## 5 Results
### Experiment #3--Benchmarking AIM5 LA  
Finally, after the training process, AIM5 LA was compared with other AIM and traffic-light based protocols in the test scenario described above. The results are included in Table 4.  
AIM5 LA presents an excellent performance, being the only AIM protocol able to reduce accidents in the test scenario. Looking at other metrics such as time loss, waiting time, pollution, or fuel consumption metrics, AIM5 LA can achieve a performance similar to other AIM techniques such as the one proposed by [PERSON] et al. (2015) or _adv_.RAIM ([PERSON] et al., 2017). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & **Time Loss (s)** & **Collisions** & \begin{tabular}{c} **Waiting Time** \\ **(s)** \\ \end{tabular} & \begin{tabular}{c} **CO\({}_{2}\) **Emiss.** \\ **(g)** \\ \end{tabular} & \begin{tabular}{c} **PMx Emiss.** \\ **(mg)** \\ \end{tabular} & \
\begin{tabular}{c} **Fuel Cons.** \\ **(m)** \\ \end{tabular} &
\begin{tabular}{c} **Elect. Cons.** \\ **(W)** \\ \end{tabular} \\ \hline FX30 & 79.61 \(\pm\) 8.98 & 0 \(\pm\) 0 & 61.32 \(\pm\) 7.99 & 101.78 \(\pm\) 15.11 & 78.41 \(\pm\) 8.54 & 391.12 \(\pm\) 64.51 & 103.48 \(\pm\) 11.51 \\ FX60 & 70.16 \(\pm\) 11.41 & 0 \(\pm\) 0 & 50.71 \(\pm\) 6.11 & 89.64 \(\pm\) 9.87 & 66.98 \(\pm\) 7.21 & 333.47 \(\pm\) 42.66 & 99.74 \(\pm\) 9.63 \\ FX90 & 72.58 \(\pm\) 7.42 & 0 \(\pm\) 0 & 55.65 \(\pm\) 7.05 & 95.87 \(\pm\) 8.45 & 72.54 \(\pm\) 7.88 & 351.52 \(\pm\) 39.98 & 110.88 \(\pm\) 9.88 \\ iREDVD ([PERSON] et al., 2017) & 34.97 \(\pm\) 3.32 & 0 \(\pm\) 0 & 32.23 \(\pm\) 4.44 & 53.44 \(\pm\) 3.22 & 39.74 \(\pm\) 6.11 & 205.25 \(\pm\) 13.14 & 66.27 \(\pm\) 4.48 \\ _adv_.[PERSON] ([PERSON] et al., 2017) & 5.11 \(\pm\) 1.24 & 49.91 \(\pm\) 9.89 & 0.25 \(\pm\) 0.03 & 25.48 \(\pm\) 2.69 & 18.99 \(\pm\) 2.81 & 124.47 \(\pm\) 12.35 & 33.74 \(\pm\) 3.81 \\ [PERSON] et al. (2017) & 4.98 \(\pm\) 1.18 & 27.13 \(\pm\) 3.11 & 0.24 \(\pm\) 0.03 & 26.93 \(\pm\) 2.33 & 17.52 \(\pm\) 1.99 & 118.94 \(\pm\) 18.74 & 31.67 \(\pm\) 3.39 \\ AIM5 LA\_v0.1 & 4.12 \(\pm\) 1.41 & 32.01 \(\pm\) 4.98 & 0.24 \(\pm\) 0.02 & 26.18 \(\pm\) 1.29 & 17.82 \(\pm\) 1.97 & 119.14 \(\pm\) 15.29 & 30.26 \(\pm\) 3.21 \\ AIM5 LA\_v0.2 & 4.89 \(\pm\) 1.93 & 3.22 \(\pm\) 2.61 & 0.28 \(\pm\) 0.03 & 26.96 \(\pm\) 1.46 & 18.49 \(\pm\) 1.89 & 126.46 \(\pm\) 18.46 & 32.34 \(\pm\) 2.94 \\ AIM5 LA & 5.42 \(\pm\) 1.29 & 0 \(\pm\) 0 & 0.31 \(\pm\) 0.02 & 27.52 \(\pm\) 1.97 & 19.22 \(\pm\) 2.14 & 131.87 \(\pm\) 17.42 & 34.29 \(\pm\) 3.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of testing scenario. Figure 11: AIM5 LA training results. The metrics show the mean (solid line) value and standard deviation (shaded area) of the 3 runs: (**a**) Average episode reward; (**b**) Time Loss (s); (**c**) Number of collisions. However, it should be noted that the performance offered by AIM5 LA is limited by the elimination of collisions, therefore obtaining a similar result gives a higher value to our proposal. On the other hand, comparing AIM5 LA with the v0.1 version of AIM5 LA (which only considers the previous latency of the _ego_-vehicle), we see that there are some collisions that AIM5 LA_v0.1 is not able to eliminate due to the instability of the channel in the latency behavior, as well as due to the influence of other AVs. Last, when compared to AIM5 LA_v0.2 (that is, AIM5 LA_v0.1 plus the latency prediction module), the result is significantly improved. However, there is still a small number of collisions that are completely eliminated when using the final version of AIM5 LA. In general, AIM5 LA achieves great performance in all the analyzed metrics, showing a reduction in time loss of up to 92.25% when compared to other traditional traffic light control (FX) algorithms, or a reduction of more than 99.52% in waiting time.