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·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667
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The Journal of Information and Optimization Sciences (JIOS) is a world leading journal publishing high quality, rigorously peer-reviewed original research in all mathematically-oriented theoretical and applied topics in information sciences, optimization sciences and related areas since 1980. Subjects include but are not limited to:
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Issues up to 2022 co-published with and available at:
Deep learning for optimized path planning in autonomous vehicles by integrating reinforcement learning with convolutional neural networks
Monali G. Dhotethakaremonali@gmail.comDepartment of Applied Mathematics and Humanities Yeshwantrao Chavan College of EngineeringNagpur, Maharashtra, 441110, IndiaView full profile →
, Bhushan Marutirao Nanchebmnanche@gmail.comDepartment of Information Technology D. Y. Patil College of Engineering AkurdiPune, Maharashtra, 411044, IndiaView full profile →
, *Parikshit MahalleCorresponding authorparikshit.mahalle@vit.eduDepartment of Artificial Intelligence and Data Science Vishwakarma Institute of TechnologyDepartment of Artificial Intelligence & Data Science Vishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile →
, Syed Sumera Alisyed.sumera.ali@gmail.comDepartment of Electronics & Communication CSMSS Chhatrapati Shahu College of EngineeringDepartment of Electronics & Communication CSMSS Chh. Shahu College of EngineeringAurangabad, Maharashtra, 431001, IndiaView full profile →
, Monali Gulhanemonali.gulhane4@gmail.comSymbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University)Department of Computer Science & Engineering Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University)Pune, Maharashtra, 440008, IndiaView full profile →
, Vijay Suresh Karwandevjy725@gmail.comDepartment of Computer science and Engineering Everest Educational Society Group of Institutions College of Engineering and TechnologyChatrapati Sambhaji Nagar, Maharashtra, 431006, IndiaView full profile →
* Corresponding author · click or hover a name for details
Autonomous vehicles (AVs) need efficient path planning systems to get around in complicated settings safely and successfully. It can be hard for traditional methods to change to changing situations and find the best solutions for a wide range of goals. As a reaction, this study describes a new method that combines reinforcement learning (RL) with convolutional neural networks (CNNs) to help autonomous vehicles find the best routes. Using RL to learn how to make decisions by interacting with the world and CNNs to get useful spatial features from sensor data is what our suggested system is all about. By training these parts together, the model learns to move through different settings while maximizing different goals, like reducing trip time, energy use, or the chance of a crash. A lot of tests are done on virtual and real-world datasets to see how well our approach works. The results show that it works better than standard methods and other deep learning techniques. In addition, we do in-depth ablation studies to look at how different parts and hyperparameters affect the total performance. Our research shows that combining RL and CNNs could greatly improve the ability of self-driving cars to plan their routes, which would allow for better and more efficient travel in real life.
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