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Autonomous driving: cognitive construction and situation understanding
2019
Science China Information Sciences
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The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring ...
It is necessary to explore a basic computing framework that conforms to human driver's attention, reasoning, learning, and decision-making mechanism with regard to traffic scenarios and build an autonomous ...
This is a significant step toward a transition from "autonomous driving" to "self-driving". ...
doi:10.1007/s11432-018-9850-9
fatcat:qys3uucz3zgznfou6vgfjerwlq
Artificial Intelligence (AI) Framework for Multi-Modal Learning and Decision Making towards Autonomous and Electric Vehicles
2021
E3S Web of Conferences
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Apart from this, there is reinforcement learning and transfer learning to speed up the process of gaining real time business intelligence. ...
A methodology is proposed with multiple deep learning methods. For instance, deep learning is used for localization of vehicle, path planning at high level and path planning for low level. ...
It also focused on safety deep learning to realize autonomous driving. It establishes feasibility of using deep learning for AI based self-driving cars. ...
doi:10.1051/e3sconf/202130901167
fatcat:3igmmoq7abgijiro4iwko263cq
Self-Awareness Safety of Deep Reinforcement Learning in Road Traffic Junction Driving
[article]
2022
arXiv
pre-print
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Deep reinforcement learning (DRL) has been applied to autonomous driving to provide solutions for obstacle avoidance. ...
Autonomous driving has been at the forefront of public interest, and a pivotal debate to widespread concerns is safety in the transportation system. ...
paid heed to the self-attention module that was first intro-
Furthermore, deep reinforcement learning (DRL) models have duced by [21] to resolve ...
arXiv:2201.08116v1
fatcat:wqvofwstfnb6nk4tmmw5ciqicu
Autonomous Industrial Management via Reinforcement Learning: Self-Learning Agents for Decision-Making – A Review
[article]
2019
arXiv
pre-print
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In this paper we indicate the distinctions between automated and autonomous system as well as review the current literature and identify the core challenges for creating learning mechanisms of autonomous ...
We then introduce self-play scenarios and how they can be used to teach self-learning agents through a supportive environment which focuses on how the agents can adapt to different real-world environments ...
more recently Deep Reinforcement Learning [80] . ...
arXiv:1910.08942v1
fatcat:wbpy3iijhbfwxeatiy4ztt5f2m
Towards Safe, Explainable, and Regulated Autonomous Driving
[article]
2022
arXiv
pre-print
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(AI), especially in the applications of deep learning and reinforcement learning. ...
However, as demonstrated by recent traffic accidents, autonomous driving technology is not mature for safe deployment. ...
ACKNOWLEDGMENTS We acknowledge support from the Alberta Machine Intelligence Institute (Amii), from the Computing Science Department of the University of Alberta, and the Natural Sciences and Engineering ...
arXiv:2111.10518v3
fatcat:topadg7bp5enhflk7yqm6j27ga
A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles
[article]
2021
arXiv
pre-print
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In this survey, we systematically summarize the current literature on studies that apply reinforcement learning (RL) to the motion planning and control of autonomous vehicles. ...
Finally, the remaining challenges applying deep RL algorithms on autonomous driving are summarized, and future research directions are also presented to tackle these challenges. ...
An autonomous driving model using combined deep imitation learning and model-based reinforcement learning [51] . unstable environments, an advantage actor-critic algorithm was implemented in [39] with ...
arXiv:2105.14218v2
fatcat:27glt4i4lfhg3j4ozjrlsq6i3e
Decision-Making for the Autonomous Navigation of Maritime Autonomous Surface Ships Based on Scene Division and Deep Reinforcement Learning
2019
Sensors
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To achieve intelligent obstacle avoidance of MASSs in a port, an autonomous navigation decision-making model based on hierarchical deep reinforcement learning is proposed. ...
Finally, two sets of verification experiments of the deep reinforcement learning (DRL) and improved DRL algorithms were designed with Rizhao port as a study case. ...
Acknowledgments: Our deepest gratitude goes to the anonymous reviewers and guest editor for their careful work and thoughtful suggestions that have helped improve this paper substantially. ...
doi:10.3390/s19184055
fatcat:gwsbpymxwvdfhiaatjukzjzobm
Deep Reinforcement Learning for Autonomous Driving: A Survey
[article]
2021
arXiv
pre-print
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This review summarises deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks where (D)RL methods have been employed, while addressing key computational challenges ...
in real world deployment of autonomous driving agents. ...
Index Terms-Deep reinforcement learning, Autonomous driving, Imitation learning, Inverse reinforcement learning, Controller learning, Trajectory optimisation, Motion planning, Safe reinforcement learning ...
arXiv:2002.00444v2
fatcat:axj3ohhjwzdrxp6dgpfqvctv2i
A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods
2020
Applied Sciences
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This paper presents a review of recent research on theories and applications of deep learning for self-driving cars. ...
This survey provides a detailed explanation of the developments of self-driving cars and summarizes the applications of deep learning methods in the field of self-driving cars. ...
[68] proposed an accurate and fast deep CNN, which combined self-attention and channel attention in lane marking detection. Kim, et al. ...
doi:10.3390/app10082749
fatcat:iohm7uqj2vbojmnao6kyhzeliu
Explainable artificial intelligence for autonomous driving: An overview and guide for future research directions
[article]
2022
arXiv
pre-print
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However, intelligent decision-making in autonomous cars is not generally understandable by humans in the current state of the art, and such deficiency hinders this technology from being socially acceptable ...
Autonomous driving has achieved a significant milestone in research and development over the last decade. ...
ACKNOWLEDGMENT We acknowledge support from the Alberta Machine Intelligence Institute (Amii), from the Computing Science Department of the University of Alberta, and the Natural Sciences and Engineering ...
arXiv:2112.11561v2
fatcat:zluqlvmtznh25eihtouubib3ba
Explainability of deep vision-based autonomous driving systems: Review and challenges
[article]
2022
arXiv
pre-print
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Gathering contributions from several research fields, namely computer vision, deep learning, autonomous driving, explainable AI (X-AI), this survey tackles several points. ...
Third, approaches from the literature that aim at building more interpretable self-driving systems by design are presented and discussed in detail. ...
multi-agent traffic, and Kiran et al (2020) review self-driving models based on deep reinforcement learning. ...
arXiv:2101.05307v2
fatcat:c4y7wkesfrczpiw3v6eywdh5m4
Guest Editorial Introduction to the Special Issue on Deep Learning Models for Safe and Secure Intelligent Transportation Systems
2021
IEEE transactions on intelligent transportation systems (Print)
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He carries a combined 40 years of experience in academia, industry, and government. ...
He served in industry for 18 years at Intel, Bellcore, and Bell Labs, and ten years in academia at Penn State and Northwestern University. ...
In the article entitled "Toward safe and smart mobility: Energy-aware deep learning for driving behavior analysis and prediction of connected vehicles," Xing et al. propose an energy-aware driving pattern ...
doi:10.1109/tits.2021.3090721
fatcat:c2o2vno6bjbnxdn6y4zm7ztmvq
Applying Deep Convolutional Neural Network (DCNN) Algorithm in the Cloud Autonomous Vehicles Traffic Model
2022
˜The œinternational Arab journal of information technology
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Supervised, Unsupervised, and even reinforcement learning are also being used in the process of creating cloud autonomous vehicles with the aim of error-free ones. ...
Cloud autonomous vehicles rely on a whole range of machine learning and data mining techniques to process all the sensor data. ...
This paper focuses mainly to extract the merits of CNN by specialty of self-learning the filters in high numbers that result in attractive outcomes towards smart and speedy assessment of decision making ...
doi:10.34028/iajit/19/2/5
fatcat:rbms5z4z2jev3pizlb2ztzbpze
Autonomous Vehicles and Intelligent Automation: Applications, Challenges, and Opportunities
2022
Mobile Information Systems
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The advancements in autonomous vehicles technology using machine learning, deep learning, reinforcement learning, statistical techniques, and IoT are presented with comparative analysis. ...
Intelligent Automation (IA) in automobiles combines robotic process automation and artificial intelligence, allowing digital transformation in autonomous vehicles. ...
Acknowledgments is research was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R195), Princess Nourah bint Abdulrahman University, Riyadh, Saudi ...
doi:10.1155/2022/7632892
fatcat:7ffu7l77pngirijr2blvtefhym
A Deep Coordination Graph Convolution Reinforcement Learning for Multi-Intelligent Vehicle Driving Policy
2022
Wireless Communications and Mobile Computing
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A large number of deep reinforcement learning (RL) technologies are continuously applied to the behavior planning module of single-vehicle autonomous driving in early. ...
Driving samples are used as training data, and the model guided by reward shaping is combined with the model of the free graph convolution RL method, which enables our proposed method to achieve high gradualness ...
Acknowledgments This work was supported in part by the National Natural Science Foundation of China under Grant no. U1808206. ...
doi:10.1155/2022/9665421
fatcat:iwmidwpyavglfjig3hwwtsrjsi
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