Name
Abstract | ||
|---|---|---|
Making autonomous driving a safe, feasible, and better alternative is one of the core problems the world is facing today. The horizon of the applications of AI and deep learning has changed the perspective of the human mind. Initially, what used to be thought of as the subtle impossible task is applicable today, and that too in the feasibly efficient way. Computer vision tasks powered with highly tuned CNNs are outperforming humans in many fields. Introductory implementations of the autonomous vehicle were merely achieved using raw image processing, and hard programmed rule-based logic systems along with machine/deep learning were used as secondary objective handlers. With the autonomous driving method proposed by Nvidia, the usability of CNNs is more adequate, adaptable, and applicable. In this paper, we propose the ensemble implementation of CNN-based regression models for autonomous driving. We have taken simulator generated driving view image dataset along with a mapped file of steering angle in radians. After applying image pre-processing and augmentation, we have used two CNN models along with their ensemble and compared their performance to minimize the risks of unsafe driving. We have compared Nvidia proposed CNN, MobileNet-V2 as regression model and Ensemble-M results for comparison of their respective performance, MSE scores and compute time to process. In result analysis, the MobileNet-V2 model performs better in densely featured roads and the Nvidia model performs better in sparsely featured roads, whereas Ensemble-M normalizes the performance of both models and efficiently results in the least MSE score (0.0201) with the highest computation time utilization making autonomous driving better, safer alternative. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s00500-021-05954-4 | SOFT COMPUTING |
Keywords | DocType | Volume |
Autonomous driving, CNN, Deep learning, ImageNet, MobileNetV2, Nvidia model, Self-driving, Simulator | Journal | 25 |
Issue | ISSN | Citations |
18 | 1432-7643 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meenu Gupta | 1 | 13 | 2.03 |
| Vikalp Upadhyay | 2 | 0 | 0.34 |
| Prince Kumar | 3 | 0 | 0.34 |
| Fadi M. Al-Turjman | 4 | 111 | 18.27 |