow{4}{*}{60} & 16 & 39.26 & 39.17 & [14.02, 1.47, 0] & [37.86, \(-\)22.98, 0] & 111.79 \\  & 17 & 24.75 & 27.11 & [16.46, 2.05, 0] & [37.59, \(-\)27.42, 0] & 130.46 \\  & 18 & 25.67 & 24.02 & [16.68, 1.53, 0] & [38.04, \(-\)26.41, 0] & 138.12 \\  & 19 & 35.58 & 32.86 & [16.87, 1.14, 0] & [37.30, \(-\)23.20, 0] & 107.88 \\  & 20 & 28.30 & 23.88 & [14.28, 2.08, 0] & [37.61, \(-\)25.60, 0] & 137.09 \\ \hline \multirow{4}{*}{70} & 21 & 37.37 & 34.69 & [12.13, 2.14, 0] & [38.61, \(-\)27.60, 0] & 107.47 \\  & 22 & 20.12 & 22.11 & [15.13, 1.56, 0] & [38.12, \(-\)27.66, 0] & 178.85 \\ \cline{1-1}  & 23 & 35.18 & 34.11 & [15.85, 1.62, 0] & [38.70, \(-\)27.38, 0] & 173.33 \\ \cline{1-1}  & 24 & 19.53 & 23.00 & [17.13, 1.87, 0] & [38.73, \(-\)26.59, 0] & 175.19 \\ \cline{1-1}  & 25 & 21.77 & 20.92 & [15.37, 1.16, 0] & [38.57, \(-\)26.94, 0] & 165.69 \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._From Table 3, it can be observed that the distance from the collision point is much greater when communication is introduced or when the ego vehicle is aware of the obstacle much earlier.