## 5 Sensitivity Analysis of AV to Situations and Events for the Generation of Concrete Scenarios
#### 5.1.1 Definition of the Sensitivity of AV to Logical Situations  
The calculation of the a priori sensitivity of the AV to logical situations is obtained in two steps. First, we consider the a priori sensitivity of the AV to each value range characterizing the generation parameters. This is done with the functional architecture of the AV composed as follows: perception, decision and actuation. Five sensor technologies \begin{table}
\begin{tabular}{c} \hline Logical situation 1 (Y1): A1\_1, A2\_1, A3\_1, A4\_1, A5\_1 = 2 lanes * Curvature.Low * Speed\_Ego.Low * Rain.Light * Speed\_OtherVehicle.1.Low \\ \hline Logical situation 2 (Y2): A1\_1, A2\_2, A3\_1, A4\_1, A5\_1 = 2 lanes * Curvature.High * Speed\_Ego.Low * Rain.Light * Speed\_OtherVehicle.1.Low \\ \hline Logical situation 3 (Y3): A1\_1, A2\_1, A3\_1, A4\_2, A5\_1 = 2 lanes * Curvature.Low * Speed\_Ego.Low * Rain.Heavy * Speed\_OtherVehicle.1.Low \\ \hline Logical situation 4 (Y4): A1\_1, A2\_2, A3\_1, A4\_2, A5\_1 = 2 lanes * Curvature.High * Speed\_Ego.Low * Rain.Heavy * Speed\_OtherVehicle.1.Low \\ \hline Logical situation 5 (Y5): A1\_2, A2\_1, A3\_1, A4\_1, A5\_1 = (3 and more) lanes * Curvature.Low * Speed\_Ego.Low * Rain.Light * Speed\_OtherVehicle.1.Low \\ \hline Logical situation 6 (Y6): A1\_2, A2\_2, A3\_1, A4\_1, A5\_1 = (3 or more) lanes * Curvature.High * Speed\_Ego.Low * Rain.Light * Speed\_OtherVehicle.1.Low \\ \hline Logical situation 7 (Y7): A1\_2, A2\_1, A3\_1, A4\_2, A5\_1 = (3 and more) lanes * Curvature.Low * Speed\_Ego.Low * Rain.Heavy * Speed\_OtherVehicle.1.Low \\ \hline Logical situation 8 (Y8): A1\_2, A2\_2, A3\_1, A4\_2, A5\_1 = (3 or more) lanes * Curvature.High * Speed\_Ego.Low * Rain.Heavy * Speed\_OtherVehicle.1.Low \\ \hline \end{tabular}
\end{table} 
Table 4: Examples of logical situations obtained by combining the variation ranges. ensure perception functionalities and are grouped as follows: radar (long-range and short-range), ultrasonic, lidar, stereoscopic (3D vision) and monocular cameras. The proposed process for determining sensitivity is not tied to specific components. It offers the possibility of being adaptable to the specificities of the components that will be used on a given vehicle. Thus, for each considered range, with the help of the expert, we answer the following question: is the sensor technology under consideration (or decision or actuation) sensitive in this range? The expected answer from the expert is either yes or no. Then, the scale described in Table 5 makes it possible to determine the level of sensitivity of the AV to the range according to the impacted AV component(s). Next, all the responses obtained will be used to determine the a priori sensitivity of the AV to each logical situation. Indeed, the logical situation is a combination of ranges of values. We propose to calculate the a priori sensitivity of the AV to a combination of ranges (and therefore to a logical situation) by aggregating the sensitivities associated with the ranges. We assume that each component (perception, decision, actuation) makes a maximum contribution of 1 to the overall sensitivity of a logical situation. Since the AV has five main types of sensor technologies, each sensor technology, therefore, is considered to make a contribution of 0.2 towards the sensitivity to the logical situation if it is impacted by at least one range of variation. The decision made by the AV (in the absence of any lure of the sensors) or the actuation makes, respectively, a contribution of 1 in the calculation of sensitivity to the logical situation, when it is misled by at least one of the ranges of values composing the logical situation. Table 6 synthetizes the contribution of each component in obtaining the sensitivity of a logical situation. Furthermore, when generating scenarios, the randomness of their occurrences must be considered. We, therefore, propose the implementation of a sensitivity distribution function.