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Deep Reinforcement Learning-Based Pedestrian and Independent Vehicle Safety Fortification Using Intelligent Perception

Deep Reinforcement Learning-Based Pedestrian and Independent Vehicle Safety Fortification Using Intelligent Perception

Vijayakumar P., Jegatha Deborah L., Rajkumar S. C.
Copyright: © 2022 |Volume: 14 |Issue: 1 |Pages: 33
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781683181019|DOI: 10.4018/IJSSCI.291712
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MLA

Vijayakumar P., et al. "Deep Reinforcement Learning-Based Pedestrian and Independent Vehicle Safety Fortification Using Intelligent Perception." IJSSCI vol.14, no.1 2022: pp.1-33. http://doi.org/10.4018/IJSSCI.291712

APA

Vijayakumar P., Jegatha Deborah L., & Rajkumar S. C. (2022). Deep Reinforcement Learning-Based Pedestrian and Independent Vehicle Safety Fortification Using Intelligent Perception. International Journal of Software Science and Computational Intelligence (IJSSCI), 14(1), 1-33. http://doi.org/10.4018/IJSSCI.291712

Chicago

Vijayakumar P., Jegatha Deborah L., and Rajkumar S. C. "Deep Reinforcement Learning-Based Pedestrian and Independent Vehicle Safety Fortification Using Intelligent Perception," International Journal of Software Science and Computational Intelligence (IJSSCI) 14, no.1: 1-33. http://doi.org/10.4018/IJSSCI.291712

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Abstract

The Light Detection and Ranging (LiDAR) sensor is utilized to track each sensed obstructions at their respective locations with their relative distance, speed, and direction; such sensitive information forwards to the cloud server to predict the vehicle-hit, traffic congestion and road damages. Learn the behaviour of the state to produce an appropriate reward as the recommendation to avoid tragedy. Deep Reinforcement Learning and Q-network predict the complexity and uncertainty of the environment to generate optimal reward to states. Consequently, it activates automatic emergency braking and safe parking assistance to the vehicles. In addition, the proposed work provides safer transport for pedestrians and independent vehicles. Compared to the newer methods, the proposed system experimental results achieved 92.15% higher prediction rate accuracy. Finally, the proposed system saves many humans, animal lives from the vehicle hit, suggests drivers for rerouting to avoid unpredictable traffic, saves fuel consumption, and avoids carbon emission.

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