Behavioral Cloning Strategies in Steering Angle Prediction: Applications in Mobile Robotics and Autonomous Driving
Artificial neural networks (ANNs) are artificial intelligence techniques that have made autonomous driving more efficient and accurate; however, autonomous driving faces ongoing challenges in the accuracy of decision making based on the analysis of the vehicle environment. A critical task of ANNs is...
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MDPI AG
2024-10-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/15/11/486 |
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| author | Sergio Iván Morga-Bonilla Ivan Rivas-Cambero Jacinto Torres-Jiménez Pedro Téllez-Cuevas Rafael Stanley Núñez-Cruz Omar Vicente Perez-Arista |
| author_facet | Sergio Iván Morga-Bonilla Ivan Rivas-Cambero Jacinto Torres-Jiménez Pedro Téllez-Cuevas Rafael Stanley Núñez-Cruz Omar Vicente Perez-Arista |
| author_sort | Sergio Iván Morga-Bonilla |
| collection | DOAJ |
| description | Artificial neural networks (ANNs) are artificial intelligence techniques that have made autonomous driving more efficient and accurate; however, autonomous driving faces ongoing challenges in the accuracy of decision making based on the analysis of the vehicle environment. A critical task of ANNs is steering angle prediction, which is essential for safe and effective navigation of mobile robots and autonomous vehicles. In this study, to optimize steering angle prediction, NVIDIA’s architecture was adapted and modified along with the implementation of the Swish activation function to train convolutional neural networks (CNNs) by behavioral cloning. The CNN used human driving data obtained from the UDACITY beta simulator and tests in real scenarios, achieving a significant improvement in the loss function during training, indicating a higher efficiency in replicating human driving behavior. The proposed neural network was validated through implementation on a differential drive mobile robot prototype, by means of a comparative analysis of trajectories in autonomous and manual driving modes. This work not only advances the accuracy of steering angle predictions but also provides valuable information for future research and applications in mobile robotics and autonomous driving. The performance results of the model trained with the proposed CNN show improved accuracy in various operational contexts. |
| format | Article |
| id | doaj-art-9183565c5de04b4b8569796a2bc07cdd |
| institution | OA Journals |
| issn | 2032-6653 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-9183565c5de04b4b8569796a2bc07cdd2025-08-20T02:04:43ZengMDPI AGWorld Electric Vehicle Journal2032-66532024-10-01151148610.3390/wevj15110486Behavioral Cloning Strategies in Steering Angle Prediction: Applications in Mobile Robotics and Autonomous DrivingSergio Iván Morga-Bonilla0Ivan Rivas-Cambero1Jacinto Torres-Jiménez2Pedro Téllez-Cuevas3Rafael Stanley Núñez-Cruz4Omar Vicente Perez-Arista5División de Ingeniería Eléctrica, Institiuto Tecnológico Superior de Huauchinango TECNM, Huauchinango 73173, Puebla, MexicoDepartamento de Posgrado, Universidad Politécnica de Tulancingo, Tulancingo de Bravo 43629, Hidalgo, MexicoDivisión de Ingeniería Eléctrica, Institiuto Tecnológico Superior de Huauchinango TECNM, Huauchinango 73173, Puebla, MexicoDivisión de Ingeniería Eléctrica, Institiuto Tecnológico Superior de Huauchinango TECNM, Huauchinango 73173, Puebla, MexicoDepartamento de Posgrado, Universidad Politécnica de Tulancingo, Tulancingo de Bravo 43629, Hidalgo, MexicoDepartamento de Posgrado, Universidad Politécnica de Tulancingo, Tulancingo de Bravo 43629, Hidalgo, MexicoArtificial neural networks (ANNs) are artificial intelligence techniques that have made autonomous driving more efficient and accurate; however, autonomous driving faces ongoing challenges in the accuracy of decision making based on the analysis of the vehicle environment. A critical task of ANNs is steering angle prediction, which is essential for safe and effective navigation of mobile robots and autonomous vehicles. In this study, to optimize steering angle prediction, NVIDIA’s architecture was adapted and modified along with the implementation of the Swish activation function to train convolutional neural networks (CNNs) by behavioral cloning. The CNN used human driving data obtained from the UDACITY beta simulator and tests in real scenarios, achieving a significant improvement in the loss function during training, indicating a higher efficiency in replicating human driving behavior. The proposed neural network was validated through implementation on a differential drive mobile robot prototype, by means of a comparative analysis of trajectories in autonomous and manual driving modes. This work not only advances the accuracy of steering angle predictions but also provides valuable information for future research and applications in mobile robotics and autonomous driving. The performance results of the model trained with the proposed CNN show improved accuracy in various operational contexts.https://www.mdpi.com/2032-6653/15/11/486steering angle predictionbehavioral cloningconvolutional neural networks (CNNs)autonomous drivingmobile robotics |
| spellingShingle | Sergio Iván Morga-Bonilla Ivan Rivas-Cambero Jacinto Torres-Jiménez Pedro Téllez-Cuevas Rafael Stanley Núñez-Cruz Omar Vicente Perez-Arista Behavioral Cloning Strategies in Steering Angle Prediction: Applications in Mobile Robotics and Autonomous Driving World Electric Vehicle Journal steering angle prediction behavioral cloning convolutional neural networks (CNNs) autonomous driving mobile robotics |
| title | Behavioral Cloning Strategies in Steering Angle Prediction: Applications in Mobile Robotics and Autonomous Driving |
| title_full | Behavioral Cloning Strategies in Steering Angle Prediction: Applications in Mobile Robotics and Autonomous Driving |
| title_fullStr | Behavioral Cloning Strategies in Steering Angle Prediction: Applications in Mobile Robotics and Autonomous Driving |
| title_full_unstemmed | Behavioral Cloning Strategies in Steering Angle Prediction: Applications in Mobile Robotics and Autonomous Driving |
| title_short | Behavioral Cloning Strategies in Steering Angle Prediction: Applications in Mobile Robotics and Autonomous Driving |
| title_sort | behavioral cloning strategies in steering angle prediction applications in mobile robotics and autonomous driving |
| topic | steering angle prediction behavioral cloning convolutional neural networks (CNNs) autonomous driving mobile robotics |
| url | https://www.mdpi.com/2032-6653/15/11/486 |
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