Self Training Autonomous Driving Agent

Kotyan, Shashank; Vargas, Danilo; Udutalapally, Venkanna

doi:10.23919/sice.2019.8859883

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Another approach could be the simplification of the unstructured data. In [71] Kotyan et al uses the difference image as the background subtraction between the two consecutive frames as an input, assuming this image contains the motion of the foreground and the underlying neural network would focus more on the features of the foreground than the background. By using the same training algorithm, their results showed that the including difference image instead of the original unprocessed input needs approximately 10 times less training steps to achieve the same performance.…”

Section: E Observation Spacementioning

confidence: 99%

Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles

Aradi

2022

IEEE Trans. Intell. Transport. Syst.

310

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Academic research in the field of autonomous vehicles has reached high popularity in recent years related to several topics as sensor technologies, V2X communications, safety, security, decision making, control, and even legal and standardization rules. Besides classic control design approaches, Artificial Intelligence and Machine Learning methods are present in almost all of these fields. Another part of research focuses on different layers of Motion Planning, such as strategic decisions, trajectory planning, and control. A wide range of techniques in Machine Learning itself have been developed, and this article describes one of these fields, Deep Reinforcement Learning (DRL). The paper provides insight into the hierarchical motion planning problem and describes the basics of DRL. The main elements of designing such a system are the modeling of the environment, the modeling abstractions, the description of the state and the perception models, the appropriate rewarding, and the realization of the underlying neural network. The paper describes vehicle models, simulation possibilities and computational requirements. Strategic decisions on different layers and the observation models, e.g., continuous and discrete state representations, grid-based, and camera-based solutions are presented. The paper surveys the state-of-art solutions systematized by the different tasks and levels of autonomous driving, such as carfollowing, lane-keeping, trajectory following, merging, or driving in dense traffic. Finally, open questions and future challenges are discussed.

show abstract

Section: E Observation Spacementioning

confidence: 99%

Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles

Aradi

2022

IEEE Trans. Intell. Transport. Syst.

310

View full text Add to dashboard Cite

show abstract

“…Another approach could be the simplification of the unstructured data. In [58] Kotyan et al uses the difference image as the background subtraction between the two consecutive frames as an input, assuming this image contains the motion of the foreground and the underlying neural network would focus more on the features of the foreground than the background. By using the same training algorithm, their results showed that the including difference image instead of the original unprocessed input needs approximately 10 times less training steps to achieve the same performance.…”

Section: E Observation Spacementioning

confidence: 99%

Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles

Aradi¹

2020

Preprint

View full text Add to dashboard Cite

Academic research in the field of autonomous vehicles has reached high popularity in recent years related to several topics as sensor technologies, V2X communications, safety, security, decision making, control, and even legal and standardization rules. Besides classic control design approaches, Artificial Intelligence and Machine Learning methods are present in almost all of these fields. Another part of research focuses on different layers of Motion Planning, such as strategic decisions, trajectory planning, and control. A wide range of techniques in Machine Learning itself have been developed, and this article describes one of these fields, Deep Reinforcement Learning (DRL).The paper provides insight into the hierarchical motion planning problem and describes the basics of DRL. The main elements of designing such a system are the modeling of the environment, the modeling abstractions, the description of the state and the perception models, the appropriate rewarding, and the realization of the underlying neural network. The paper describes vehicle models, simulation possibilities and computational requirements. Strategic decisions on different layers and the observation models, e.g., continuous and discrete state representations, grid-based, and camera-based solutions are presented. The paper surveys the state-of-art solutions systematized by the different tasks and levels of autonomous driving, such as car-following, lane-keeping, trajectory following, merging, or driving in dense traffic. Finally, open questions and future challenges are discussed.

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Frame Difference Generative Adversarial Networks: Clearer Contour Video Generating

Qiu

Vargas

Sakurai

2019

2019 Seventh International Symposium on Computing and Networking Workshops (CANDARW)

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Self Training Autonomous Driving Agent

Cited by 5 publications

References 12 publications

Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles

Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles

Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles

Frame Difference Generative Adversarial Networks: Clearer Contour Video Generating

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