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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 20446

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Special Issue Editor

Dr. Zhijun Chen

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Guest Editor

Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430070, China
Interests: intelligent vehicle; artificial intelligence; traffic safety; vehicle behavior recognition; machine learning; intelligent transportation systems
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Special Issue Information

Dear Colleagues,

Intelligent vehicle (IV) is a comprehensive system that integrates functions such as environment perception, planning, and decision making, and multi-level assisted driving. It concentrates on the technologies of computers, modern sensing, information fusion, communication, artificial intelligence, and automatic control, etc.

The improvement of the intelligence level of IV can enhance traffic safety and efficiency effectively. In recent years, with the development of hardware and software, the technology of Intelligent Connected Vehicle (ICV) has achieved rapid progress. However, there are many critical and difficult issues that remain to be addressed.

This special issue is dedicated to papers focusing on novel methods and technologies for intelligent vehicles. Researchers from both academia and industry are welcomed to submit unpublished research work related to the perception, decision-making and control, and other key technologies of IV. This includes but is not limited to the following：

Intelligent Connected Vehicles;
Artificial Intelligence;
Navigation and Localization;
Environmental Perception;
Cooperative Vehicle Infrastructure Systems;
Data Fusion;
Driver-autonomous Integration Cooperative Driving;
Decision-making and Planning;
Vehicle Dynamics Control.

Dr. Zhijun Chen
Guest Editor

Manuscript Submission Information

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Keywords

intelligent connected vehicles
artificial intelligence
navigation and localization
environmental perceptiomn
cooperative vehicle infrastructure systems
data fusion
driver-autonomous integration cooperative driving
decision-making and planning
vehicle dynamics control

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Published Papers (8 papers)

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Research

20 pages, 5869 KiB

Open AccessArticle

Design of an Intelligent Vehicle Behavior Decision Algorithm Based on DGAIL

by Junfeng Jiang, Yikang Rui, Bin Ran and Peng Luo

Appl. Sci. 2023, 13(9), 5648; https://doi.org/10.3390/app13095648 - 4 May 2023

Cited by 1 | Viewed by 1423

Abstract

With the development of AI, the intelligence level of vehicles is increasing. Structured roads, as common and important traffic scenes, are the most typical application scenarios for realizing autonomous driving. The driving behavior decision-making of intelligent vehicles has always been a controversial and difficult research topic. Currently, the mainstream decision-making methods, which are mainly based on rules, lack adaptability and generalization to the environment. Aimed at the particularity of intelligent vehicle behavior decisions and the complexity of the environment, this thesis proposes an intelligent vehicle driving behavior decision method based on DQN generative adversarial imitation learning (DGAIL) in the structured road traffic environment, in which the DQN algorithm is utilized as a GAIL generator. The results show that the DGAIL method can preserve the design of the reward value function, ensure the effectiveness of training, and achieve safe and efficient driving on structured roads. The experimental results show that, compared with A3C, DQN and GAIL, the model based on DGAIL spends less average training time to achieve a 95% success rate in the straight road scene and merging road scene, respectively. Apparently, this algorithm can effectively accelerate the selection of actions, reduce the randomness of actions during the exploration, and improve the effect of the decision-making model. Full article

(This article belongs to the Special Issue Novel Methods and Technologies for Intelligent Vehicles)

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15 pages, 4177 KiB

Open AccessArticle

Research on Truck Lane Management Strategies for Platooning Speed Optimization and Control on Multi-Lane Highways

by Yikang Rui, Shu Wang, Renfei Wu and Zhe Shen

Appl. Sci. 2023, 13(6), 4072; https://doi.org/10.3390/app13064072 - 22 Mar 2023

Cited by 1 | Viewed by 2063

Abstract

Automated truck platooning has become an increasingly popular research subject, and its applicability to highways is considered one of the earliest possible landing scenarios for automated driving. However, there is a lack of research regarding the combination of truck platooning technology and truck lane management strategy on multilane highways in the environment of a cooperative vehicle–infrastructure system (CVIS). For highway weaving sections under the CVIS environment, this paper proposes a truck platooning optimal speed control model based on multi-objective optimization. Through a combination of model predictive control and the cell transmission model, this approach considers the bottleneck cell traffic flow, overall vehicle travel time, and truck platooning fuel consumption as objectives. By conducting experiments on a mixed traffic flow simulation platform, the multi-lane management strategies and optimal speed control effect were evaluated through different scenarios. This study also determined the appropriate proportion of truck platooning for an exclusive lane and to increase truck lanes, thus providing effective lane management decision support for highway managers. Full article

(This article belongs to the Special Issue Novel Methods and Technologies for Intelligent Vehicles)

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Figure 1
Nanjing–Shanghai highway in bright cyan (left) and marked section map (right). Full article ">Figure 2
Schematic diagram of lane marking mode in weaving section (left) and simulation diagram of weaving section (right). Full article ">Figure 3
Cross-section traffic volume time varying diagram and section speed distribution diagram. Full article ">Figure 4
Delay and conflict rate of truck platooning in the queue phase in Scenario I. Full article ">Figure 5
Delay and conflict rate of truck platooning in the separation phase in Scenario I. Full article ">Figure 6
Separation phase with 4F32T strategy through the weaving section in Scenario II. Full article ">Figure 7
Comparison of different vehicle types under the 4F32T strategy. Full article ">Figure 8
Comparison of different lanes with the 4F32T strategy. Full article ">

22 pages, 5399 KiB

Open AccessArticle

Improved Traffic Sign Detection Algorithm Based on Faster R-CNN

by Xiang Gao, Long Chen, Kuan Wang, Xiaoxia Xiong, Hai Wang and Yicheng Li

Appl. Sci. 2022, 12(18), 8948; https://doi.org/10.3390/app12188948 - 6 Sep 2022

Cited by 14 | Viewed by 2668

Abstract

The traffic sign detection algorithm based on Faster Region-Based Convolutional Neural Network (R-CNN) has been applied to various intelligent-vehicles driving scenarios. However, the model of the current detection algorithm has certain shortcomings, which include the influence of weather and light, the detection of distance traffic signs, and the detection of similar traffic signs. To solve these problems, this paper proposes an improved traffic sign detection method based on Faster R-CNN. First, we propose a fusion method that fuses the feature pyramid into the Faster R-CNN algorithm. This fusion method can extract object features with precision and decrease the influence of weather and light. Second, a deformable convolution (DCN) which can train the algorithm to identify traffic signs with precision and make similar signs more distinguishable, and in particular make it work better with distorted images, is added to the backbone network. Lastly, we apply ROI align to replace the ROI pooling, which can avoid the distant traffic sign detail loss caused by pooling and increase the detection precision of distant traffic signs. The experimental results on both the TT100k dataset and real intelligent vehicle tests demonstrate that the algorithm is superior to the original Faster R-CNN algorithm and four other state-of-the-art methods in traffic sign detection, specifically in small-target traffic sign detection and low-intensity environments such as sunset time and rainy days. Therefore, the proposed method is helpful to improve the traffic sign detection performance in extreme environments (low-light intensity or rainy weather). Full article

(This article belongs to the Special Issue Novel Methods and Technologies for Intelligent Vehicles)

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15 pages, 2510 KiB

Open AccessArticle

SuperFormer: Enhanced Multi-Speaker Speech Separation Network Combining Channel and Spatial Adaptability

by Yanji Jiang, Youli Qiu, Xueli Shen, Chuan Sun and Haitao Liu

Appl. Sci. 2022, 12(15), 7650; https://doi.org/10.3390/app12157650 - 29 Jul 2022

Viewed by 1983

Abstract

Speech separation is a hot topic in multi-speaker speech recognition. The long-term autocorrelation of speech signal sequences is an essential task for speech separation. The keys are effective intra-autocorrelation learning for the speaker’s speech, modelling the local (intra-blocks) and global (intra- and inter- blocks) dependence features of the speech sequence, with the real-time separation of as few parameters as possible. In this paper, the local and global dependence features of speech sequence information are extracted by utilizing different transformer structures. A forward adaptive module of channel and space autocorrelation is proposed to give the separated model good channel adaptability (channel adaptive modeling) and space adaptability (space adaptive modeling). In addition, at the back end of the separation model, a speaker enhancement module is considered to further enhance or suppress the speech of different speakers by taking advantage of the mutual suppression characteristics of each source signal. Experiments show that the scale-invariant signal-to-noise ratio improvement (SI-SNRi) of the proposed separation network on the public corpus WSJ0-2mix achieves better separation performance compared with the baseline models. The proposed method can provide a solution for speech separation and speech recognition in multi-speaker scenarios. Full article

(This article belongs to the Special Issue Novel Methods and Technologies for Intelligent Vehicles)

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Figure 1
Overall technical route of speech separation system. Full article ">Figure 2
Structure design of the SuperFormer Block, including the Intra-SuperFormer and Inter-SuperFormer with different structures. The difference between the two modules lies in the structure of FFN module in transformer. The Intra-Transformer uses a TCN layer to learn the intra -block data correlation of speech, and the Inter-Transformer uses an RNN network to learn the inter -block data correlation of speech. Full article ">Figure 3
The Forward Adaptive Module structure is designed to enhance the adaptability of the model to features. Full article ">Figure 4
Structural design of the TCN module, which is designed for features extraction of intra-block speech. Full article ">Figure 5
The internal structure of the speaker enhancement module, as the post-processing module of the model, uses the temporal correlation characteristics of the same speaker’s speech to improve the separation performance of the model. Full article ">Figure 6
Visualization of speech separation process. Taking the mixing of two speakers’ speech as an example, the speaker’s speech is displayed in red and blue respectively. The number in the figure represents the change of channel dimension, in which the encoder adopts 1 × 256 convolution coding. Through the autocorrelation modeling of SuperFormer, the separated speech feature map is obtained. It can be clearly seen that the speaker enhancement module improves the separation effect. Finally, the decoder applies 256 × 1 transpose convolution to decode and obtain the separated speech waveform. Full article ">Figure 7
Comparison diagram of the predicted speaker speech and real speaker tag. (a) Comparison of speaker 1’s separation results and labels; (b) Comparison of speaker 2’s separation results and labels. The coincidence degree between two-speaker speech separated by the model and the original speech data tag is ideal, which verifies the generalization effect of the model. Full article ">

14 pages, 3920 KiB

Open AccessArticle

A Control Method for the Differential Steering of Tracked Vehicles Driven Independently by a Dual Hydraulic Motor

by Jiangyi Han, Fan Wang and Yuhang Wang

Appl. Sci. 2022, 12(13), 6355; https://doi.org/10.3390/app12136355 - 22 Jun 2022

Cited by 8 | Viewed by 2726

Abstract

It is well known that tracked vehicles can adapt well to all kinds of terrain. However, the safety of tracked vehicles should be considered during steering on sloped terrain. This paper focuses on the differential steering control of tracked vehicles independently driven by a hydraulic motor. Firstly, the dynamic model of hydrostatic drive system was built and the kinematics and dynamics of differential steering driving were analyzed theoretically. Secondly, in order to prevent rollover of the tracked vehicle, the method of vehicle speed constraint was proposed. The constraint conditions of vehicle speed and steering angular velocity were analyzed under different slope conditions. Thirdly, based on the analysis results, differential steering control rules for tracked vehicles were formulated. To verify the effectiveness of the control rules, the models of vehicle driving dynamics and Fuzzy PID control simulation were established in MATLAB/Simulink. Longitudinal steering simulation was carried out on a slope (0°, 30°), and an analysis of the simulation of lateral steering along the contour line was carried out. The simulation results showed that this steering control strategy was able to automatically adjust the target vehicle speed to avoid rollover while the driver was inputting steering signals. Full article

(This article belongs to the Special Issue Novel Methods and Technologies for Intelligent Vehicles)

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29 pages, 3943 KiB

Open AccessArticle

Safety-Oriented System Hardware Architecture Exploration in Compliance with ISO 26262

by Kuen-Long Lu and Yung-Yuan Chen

Appl. Sci. 2022, 12(11), 5456; https://doi.org/10.3390/app12115456 - 27 May 2022

Cited by 6 | Viewed by 4323

Abstract

Safety-critical intelligent automotive systems require stringent dependability while the systems are in operation. Therefore, safety and reliability issues must be addressed in the development of such safety-critical systems. Nevertheless, the incorporation of safety/reliability requirements into the system will raise the design complexity considerably. Furthermore, the international safety standards only provide guidelines and lack concrete design methodology and flow. Therefore, developing an effective safety process to assist system engineers in tackling the complexity of system design and verification, while also satisfying the requirements of international safety standards, has become an important and valuable research topic. In this study, we propose a safety-oriented system hardware architecture exploration framework, which incorporates fault tree-based vulnerability analysis with safety-oriented system hardware architecture exploration to rapidly discover an efficient solution that complies with the ISO-26262 safety requirements and hardware overhead constraint. A failure mode, effect, and diagnostic analysis (FMEDA) report is generated after performing the exploration framework. The proposed framework can facilitate the system engineers in designing, assessing, and enhancing the safety/robustness of a system in a cost-effective manner. Full article

(This article belongs to the Special Issue Novel Methods and Technologies for Intelligent Vehicles)

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18 pages, 2013 KiB

Open AccessArticle

Influencing Factors of the Length of Lane-Changing Buffer Zone for Autonomous Driving Dedicated Lanes

by Fujin Hou, Ying Zhang, Shujian Wang, Zhengxi Shen, Peipei Mao and Xu Qu

Appl. Sci. 2022, 12(10), 4923; https://doi.org/10.3390/app12104923 - 12 May 2022

Cited by 3 | Viewed by 2280

Abstract

With the development of intelligent transportation, dedicated highway lanes for autonomous vehicles (AVs), necessary for ensuring their right of way, have emerged as critical issues in intelligent transportation research, which makes it necessary to set up specialized lane-changing buffer zones in the lane adjacent to the dedicated one. Restricted by the current situation of intelligent transportation systems, based on NGSIM data, this study filters out typical lane-changing description data featuring lane-changing behaviors and constructs a principal component analysis (PCA) model containing factors affecting the longitudinal driving distance during the whole lane-changing procedure. The validity of the model is evaluated with a significance test. Comparing the PCA model to a general linear regression model, suggestions on setting the length of lane-changing buffer zones are put forward. The length of the buffer zone mainly considers speed, acceleration, and the flow in the dedicated lane. In general, a shorter buffer zone length can be achieved by increasing the design speed of the buffer zone, raising the headway of AVs in the dedicated lane, reducing the acceleration rate of lane-changing vehicles, and reducing the time proportion of the lane change preparation stage, which occurs earlier in the procedure. Full article

(This article belongs to the Special Issue Novel Methods and Technologies for Intelligent Vehicles)

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11 pages, 3050 KiB

Open AccessCommunication

On Training Road Surface Classifiers by Data Augmentation

by Addisson Salazar, Alberto Rodríguez, Nancy Vargas and Luis Vergara

Appl. Sci. 2022, 12(7), 3423; https://doi.org/10.3390/app12073423 - 28 Mar 2022

Cited by 6 | Viewed by 1608

Abstract

It is demonstrated that data augmentation is a promising approach to reduce the size of the captured dataset required for training automatic road surface classifiers. The context is on-board systems for autonomous or semi-autonomous driving assistance: automatic power-assisted steering. Evidence is obtained by extensive experiments involving multiple captures from a 10-channel multisensor deployment: three channels from the accelerometer (acceleration in the X, Y, and Z axes); three microphone channels; two speed channels; and the torque and position of the handwheel. These captures were made under different settings: three worm-gear interface configurations; hands on or off the wheel; vehicle speed (constant speed of 10, 15, 20, 30 km/h, or accelerating from 0 to 30 km/h); and road surface (smooth flat asphalt, stripes, or cobblestones). It has been demonstrated in the experiments that data augmentation allows a reduction by an approximate factor of 1.5 in the size of the captured training dataset. Full article

(This article belongs to the Special Issue Novel Methods and Technologies for Intelligent Vehicles)

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