The Development and Prospects of Autonomous Driving Technology

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 16086

Special Issue Editors


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Guest Editor
Instituto Universitario de Investigación del Automóvil (INSIA), Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: autonomous vehicles; intelligent transport systems; artificial intelligence; environment perception; vehicle localization; mapping optimization

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Guest Editor
Instituto Universitario de Investigación del Automóvil (INSIA), Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: intelligent transport systems; advanced driver assistance systems; vehicle positioning; inertial sensors; digital maps; vehicle dynamics; driver monitoring; perception; autonomous vehicles; cooperative services; connected and autonomous driving
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Instituto Universitario de Investigación del Automóvil (INSIA), Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: connected and autonomous driving; intelligent transport systems; electromobility; cooperative services; vehicular communications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous driving is a topic of great interest for developers, researchers, and even automotive integrators and manufacturers. Recently, it has drastically evolved not only due to technological advancement, but also due to the development of increasingly powerful and refined algorithms. However, autonomous driving is still a major challenge to become a daily reality. The great variety of situations, the complexity of adapting to each traffic regulation or even the definition of driver behavior strategies, lead us to continue looking for solutions for the improvement of the safety and efficiency of road transport.

The challenges faced by autonomous ground navigation are very diverse. The SAE classification regarding the degree of automation is widely known, where the higher this degree, the greater the number of scenarios that the vehicle must solve. Regardless of the degree of automation, the developments applied for autonomous driving could be grouped into three broad categories: perception, decision-making and control. All of these are supported by the data provided by the vehicle's sensors and communications, which also allow cooperative driving.

In this sense, when talking about the perception of the vehicle, it refers to everything that involves defining a vehicle's environment model and its positioning. Likewise, decision-making encompasses all those developments in behavior planning, movement prediction, or even dependence on the infrastructure to define a driving strategy.

Finally, the rapid evolution of these systems has led to performing field operational tests or also implementations in special applications. In both cases, the problems encountered and the lessons learned can provide useful information for future developments.

In summary, this Special Issue aims to bring together, firstly, a state-of-the-art survey of the major challenges in autonomous driving, followed by a detailed study on each research line, including, but not limited to the following topics:

  • State-of-the-art review of main challenges in autonomous driving
  • Environmental perception
  • Localization and mapping accuracy optimization
  • Behavioral planning
  • Motion prediction and planning
  • Infrastructure-oriented decision-making algorithms for autonomous actions
  • Autonomous vehicles and infrastructure communications
  • Human-machine interaction in autonomous vehicles
  • Field operational tests of autonomous vehicles
  • Special applications of vehicle automation

Dr. Miguel Clavijo
Dr. Felipe Jiménez
Prof. Dr. Jose Eugenio Naranjo
Guest Editors

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Keywords

  • Autonomous vehicles
  • Environment perception
  • SLAM
  • Behavioral planning
  • Path and motion planning
  • Decision-making algorithms
  • V2X communications
  • Human driver modeling
  • Deep Learning for autonomous driving

Published Papers (7 papers)

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Editorial

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2 pages, 187 KiB  
Editorial
The Development and Prospects of Autonomous Driving Technology
by Miguel Clavijo, Felipe Jiménez and Jose Eugenio Naranjo
Appl. Sci. 2023, 13(9), 5377; https://doi.org/10.3390/app13095377 - 25 Apr 2023
Viewed by 1845
Abstract
Autonomous driving is a topic of great interest for developers, researchers, and even automotive integrators and manufacturers [...] Full article
(This article belongs to the Special Issue The Development and Prospects of Autonomous Driving Technology)

Research

Jump to: Editorial

24 pages, 1249 KiB  
Article
Game Theory-Based Interactive Control for Human–Machine Cooperative Driving
by Yangyang Zhou, Chao Huang and Peng Hang
Appl. Sci. 2024, 14(6), 2441; https://doi.org/10.3390/app14062441 - 14 Mar 2024
Viewed by 501
Abstract
To address the safety and efficient driving issues of human–machine shared control vehicles (HSCVs) in future complex traffic environments, this paper proposes a game theory-based interactive control method between HSCVs and surrounding autonomous vehicles (SVs) and involves considering different driving behaviors. In HSCV, [...] Read more.
To address the safety and efficient driving issues of human–machine shared control vehicles (HSCVs) in future complex traffic environments, this paper proposes a game theory-based interactive control method between HSCVs and surrounding autonomous vehicles (SVs) and involves considering different driving behaviors. In HSCV, a comprehensive driver model integrating steering control and speed control is designed based on the brain emotional learning circuit model (BELCM), and the control authority between the driver and the automation system is dynamically allocated through the evaluation of the driving safety field. Factors such as driving safety and travel efficiency that reflect personalized driving style are considered for modeling the uncertain behavior of SVs. In the interaction between HSCVs and SVs, a method based on game theory and distributed model predictive control (DMPC) that considers the uncertainty of SVs’ driving behavior is established and is finally integrated into a multi-objective constraint problem. The driver control input in an HSCV will also be introduced into the solution process. To demonstrate the feasibility of the proposed method, two test scenarios considering the driving characteristics of different SVs are established. The test results show that automation control systems can promptly stop the human driver’s dangerous operations in an HSCV and safely interact with multiple AVs with different driving characteristics. Full article
(This article belongs to the Special Issue The Development and Prospects of Autonomous Driving Technology)
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13 pages, 3845 KiB  
Article
A Novel Method to Generate Auto-Labeled Datasets for 3D Vehicle Identification Using a New Contrast Model
by Guillermo S. Gutierrez-Cabello, Edgar Talavera, Guillermo Iglesias, Miguel Clavijo and Felipe Jiménez
Appl. Sci. 2023, 13(7), 4334; https://doi.org/10.3390/app13074334 - 29 Mar 2023
Cited by 1 | Viewed by 1585
Abstract
Auto-labeling is one of the main challenges in 3D vehicle detection. Auto-labeled datasets can be used to identify objects in LiDAR data, which is a challenging task due to the large size of the dataset. In this work, we propose a novel methodology [...] Read more.
Auto-labeling is one of the main challenges in 3D vehicle detection. Auto-labeled datasets can be used to identify objects in LiDAR data, which is a challenging task due to the large size of the dataset. In this work, we propose a novel methodology to generate new 3D based auto-labeling datasets with a different point of view setup than the one used in most recognized datasets (KITTI, WAYMO, etc.). The performance of the methodology has been further demonstrated with the development of our own dataset with the auto-generated labels and tested under boundary conditions on a bridge in a fixed position. The proposed methodology is based on the YOLO model trained with the KITTI dataset. From a camera-LiDAR sensor fusion, it is intended to auto-label new datasets while maintaining the consistency of the ground truth. The performance of the model, with respect to the manually labeled KITTI images, achieves an F-Score of 0.957, 0.927 and 0.740 in the easy, moderate and hard images of the dataset. The main contribution of this work is a novel methodology to auto-label autonomous driving datasets using YOLO as the main labeling system. The proposed methodology is tested under boundary conditions and the results show that this approximation can be easily adapted to a wide variety of problems when labeled datasets are not available. Full article
(This article belongs to the Special Issue The Development and Prospects of Autonomous Driving Technology)
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15 pages, 2454 KiB  
Article
Computational Intelligence with Wild Horse Optimization Based Object Recognition and Classification Model for Autonomous Driving Systems
by Eatedal Alabdulkreem, Jaber S. Alzahrani, Nadhem Nemri, Olayan Alharbi, Abdullah Mohamed, Radwa Marzouk and Anwer Mustafa Hilal
Appl. Sci. 2022, 12(12), 6249; https://doi.org/10.3390/app12126249 - 20 Jun 2022
Cited by 3 | Viewed by 1501
Abstract
Presently, autonomous systems have gained considerable attention in several fields such as transportation, healthcare, autonomous driving, logistics, etc. It is highly needed to ensure the safe operations of the autonomous system before launching it to the general public. Since the design of a [...] Read more.
Presently, autonomous systems have gained considerable attention in several fields such as transportation, healthcare, autonomous driving, logistics, etc. It is highly needed to ensure the safe operations of the autonomous system before launching it to the general public. Since the design of a completely autonomous system is a challenging process, perception and decision-making act as vital parts. The effective detection of objects on the road under varying scenarios can considerably enhance the safety of autonomous driving. The recently developed computational intelligence (CI) and deep learning models help to effectively design the object detection algorithms for environment perception depending upon the camera system that exists in the autonomous driving systems. With this motivation, this study designed a novel computational intelligence with a wild horse optimization-based object recognition and classification (CIWHO-ORC) model for autonomous driving systems. The proposed CIWHO-ORC technique intends to effectively identify the presence of multiple static and dynamic objects such as vehicles, pedestrians, signboards, etc. Additionally, the CIWHO-ORC technique involves the design of a krill herd (KH) algorithm with a multi-scale Faster RCNN model for the detection of objects. In addition, a wild horse optimizer (WHO) with an online sequential ridge regression (OSRR) model was applied for the classification of recognized objects. The experimental analysis of the CIWHO-ORC technique is validated using benchmark datasets, and the obtained results demonstrate the promising outcome of the CIWHO-ORC technique in terms of several measures. Full article
(This article belongs to the Special Issue The Development and Prospects of Autonomous Driving Technology)
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19 pages, 4197 KiB  
Article
Stochastic Model-Predictive Control with Uncertainty Estimation for Autonomous Driving at Uncontrolled Intersections
by Yonghwan Jeong
Appl. Sci. 2021, 11(20), 9397; https://doi.org/10.3390/app11209397 - 10 Oct 2021
Cited by 6 | Viewed by 2352
Abstract
This paper presents an uncontrolled intersection-passing algorithm with an integrated approach of stochastic model-predictive control and prediction uncertainty estimation for autonomous vehicles. The proposed algorithm is designed to utilize information from sensors mounted on the autonomous vehicle and high-definition intersection maps. The proposed [...] Read more.
This paper presents an uncontrolled intersection-passing algorithm with an integrated approach of stochastic model-predictive control and prediction uncertainty estimation for autonomous vehicles. The proposed algorithm is designed to utilize information from sensors mounted on the autonomous vehicle and high-definition intersection maps. The proposed algorithm is composed of two modules, namely target state prediction and a motion planner. The target state prediction module has predicted the future behavior of intersection-approaching vehicles based on human driving data. The recursive covariance estimator has been utilized to estimate the prediction uncertainty for each approaching vehicle. The desired driving mode has been determined based on the uncontrolled intersection theory. The estimated prediction uncertainty has been used to define the probability distribution of the stochastic model-predictive controller to cope with time-varying uncertainty characteristics of the perception algorithm. The constrained stochastic model-predictive controller based on safety indexes has determined the desired longitudinal acceleration. The proposed robust intersection-passing algorithm has been evaluated via computer simulation based on Monte Carlo simulation with a sensor model. The simulation results showed that the proposed algorithm guarantees the minimum safety constraints and improves the ride comfort at uncontrolled intersections by estimating the uncertainty of sensors and prediction. Full article
(This article belongs to the Special Issue The Development and Prospects of Autonomous Driving Technology)
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10 pages, 625 KiB  
Article
Take-Over Time: A Cross-Cultural Study of Take-Over Responses in Highly Automated Driving
by Gregor Strle, Yilun Xing, Erika E. Miller, Linda Ng Boyle and Jaka Sodnik
Appl. Sci. 2021, 11(17), 7959; https://doi.org/10.3390/app11177959 - 28 Aug 2021
Cited by 1 | Viewed by 1848
Abstract
The article presents a cross-cultural study of take-over performance in highly automated driving. As take-over performance is an important measure of safe driving, potential cultural differences could have important implications for the future development of automated vehicles. The study was conducted in two [...] Read more.
The article presents a cross-cultural study of take-over performance in highly automated driving. As take-over performance is an important measure of safe driving, potential cultural differences could have important implications for the future development of automated vehicles. The study was conducted in two culturally different locations, Seattle, WA (n = 20) and Ljubljana, Slovenia (n = 18), using a driving simulator. While driving, participants voluntarily engaged in secondary tasks. The take-over request (TOR) was triggered at a specific time during the drive, and take-over time and type of response (none, brake, steer) were measured for each participant. Results show significant differences in take-over performance between the two locations. In Seattle 30% of participants in Seattle did not respond to TOR; the remaining 70% responded by braking only, compared to Slovenian participants who all responded by either braking or steering. Participants from Seattle responded significantly more slowly to TOR (M = +1285 ms) than Slovenian participants. Secondary task engagement at TOR also had an effect, with distracted US participants’ response taking significantly longer (M = +1596 ms) than Slovenian participants. Reported differences in take-over performance may indicate cultural differences in driving behavior and trust in automated driving. Full article
(This article belongs to the Special Issue The Development and Prospects of Autonomous Driving Technology)
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19 pages, 3816 KiB  
Article
Design and Implementation of an Ontology for Semantic Labeling and Testing: Automotive Global Ontology (AGO)
by Itziar Urbieta, Marcos Nieto, Mikel García and Oihana Otaegui
Appl. Sci. 2021, 11(17), 7782; https://doi.org/10.3390/app11177782 - 24 Aug 2021
Cited by 16 | Viewed by 4358
Abstract
Modern Artificial Intelligence (AI) methods can produce a large quantity of accurate and richly described data, in domains such as surveillance or automation. As a result, the need to organize data at a large scale in a semantic structure has arisen for long-term [...] Read more.
Modern Artificial Intelligence (AI) methods can produce a large quantity of accurate and richly described data, in domains such as surveillance or automation. As a result, the need to organize data at a large scale in a semantic structure has arisen for long-term data maintenance and consumption. Ontologies and graph databases have gained popularity as mechanisms to satisfy this need. Ontologies provide the means to formally structure descriptive and semantic relations of a domain. Graph databases allow efficient and well-adapted storage, manipulation, and consumption of these linked data resources. However, at present, there is no a universally defined strategy for building AI-oriented ontologies for the automotive sector. One of the key challenges is the lack of a global standardized vocabulary. Most private initiatives and large open datasets for Advanced Driver Assistance Systems (ADASs) and Autonomous Driving (AD) development include their own definitions of terms, with incompatible taxonomies and structures, thus resulting in a well-known lack of interoperability. This paper presents the Automotive Global Ontology (AGO) as a Knowledge Organization System (KOS) using a graph database (Neo4j). Two different use cases for the AGO domain ontology are presented to showcase its capabilities in terms of semantic labeling and scenario-based testing. The ontology and related material have been made public for their subsequent use by the industry and academic communities. Full article
(This article belongs to the Special Issue The Development and Prospects of Autonomous Driving Technology)
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