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Electronics
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Machine Learning Techniques in Autonomous Driving
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Machine Learning Techniques in Autonomous Driving

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: closed (15 July 2024) | Viewed by 7378

Special Issue Editor

Dr. Ali Riza Ekti

E-Mail Website
Guest Editor
Oak Ridge National Laboratory, 1 Bethel Valley Rd., Oak Ridge, TN 37831, USA
Interests: machine learning applications for wireless communications; autonomous aerial vehicles; wireless channel propagation modeling; machine learning applications for smart grid; signal classification

Special Issue Information

Dear Colleagues,

The fields of advanced mobility, manufacturing and smart sensors are just a few examples of where artificial intelligence (AI) is considered a foundational technology. In addition to aiding in the resolution of today’s major social concerns, its capacity to extract meaningful information from raw data allows for the creation of novel modes of interaction between humans, vehicles and infrastructure, which also eventually leads to the autonomous aerial and ground driving. However, these opportunities mean new challenges in terms of interoperability, connectivity performance for the real-time transmission of sensor data, wireless communication delays, coordination between infrastructure sensors and on-board vehicle sensors and computational complexity. In order to cope with the existing challenges, it is envisioned that next-generation autonomous vehicles will be required a multidisciplinary effort. This includes advanced machine learning, signal processing algorithms, and hardware–software co-designed for perception of the surrounding environment, probabilistic modeling and estimation, vehicle behavior prediction, edge computing, as well as real time constraints to ensure high-level autonomy, security, robustness, and privacy, and to eliminate catastrophic events.

Technical advancements in this area of study are thought to hasten the convergence of current and developing autonomous vehicle systems, paving the way for machine-learning-based xGeneration autonomous vehicle systems. The scope of the Special Issue includes, and is not limited to, the following topics:

  • Deep learning based wireless RF spectrum management and allocation for autonomous driving;
  • Deep learning for perception and environment awareness for V2X networks;
  • Unsupervised and supervised learning for autonomous driving;
  • Real-time prediction and edge computing utilizing machine learning;
  • Investigation of 5G and Beyond systems to eliminate latency for fronthaul and backhaul communications;
  • Deep-learning-assisted coordination between on-board vehicle and infrastructure sensors;
  • Practical consideration on machine learning for autonomous driving metrics;
  • Energy- and cost-efficient deep-learning-based on-board processing such as advanced AI on-board SoC architectures for autonomous driving;
  • Incorporation of autonomous aerial and ground-based vehicles to increase situational awareness utilizing machine learning;
  • Wireless channel propagation modeling: vehicle to infrastructure, vehicle to vehicle and ground vehicle to aerial vehicles;
  • Cybersecurity for autonomous systems;
  • Enhanced transfer learning for autonomous driving.

Dr. Ali Riza Ekti
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • autonomous driving
  • V2X networks
  • deep learning
  • autonomous aerial vehicles
  • autonomous ground vehicles
  • vehicle to vehicle
  • autonomous systems

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Published Papers (4 papers)

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Research

16 pages, 714 KiB  
Article
VN-MADDPG: A Variable-Noise-Based Multi-Agent Reinforcement Learning Algorithm for Autonomous Vehicles at Unsignalized Intersections
by Hao Zhang, Yu Du, Shixin Zhao, Ying Yuan and Qiuqi Gao
Electronics 2024, 13(16), 3180; https://doi.org/10.3390/electronics13163180 - 11 Aug 2024
Cited by 1 | Viewed by 1873
Abstract
The decision-making performance of autonomous vehicles tends to be unstable at unsignalized intersections, making it difficult for them to make optimal decisions. We propose a decision-making model based on the Variable-Noise Multi-Agent Deep Deterministic Policy Gradient (VN-MADDPG) algorithm to address these issues. The [...] Read more.
The decision-making performance of autonomous vehicles tends to be unstable at unsignalized intersections, making it difficult for them to make optimal decisions. We propose a decision-making model based on the Variable-Noise Multi-Agent Deep Deterministic Policy Gradient (VN-MADDPG) algorithm to address these issues. The variable-noise mechanism reduces noise dynamically, enabling the agent to utilize the learned policy more effectively to complete tasks. This significantly improves the stability of the decision-making model in making optimal decisions. The importance sampling module addresses the inconsistency between outdated experience in the replay buffer and current environmental features. This enhances the model’s learning efficiency and improves the robustness of the decision-making model. Experimental results on the CARLA simulation platform show that the success rate of decision making at unsignalized intersections by autonomous vehicles has significantly increased, and the pass time has been reduced. The decision-making model based on the VN-MADDPG algorithm demonstrates stable and excellent decision-making performance. Full article
(This article belongs to the Special Issue Machine Learning Techniques in Autonomous Driving)
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19 pages, 1877 KiB  
Article
Privacy-Preserving Real-Time Action Detection in Intelligent Vehicles Using Federated Learning-Based Temporal Recurrent Network
by Alpaslan Gökcen and Ali Boyacı
Electronics 2024, 13(14), 2820; https://doi.org/10.3390/electronics13142820 - 18 Jul 2024
Viewed by 1156
Abstract
This study introduces a privacy-preserving approach for the real-time action detection in intelligent vehicles using a federated learning (FL)-based temporal recurrent network (TRN). This approach enables edge devices to independently train models, enhancing data privacy and scalability by eliminating central data consolidation. Our [...] Read more.
This study introduces a privacy-preserving approach for the real-time action detection in intelligent vehicles using a federated learning (FL)-based temporal recurrent network (TRN). This approach enables edge devices to independently train models, enhancing data privacy and scalability by eliminating central data consolidation. Our FL-based TRN effectively captures temporal dependencies, anticipating future actions with high precision. Extensive testing on the Honda HDD and TVSeries datasets demonstrated robust performance in centralized and decentralized settings, with competitive mean average precision (mAP) scores. The experimental results highlighted that our FL-based TRN achieved an mAP of 40.0% in decentralized settings, closely matching the 40.1% in centralized configurations. Notably, the model excelled in detecting complex driving maneuvers, with mAPs of 80.7% for intersection passing and 78.1% for right turns. These outcomes affirm the model’s accuracy in action localization and identification. The system showed significant scalability and adaptability, maintaining robust performance across increased client device counts. The integration of a temporal decoder enabled predictions of future actions up to 2 s ahead, enhancing the responsiveness. Our research advances intelligent vehicle technology, promoting safety and efficiency while maintaining strict privacy standards. Full article
(This article belongs to the Special Issue Machine Learning Techniques in Autonomous Driving)
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18 pages, 8110 KiB  
Article
Exploring Deep Learning for Adaptive Energy Detection Threshold Determination: A Multistage Approach
by Oguz Bedir, Ali Riza Ekti and Mehmet Kemal Ozdemir
Electronics 2023, 12(19), 4183; https://doi.org/10.3390/electronics12194183 - 9 Oct 2023
Cited by 2 | Viewed by 2039
Abstract
The concept of spectrum sensing has emerged as a fundamental solution to address the growing demand for accessing the limited resources of wireless communications networks. This paper introduces a straightforward yet efficient approach that incorporates multiple stages that are based on deep learning [...] Read more.
The concept of spectrum sensing has emerged as a fundamental solution to address the growing demand for accessing the limited resources of wireless communications networks. This paper introduces a straightforward yet efficient approach that incorporates multiple stages that are based on deep learning (DL) techniques to mitigate Radio Frequency (RF) impairments and estimate the transmitted signal using the time domain representation of received signal samples. The proposed method involves calculating the energies of the estimated transmitted signal samples and received signal samples and estimating the energy of the noise using these estimates. Subsequently, the received signal energy and the estimated noise energy, adjusted by a correction factor (k), are employed in binary hypothesis testing to determine the occupancy of the wireless channel under investigation. The proposed system demonstrates encouraging outcomes by effectively mitigating RF impairments, such as carrier frequency offset (CFO), phase offset, and additive white Gaussian noise (AWGN), to a considerable degree. As a result, it enables accurate estimation of the transmitted signal from the received signal, with 3.85% false alarm and 3.06% missed detection rates, underscoring the system’s capability to adaptively determine a decision threshold for energy detection. Full article
(This article belongs to the Special Issue Machine Learning Techniques in Autonomous Driving)
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20 pages, 3090 KiB  
Article
MultiNet-GS: Structured Road Perception Model Based on Multi-Task Convolutional Neural Network
by Ang Li, Zhaoyang Zhang, Shijie Sun, Mingtao Feng and Chengzhong Wu
Electronics 2023, 12(19), 3994; https://doi.org/10.3390/electronics12193994 - 22 Sep 2023
Cited by 3 | Viewed by 1626
Abstract
In order to address the issue of environmental perception in autonomous driving on structured roads, we propose MultiNet-GS, a convolutional neural network model based on an encoder–decoder architecture that tackles multiple tasks simultaneously. We use the main structure of the latest object detection [...] Read more.
In order to address the issue of environmental perception in autonomous driving on structured roads, we propose MultiNet-GS, a convolutional neural network model based on an encoder–decoder architecture that tackles multiple tasks simultaneously. We use the main structure of the latest object detection model, the YOLOv8 model, as the encoder structure of our model. We introduce a new dynamic sparse attention mechanism, BiFormer, in the feature extraction part of the model to achieve more flexible computing resource allocation, which can significantly improve the computational efficiency and occupy a small computational overhead. We introduce a lightweight convolution, GSConv, in the feature fusion part of the network, which is used to build the neck part into a new slim-neck structure so as to reduce the computational complexity and inference time of the detector. We also add an additional detector for tiny objects to the conventional three-head detector structure. Finally, we introduce a lane detection method based on guide lines in the lane detection part, which can aggregate the lane feature information into multiple key points, obtain the lane heat map response through conditional convolution, and then describe the lane line through the adaptive decoder, which effectively makes up for the shortcomings of the traditional lane detection method. Our comparative experiments on the BDD100K dataset on the embedded platform NVIDIA Jetson TX2 show that compared with SOTA(YOLOPv2), the mAP@0.5 of the model in traffic object detection reaches 82.1%, which is increased by 2.7%. The accuracy of the model in drivable area detection reaches 93.2%, which is increased by 0.5%. The accuracy of the model in lane detection reaches 85.7%, which is increased by 4.3%. The Params and FLOPs of the model reach 47.5 M and 117.5, which are reduced by 6.6 M and 8.3, respectively. The model achieves 72 FPS, which is increased by 5. Our MultiNet-GS model has the highest detection accuracy among the current mainstream models while maintaining a good detection speed and has certain superiority. Full article
(This article belongs to the Special Issue Machine Learning Techniques in Autonomous Driving)
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