# CAVVPM: Challenge-Based Authentication and Verification of Vehicle Platooning at Motorway
## 5 Evaluation
### Performance of CAVVPM  
Before evaluating CAVVPM's behavior as a result of multiple protocols, we first examined its functionality. We ran a simulation where we accompanied a verifier V and a candidate C on a motorway. The following range was controlled using the ACC model provided in Section 3.3. Table 1 contains a list of the simulation's parameters. Initially, platooning took place at 30 m/s (108 km/h) with no other vehicles. Distance \(dref\) 45 m, which equates to a 1.5 s time delay, was fixed. The verifier sent random physical challenges to test the candidate's ability to conduct changes at random throughout the preceding distance of 1000 m in 33 s.  
**Time for a verification**: Evaluation criteria included the time it took for CAVVPM to accept new recruits. The ACC characteristics and the range of physical difficulties can be used to estimate the amount of time it takes to complete the task. **Investigating the ACC's effects:** The ACC settings control threshold deadlines. The vehicle's speed is controlled by parameter \(\gamma\), in particular, concerning the checkpoint's range. At a radius of 3 m from the dref, the checkpoint in Figure 8 indicates the person's acceleration, speed, and the following field with time. Figure 8A shows that the vehicle's starting speed progressively decreases until the milestone arrives when it stops. A virtually undetectable change in rate is demonstrated by the speed difference of 0.6 m/s (2 Km/h). In addition, we see a drop in speed and momentum difference with a longer delay until the checkpoint is reached once \(\gamma\) is reduced to 0.1. We used \(\gamma\) = 0.4 for the remainder of our runs. Figure 9 displays the Markov chain model, the verifier's chosen checkpoints, and the N potential states for the vehicle R.  
The checkpoint's range allowance \(\gamma\) is another crucial factor that affects the latency. When the vehicle is near a checkpoint, it swiftly converges and adjusts its location to arrive at the checkpoint on time. The timeframe may be reduced by raising the distance tolerance. Figure 10A illustrates that \(\gamma\) is approximately equal to the deadline duration. The \(\gamma\) = 0.3 m we used in our simulation is close to the standard car sensor resolution [29]. Figure 10A This chart shows how long it will take to complete a checkpoint 3 m distant from dref as a function of the range threshold \(\gamma\) and (B) the limit time as a function of milestone range, while \(\gamma\) = 0.3 m.  
Figure 10B shows the limit as a function of the distance to the checkpoint. We see that deadlines expand with distance, but the connection is not straightforward. The ACC theory justifies this. We also observe that limits are not symmetric when the same distance must be reached forward and backward due to differing speeds. **Impact of traffic**: Physical obstacles are seen as a continual source of movement for our verifier. The verifier's speed and the candidate's ACC approach to a checkpoint may be affected by traffic. We simulated a car driving at 27 m/s approaching the verifier to examine this effect. While the candidate is trying to reach a checkpoint, [PERSON] slows its speed to 27 m/s to maintain a safe distance. Figure 11 depicts the candidate's time-dependent acceleration, velocity, and following distance for a checkpoint 42 m from V. According to Figure 10, the time it takes to reach the checkpoint has risen from 7.6 s to 13.6 s. As a result of [PERSON]'s braking, the candidate could advance beyond the checkpoint before regaining the checkpoint. In other words, if the verifier's speed changes, a legitimate candidate will miss the initial deadline. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Velocity of \(V\) and \(C\) & 30 m/s \\ \hline \(d_{\mathit{ref}}\) & 1.6... \(  
To solve this issue, there are two ways to proceed. First, if the verifier's velocity dramatically fluctuates, it is best to disregard any challenges, then repeat them when the velocity stabilizes.