# Effects of Vehicular Communication on Risk Assessment in Automated Driving Vehicles
## 3 Automated Driving Risk Assessment
### Safe Triangle Analysis  
It is widely known that the best trade-off between human reaction time and collision risk computed from the machine is proper intervention timing for active safety function. To maximize driver acceptance and vehicle safety, these two factors are considered for whole driving speed range. For a human factor, driver's perception reaction time had been implemented and a stopping sight distance formula is introduced. The collision risk had been computed real time by finding corresponding human reaction time. This means that embedded human factor is interacting with machine which numerically computes the risky situation with Monte-Carlo simulation process. In addition, this graph is called single safety plane. Here a safe triangle is introduced in order to consider both normal and emergency driving situations. The driving situation changes many times and situation awareness is getting more important. The safe triangle includes two intervention points so that two modes are activated with proper timing.  
The algorithm aims to decide a proper intervention timing of two control functions for a fully automated driving. For a longitudinal safety, the safe triangle analysis detects a drive mode decision timing before active safety intervention function is activated. It can be achieved by finding the intervention point (moment) of active safety and drive mode. Between maximum collision risk and corresponding predicted human reaction time with respect to \(\mathbf{Dp_{max}}\mathbf{c_{p}}\), each crossing point is a best intervention point. To improve driver acceptance while ensuring safety and driving efficiency, a human driver's ability has to be implemented to active safety control and drive mode decision. It is well known that human reaction time and probabilistic collision risk well express a best trade-off between a human sense and automated function [5].  
In Figure 5, a remote vehicle sudden braking of 0.2 g during which a host vehicle is initially following the remote vehicle by driving 110 km/h of constant speed. Here two maximum predicted  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Speed (km/h)** & **Decision Sight Distance for Mode Decision (m)** \\ \hline
50 & 170 \\
60 & 205 \\
70 & 235 \\
80 & 270 \\
90 & 315 \\
100 & 355 \\
110 & 380 \\
120 & 415 \\
130 & 450 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Decision sight distance for a range of speed.  
human reaction times of two control functions are set to be 2.5 and 14 s, respectively. Since previous experimental data [9; 21] verified the validity of the 2.5 and 14 s as the optimal design perception-reaction time (human reaction time) for a safety and smoother operations of human-like driving. The collision probability, 0 to 100%, and predicted reaction time, 0 to 2.5 s, are corresponding to each other as described in Figure 5. More than 14 s of human reaction time describes enough of a situation for a driver to determine drive mode such as a lane keeping/changing safely. However, smaller than that demonstrates collision probability should be considered due to the fact that the driver and the safety system have to be cautious and to predict the remote vehicle's behavior.