Deep Reinforcement Learning for Autonomous Vehicle Control and Navigation
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How to Cite

[1]
Dr. Vijay Kumar, “Deep Reinforcement Learning for Autonomous Vehicle Control and Navigation”, Journal of Bioinformatics and Artificial Intelligence, vol. 2, no. 2, pp. 108–123, Jun. 2024, Accessed: Jul. 06, 2024. [Online]. Available: https://biotechjournal.org/index.php/jbai/article/view/44

Abstract

Advancements in the development of Automated vehicles (AVs) could greatly affect public transportation and road freight transport not only due to e-mobility and automated driving, but also due to connectivity and related changes in road design [1]. The development of autonomous vehicles (AVs) is also fundamentally reinventing the world's path towards the environment. For example, the advent of AVs is expected to reduce environmental strain as the transportation of AVs is expected to consume up to 90% less energy compared with traditional vehicles [2]. Moreover, AVs are the forerunners on the road. AVs have become increasingly interesting with the development of sensor technologies and intelligent system algorithms. This technology can be broken up into two sections, environment perception and car magnifying decision-making.

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References

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