## 4 Results
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Accuracy/Speed/Hardware** \\ \hline [PERSON] et al. (2011) [33] & 83.71\%/16.39 fps/CPU (i7-4720 HQ) \\ [PERSON] et al. (2014) [34] & 85.56\%/9.09 fps/CPU (i7-4720 HQ) \\ [PERSON] et al. (2016) [36] & 91.04\%/3.27 fps/CPU (i7-4720 HQ) \\ [PERSON] et al. (2016) [36] & 91.04\%/20.83 fps/GPU (Tesla K40) \\ [PERSON] et al. (2016) [35] & 94.50\%/111.11 fps/ Intel Xeon 64 + GPU (GTX 970) \\ [PERSON] et al. (2017) [37] & 88.53\%/158.73 fps/GPU (GTX 970) \\ [PERSON] and [PERSON] (2017) [38] & 99.72\%/333.3 fps / CPU (i7-3920 XM) \\ [PERSON] et al. (2019) [10] & 96.07\%/86.34 fps/GPU (GTX 970M) \\ [PERSON] et al. (2021) [39] & 99.89\%/10.00 fps/GPU (N/A) \\ [PERSON] et al. (2021) [40] & 98.3\%/3.00 fps/GPU (Nvidia Tesla V100 GP) \\ [PERSON] et al. (2020) [29] & 98.83\%/10.00 fps/GPU (Unknown) \\ This work & 97.71\%/91.78 fps/GPU (GTX 3080 Laptop) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative results for the proposed algorithm compared with previous works. In most cases, our algorithm outperforms previous works in terms of accuracy and processing speed. the ability to refine and combine candidate solutions iteratively contributes to resilience in the face of such challenges. Figure 4: The performance of the algorithm, sequences 11 to 14 from the KITTI dataset, without ground truth data. The results were obtained from the KITTI evaluation platform, where our algorithm was submitted for evaluation. These evaluations demonstrated that the proposed algorithm maintains a high level of precision across all tested scenarios. Additionally, we tested the algorithm on the KITTI dataset, which, while not specifically designed to simulate extreme motion blur or occlusions, presents a diverse range of urban and suburban driving scenarios. These conditions include varying lighting, partial occlusions, and sensor noise. The algorithm performed consistently with high accuracy (up to 97.4%), demonstrating its effectiveness in real-world conditions where such disturbances are common. The robustness observed in these tests suggests that the approach can handle moderate motion blur and occlusions, as well as noisy sensor data, which are typical challenges for ego-motion estimation systems in autonomous vehicles. Although further testing with more severe conditions is always beneficial, the performance on the KITTI dataset suggests that our method is already well equipped to manage common sensor imperfections encountered in autonomous driving scenarios. Finally, in terms of energy consumption, we acknowledge that energy efficiency is a significant consideration in the design of autonomous vehicle systems, particularly when high-performance algorithms are deployed in embedded platforms. CUDA implementation, leveraging the parallel processing power of GPUs, is inherently more energy efficient than traditional CPU-based computations for large-scale optimization tasks. By reducing the processing time required to evaluate multiple candidate solutions in real time, our algorithm minimizes the overall energy consumption. However, it is important to note that exact energy consumption depends on factors such as the GPU model used, the complexity of the task, and the size of the input data. For instance, our experiments using the GTX 3080 laptop GPU yield high processing speeds with relatively lower energy overhead compared with traditional methods that rely on sequential CPU processing. For safety implications, real-time processing failures in autonomous vehicles can have significant consequences, including potential accidents or system malfunctions. To mitigate these risks, our algorithm is designed with multiple layers of redundancy and error handling. The iterative approach of the genetic algorithm ensures that candidate solutions are continuously evaluated and refined, making it more resilient to occasional computational errors or sensor anomalies.