## 7 Conclusions  
To achieve autonomous emergency braking and protect pedestrians, a decision-making algorithm and a dependable sensor fusion architecture were proposed in this paper. The proposed model had three types of sensors: radar, lidar, and vision sensors. The implementation of a simple trajectory prediction and a data-association technique was performed with an emphasis on the effective and trustworthy tracking of various target kinds. To find pedestrians that were either obscured or not spotted, a possible track was used based on a momentary assessment. Track management was provided by the suggested multi-sensor fusion system, which was stable and dependable. Furthermore, the EuroNCAP AEB pedestrian scenarios were thoroughly examined, and suitable collision decision and prediction algorithms were provided. The failure or false positives of any sensor may cause disturbances or accidents; therefore, to obtain an accurate performance of the system, the proposed system managed the trade-off between AEB performance and false positives by setting the threshold for AEB activation and cautiously preventing false positives. This study demonstrated that the suggested AEBS based on sensor fusion is a very reliable option for emergency braking in the autonomous vehicle since it prevented the system from failing in the case of false-positive detection of any one sensor.  
To obtain more robust and reliable detection from sensors, advanced fault-tolerant approaches to the sensor fusion part of the system may be used in the future. To increase the system's accuracy and efficiency, the processing delays caused by the environment may also be considered.  
Conceptualization, A.A.A.; Formal analysis, R.B.S.; Funding acquisition, T.A.; Investigation, R.B.S.; Methodology, A.A.A.; Project administration, A.A.A. and T.A.; Resources, A.A.A. and T.A.; Software, A.A.A. and T.A.; Supervision, A.A.A. and T.A.; Validation, A.A.A.; Visualization, A.A.A.; Writing--original draft, R.B.S.; Writing--review & editing, A.A.A. and T.A. All authors have read and agreed to the published version of the manuscript.  
The authors received no financial support for the research, authorship, and/or publication of this article. The authors would like to thank their colleagues for their suggestions on how to improve the paper's quality. The authors declare no conflict of interest in preparing this paper.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Properties** & **Radar** & **Lidar** & **Camera** & **Proposed Fusion** & **Previous Fusion** \\ \hline
**Object Detection** & Strength & Strength & Capability & Strength & Strength \\ \hline
**Pedestrian Detection** & Weakness & Capability & Strength & Strength & Capability \\ \hline
**Weather Conditions** & Strength & Capability & Weakness & Strength & Capability \\ \hline
**Lightening Conditions** & Strength & Strength & Weakness & Strength & Capability \\ \hline
**Dirt** & Strength & Capability & Weakness & Strength & Capability \\ \hline
**Velocity** & Strength & Capability & Capability & Strength & Strength \\ \hline
**Distance Accuracy** & Capability & Strength & Capability & Strength & Capability \\ \hline
**Distance Range** & Strength & Capability & Capability & Strength & Strength \\ \hline
**Data Density** & Weakness & Capability & Strength & Strength & Strength \\ \hline
**Classification** & Weakness & Capability & Strength & Strength & Capability \\ \hline
**Packaging** & Strength & Weakness & Capability & Strength & Strength \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detail properties of radar, vision, lidar, proposed fusion, and previous fusion.