Special Issue "Artificial Intelligence and Control Technology for Unmanned Transport Systems"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 March 2022) | Viewed by 5945

Special Issue Editors

Prof. Dr. Myo-Taeg Lim
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Guest Editor
School of Electrical Engineering, Korea University, 145 Anam-ro, Anam-dong, Seongbuk-gu, Seoul, Korea
Interests: intelligent control based on machine learning; vision-based control for autonomous vehicles; intelligent vehicle systems; optimal and robust control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tae-Koo Kang
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Guest Editor
Department of Human Intelligence and Robot Engineering, Sangmyeong University, 20 Hongjimun 2-gil, Hongji-dong, Jongno-gu, Seoul
Interests: driving environment recognition based on machine learning; vision- based control for autonomous vehicles; intelligent vehicle systems; emotion recognition of driver in autonomous vehicles
Prof. Dr. Dong-Sung Pae
E-Mail
Guest Editor
Department of Software, Sangmyeong University, 20 Hongjimun 2-gil, Hongji-dong, Jongno-gu, Seoul
Interests: computer vision; 3D feature extractor based on deep learning; video stabilization for self-driving; artificial intelligence; application to intelligence vehicle systems

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to bring together academics and industrial practitioners to exchange and discuss the latest innovations and applications of artificial intelligence (AI) in the domain of unmanned transport systems. In the past few decades, automated and intelligent transport systems have emerged, opening new research fields that are still evolving because of new challenges and technological advances in the area.

Topics

The scope of this Special Issue is the application of artificial intelligence techniques and algorithms to design and solve the existing problems of unmanned transport systems. These techniques include the following:

  1. Disturbance estimation and robust control for smart systems
  2. Fault diagnosis and failure control
  3. Intelligent object detection and data fusion
  4. Intelligent collision prediction and path planning
  5. Advances in control theories and applications for the smart platform
  6. Improving understanding of traffic, rule, and risk to control the platform in the environment
  7. Driver status recognition (emotion, health status, drowsiness, etc.)
  8. Deep learning and reinforcement learning technology for smart systems

Prof. Dr. Myo-Taeg Lim
Prof. Dr. Tae-Koo Kang
Prof. Dr. Dong-Sung Pae
Guest Editors

Manuscript Submission Information

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Keywords

  • Disturbance estimation and robust control
  • Sensor/actuator fault diagnosis and failure control
  • Object detection and sensor fusion
  • Collision detection and path planning
  • Unmanned platform
  • Environmental recognition
  • Driver state recognition

Published Papers (4 papers)

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Research

Article
A Decision System for Routing Problems and Rescheduling Issues Using Unmanned Aerial Vehicles
Appl. Sci. 2022, 12(12), 6140; https://doi.org/10.3390/app12126140 - 16 Jun 2022
Cited by 2 | Viewed by 673
Abstract
In recent years, consumers have come to expect faster and better delivery services. Logistics companies, therefore, must implement innovative technologies or services in their logistics processes. It is critical to adopt unmanned aerial vehicles (UAV) in last mile delivery and urban logistics. The [...] Read more.
In recent years, consumers have come to expect faster and better delivery services. Logistics companies, therefore, must implement innovative technologies or services in their logistics processes. It is critical to adopt unmanned aerial vehicles (UAV) in last mile delivery and urban logistics. The service provider applies the characteristics of UAVs to complete more requests, benefiting more revenue. However, it may not be a satisfactory solution, because the customers will be dissatisfied if the actual delivery time does not align with their expectations. This study constructs a revenue maximization model subject to time windows and customer satisfaction. Instead of addressing the traveling salesmen problem, this model takes new customer requests during the delivery process into account. We solved the problem using a genetic algorithm. The results show: (1) the model found an approximate and effective solution in the real-time delivery environment; (2) customer satisfaction is inversely proportional to the total delivery distance; (3) regarding the result of the sensitivity analysis of this study, investment in UAV has no influence on total profit and customer satisfaction. Moreover, the customer is a key factor in the logistics decision-making platform, not the provider’s investment in UAVs. Full article
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Article
Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
Appl. Sci. 2021, 11(21), 10420; https://doi.org/10.3390/app112110420 - 05 Nov 2021
Viewed by 1013
Abstract
Despite advances in autonomous driving technology, traffic accidents remain a problem to be solved in the transportation system. More than half of traffic accidents are due to unsafe driving. In addition, aggressive driving behavior can lead to traffic jams. To reduce this, we [...] Read more.
Despite advances in autonomous driving technology, traffic accidents remain a problem to be solved in the transportation system. More than half of traffic accidents are due to unsafe driving. In addition, aggressive driving behavior can lead to traffic jams. To reduce this, we propose a 4-layer CNN-2 stack LSTM-based driving behavior classification and V2X sharing system that uses time-series data as an input to reflect temporal changes. The proposed system classifies driving behavior into defensive, normal, and aggressive driving using only the 3-axis acceleration of the driving vehicle and shares it with the surroundings. We collect a training dataset by composing a road that reflects various environmental factors using a driving simulator that mimics a real vehicle and IPG CarMaker, an autonomous driving simulation. Additionally, driving behavior datasets are collected by driving real-world DGIST campus to augment training data. The proposed network has the best performance compared to the state-of-the-art CNN, LSTM, and CNN-LSTM. Finally, our system shares the driving behavior classified by 4-layer CNN-2 stacked LSTM with surrounding vehicles through V2X communication. The proposed system has been validated in ACC simulations and real environments. For real world testing, we configure NVIDIA Jetson TX2, IMU, GPS, and V2X devices as one module. We performed the experiments of the driving behavior classification and V2X transmission and reception in a real world by using the prototype module. As a result of the experiment, the driving behavior classification performance was confirmed to be ~98% or more in the simulation test and 97% or more in the real-world test. In addition, the V2X communication delay through the prototype was confirmed to be an average of 4.8 ms. The proposed system can contribute to improving the safety of the transportation system by sharing the driving behaviors of each vehicle. Full article
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Article
Adversarial Optimization-Based Knowledge Transfer of Layer-Wise Dense Flow for Image Classification
Appl. Sci. 2021, 11(8), 3720; https://doi.org/10.3390/app11083720 - 20 Apr 2021
Cited by 1 | Viewed by 1150
Abstract
A deep-learning technology for knowledge transfer is necessary to advance and optimize efficient knowledge distillation. Here, we aim to develop a new adversarial optimization-based knowledge transfer method involved with a layer-wise dense flow that is distilled from a pre-trained deep neural network (DNN). [...] Read more.
A deep-learning technology for knowledge transfer is necessary to advance and optimize efficient knowledge distillation. Here, we aim to develop a new adversarial optimization-based knowledge transfer method involved with a layer-wise dense flow that is distilled from a pre-trained deep neural network (DNN). Knowledge distillation transferred to another target DNN based on adversarial loss functions has multiple flow-based knowledge items that are densely extracted by overlapping them from a pre-trained DNN to enhance the existing knowledge. We propose a semi-supervised learning-based knowledge transfer with multiple items of dense flow-based knowledge extracted from the pre-trained DNN. The proposed loss function would comprise a supervised cross-entropy loss for a typical classification, an adversarial training loss for the target DNN and discriminators, and Euclidean distance-based loss in terms of dense flow. For both pre-trained and target DNNs considered in this study, we adopt a residual network (ResNet) architecture. We propose methods of (1) the adversarial-based knowledge optimization, (2) the extended and flow-based knowledge transfer scheme, and (3) the combined layer-wise dense flow in an adversarial network. The results show that it provides higher accuracy performance in the improved target ResNet compared to the prior knowledge transfer methods. Full article
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Article
Path Planning Based on Obstacle-Dependent Gaussian Model Predictive Control for Autonomous Driving
Appl. Sci. 2021, 11(8), 3703; https://doi.org/10.3390/app11083703 - 20 Apr 2021
Cited by 9 | Viewed by 2169
Abstract
Path planning research plays a vital role in terms of safety and comfort in autonomous driving systems. This paper focuses on safe driving and comfort riding through path planning in autonomous driving applications and proposes autonomous driving path planning through an optimal controller [...] Read more.
Path planning research plays a vital role in terms of safety and comfort in autonomous driving systems. This paper focuses on safe driving and comfort riding through path planning in autonomous driving applications and proposes autonomous driving path planning through an optimal controller integrating obstacle-dependent Gaussian (ODG) and model prediction control (MPC). The ODG algorithm integrates the information from the sensors and calculates the risk factors in the driving environment. The MPC function finds vehicle control signals close to the objective function under limited conditions, such as the structural shape of the vehicle and road driving conditions. The proposed method provides safe control and minimizes vehicle shaking due to the tendency to respond to avoid obstacles quickly. We conducted an experiment using mobile robots, similar to an actual vehicle, to verify the proposed algorithm performance. The experimental results show that the average safety metric is 72.34%, a higher ISO-2631 comport score than others, while the average processing time is approximately 14.2 ms/frame. Full article
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