# Information Fusion in Autonomous Vehicle Using Artificial Neural Group Key Synchronization
## 4 Results and Discussions
The result is 0.701496, which is a lot better than the result 0.512374 in [10], 0.537924 in [14], 0.516357 in [34], 0.538632 in [12], 0.584326 in [11], 0.1329 in [35], 0.632558 in [36], and 0.629806 in [37]. Table 3 shows the \(p\_Value\) comparisons for the frequency strategy. Table 4 shows the outcomes for different \(\tau 1-\tau 2-\tau 3-N-\lambda\) values. Two columns define the minimum and maximum syncing times. The capacity of an adversary to formulate and implement the actions of approved TLTPMs is evaluated by invader syncing. The arrangements (8-8-8-16-8) and (8-8-8-128) provide the optimum protection against the invading system, as shown in Table 4. In all 700,000 trials, E will not respond like A and B's TLTPM. Table 5 displays the most cost-effective choices. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\tau 1-\tau 2-\tau 3-N-\lambda\) & **No. of Min** & **No. of Max** & **Min** & **Max** & **Attacker’s** & **\% of Attacker’s** \\ \(\tau 3-N-\lambda\) & **Coordination** & **Coordination** & **Coordination** & **Coordination** & **Successful** & **Successful** \\  & **Steps** & **Steps** & **Time (sec.)** & **Time (sec.)** & **Syncing** & **Syncing** \\ \hline (1-1-1-512-1) & 12 & 33 & 0.0616 & 3.3241 & 336,237 & 48.02 \\ (2-2-2-256-1) & 11 & 30 & 0.0697 & 2.2658 & 332,675 & 47.54 \\ (4-4-4-128-1) & 11 & 38 & 0.0773 & 2.2549 & 331,878 & 47.42 \\ (8-8-8-64-1) & 10 & 29 & 0.0725 & 3.0376 & 327,366 & 46.74 \\ (1-1-1-256-2) & 11 & 42 & 0.0699 & 3.1590 & 312,752 & 44.68 \\ (2-2-2-128-2) & 12 & 33 & 0.0681 & 652,7532 & 318,530 & 45.6 \\ (4-4-4-64-2) & 12 & 216 & 0.0655 & 112,2364 & 279,659 & 39.94 \\ (8-8-8-32-2) & 13 & 933 & 0.0588 & 69.2487 & 95,873 & 13.68 \\ (1-1-1-128-8) & 18 & 75 & 0.0617 & 2.0964 & 98,618 & 14.09 \\ (2-2-2-64-8) & 22 & 448 & 0.0777 & 3.8630 & 31,477 & 4.47 \\ (4-4-4-32-8) & 24 & 811 & 0.0496 & 6.5721 & 114 & 0.02 \\ (8-8-8-16-8) & 43 & 714 & 0.0335 & 0.0592 & 0 & 0.0 \\ (1-1-1-64-128) & 10 & 133 & 0.0218 & 0.6478 & 115,738 & 16.52 \\ (2-2-2-32-128) & 18 & 397 & 0.0546 & 3.4973 & 33,126 & 4.74 \\ (4-4-4-16-128) & 23 & 916 & 0.0379 & 4.0366 & 411 & 0.06 \\ (8-8-8-8-128) & 36 & 1118 & 0.0487 & 5.1897 & 0 & 0.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Outcomes of 700,000 Iterations of Various \(\tau 1-\tau 2-\tau 3-N-\lambda\). \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & \(p\_Value\) \\ \hline TLTPM & 0.701496 \\ CYTPM [10] & 0.512374 \\ [PERSON] and [PERSON] [14] & 0.537924 \\ [PERSON] et al. [34] & 0.516357 \\ VVTPM [12] & 0.538632 \\ GAN-DLTPM [11] & 0.584326 \\ [PERSON] et al. [35] & 0.1329 \\ [PERSON] et al. [36] & 0.632558 \\ [PERSON] et al. [37] & 0.629806 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Frequency Test’s \(p\_Value\). For various learning, weight range, and constant TLTPM size, Table 6 analyzes the synchronization periods of TLTPM and TPM-FPGA methods. When \(\lambda\) increases in all three rules, there is a propensity for the sync step to rise. In the \(\lambda\) range of 5 to 15, Hebbian hardly requires any sync stages than that of the other two rules, but when the \(\lambda\) rises, Hebbian uses more sync steps than the others. The Anti-Hebbian harmonizing times of the TLTPM and CVTPM strategies are compared in Table 7. The TLTPM's sync interval is noticeably shorter than the CVTPM's, as shown in table. The Anti-Hebbian, in comparing to others, requires less effort. The necessary synchronizing duration for the 512-bit key generation with varying synaptic ranges is shown in Figure 18. In large scale networks, the Random Walk rule surpasses other learning techniques. Table 8 compares the TLTPM and VVTPM time synchronization approaches using the Random Walk method. The TLTPM, which uses the Random Walk learning algorithm, has a much faster coordinating period than the VVTPM.