DEEP REINFORCEMENT LEARNING FOR AUTONOMOUS DRIVING: CHALLENGES, SOLUTIONS, AND FUTURE DIRECTIONS
Keywords:
Deep Reinforcement Learning, Autonomous Driving, Artificial Intelligence, Machine Learning, SimulationAbstract
Autonomous driving has emerged as a transformative application of artificial intelligence and machine learning. Deep Reinforcement Learning (DRL) is particularly promising for autonomous driving due to its capacity to optimize decision-making through interactions with the environment. This paper reviews the state-of-the-art in DRL for autonomous driving as of 2021, discussing key challenges, proposed solutions, and future research directions. We examine DRL’s integration with sensor technologies, simulation environments, and real-world applications. A case study utilizing a DRL-based model in a simulated urban driving environment is presented, including performance metrics and data visualizations.
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