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Artificial Intelligence Enabled Future Wireless Electric Vehicles with Multi-Model Learning and Decision Making Models
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AI
DOI：10.26599/TST.2023.9010094.png)
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摘要
En 中文
In the contemporary era, driverless vehicles are a reality due to the proliferation of distributed technologies, sensing technologies, and Machine to Machine (M2M) communications. However, the emergence of deep learning techniques provides more scope in controlling and making such vehicles energy efficient. From existing methods, it is understood that there have been many approaches found to automate safe driving in autonomous and electric vehicles and also their energy efficiency. However, the models focus on different aspects separately. There is need for a comprehensive framework that exploits multiple deep learning models in order to have better control using Artificial Intelligence (AI) on autonomous driving and energy efficiency. Towards this end, we propose an AI-based framework for autonomous electric vehicles with multi-model learning and decision making. It focuses on both safe driving in highway scenarios and energy efficiency. The deep learning based framework is realized with many models used for localization, path planning at high level, path planning at low level, reinforcement learning, transfer learning, power control, and speed control. With reinforcement learning, state-action-feedback play important role in decision making. Our simulation implementation reveals that the efficiency of the AI-based approach towards safe driving of autonomous electric vehicle gives better performance than that of the normal electric vehicles.
Keyword:
Deep learning
Wireless communication
Wireless sensor networks
Decision making
Velocity control
Transfer learning
Reinforcement learning
wireless vehicles
deep learning
multi-model learning
reinforcement learning
Artificial Intelligence (AI)
期刊
T
IF:
3.5
论文数:
987
被引数:
2.5K
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