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16 pages, 664 KiB

Open AccessArticle

A Comprehensive Evaluation of Vehicle Intelligent Barrier Avoidance Function under Special Roads Based on G1-CRITIC

by Xuewen Zhang, Qi Zhan, Wei Zhou and Zhichao Liu

Sustainability 2023, 15(15), 12093; https://doi.org/10.3390/su151512093 - 7 Aug 2023

Cited by 1 | Viewed by 816

As one of the core functions of the autonomous driving of vehicles under special roads, the intelligent barrier avoidance function plays an important role in improving traffic efficiency and ensuring driving safety. Scientific, reasonable, and comprehensive evaluation methods can provide the basis for [...] Read more.

As one of the core functions of the autonomous driving of vehicles under special roads, the intelligent barrier avoidance function plays an important role in improving traffic efficiency and ensuring driving safety. Scientific, reasonable, and comprehensive evaluation methods can provide the basis for intelligent vehicles before their use. The comprehensive evaluation index system is constructed for the avoidance ability and avoidance mode of intelligent vehicles against different barriers. The weights of qualitative indicators that are difficult to quantify are determined based on the order relation analysis (G1) method, and the weights of quantitative indicators are determined based on the CRITIC (criteria importance though intercriteria correlation) method. The overall system is comprehensively and quantitatively evaluated through the grey correlation degree method. The correctness of the evaluation method is verified via testing. According to the comprehensive evaluation method studied, the comprehensive evaluation result of the test vehicle is obtained. The intelligent barrier avoidance function of negative barriers is superior to that of positive barriers. The integrated evaluation method can obtain evaluation results of vehicle performance in different test scenarios. Full article

(This article belongs to the Special Issue Intelligent Vehicle-Infrastructure System and Sustainable Transportation)

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20 pages, 4093 KiB

Open AccessArticle

A Bridge Damage Visualization Technique Based on Image Processing Technology and the IFC Standard

by Yuan Tian, Xuefan Zhang, Haonan Chen, Yujie Wang and Hongyang Wu

Sustainability 2023, 15(11), 8769; https://doi.org/10.3390/su15118769 - 29 May 2023

Viewed by 1197

Abstract

As an important part of the traffic infrastructure, the bridge has attracted much attention for its health conditions. Bridge damage detection is an important way of evaluating bridge health conditions. However, the bridge damage could not be directly expressed by traditional bridge damage [...] Read more.

As an important part of the traffic infrastructure, the bridge has attracted much attention for its health conditions. Bridge damage detection is an important way of evaluating bridge health conditions. However, the bridge damage could not be directly expressed by traditional bridge damage detection methods. With the emergence of new technologies and engineering applications, it is possible to achieve visual expression of bridge damages, improve the digitization of bridge detection, and promote sustainable development in bridge maintenance. In this paper, a method of bridge damage visualization based on image processing technology and the IFC standard description is proposed for common bridge damages. An existing cable-stayed bridge is taken as the engineering background, and the collected damage images are processed and analyzed utilizing cutting-edge technology to obtain specific damage information such as the length and width of bridge cracks and concrete defects. Then based on the IFC standard, the bridge damage and its attribute information are visualized and stored through an established REVIT model of the bridge. Full article

(This article belongs to the Special Issue Intelligent Vehicle-Infrastructure System and Sustainable Transportation)

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14 pages, 1691 KiB

Open AccessArticle

Modeling Mixed Traffic Flow with Connected Autonomous Vehicles and Human-Driven Vehicles in Off-Ramp Diverging Areas

by Xiangquan Chen, Zhizhou Wu and Yunyi Liang

Sustainability 2023, 15(7), 5651; https://doi.org/10.3390/su15075651 - 23 Mar 2023

Cited by 3 | Viewed by 1693

Abstract

This paper focuses on modeling mixed traffic flow that comprises human-driven vehicles (HV), adaptive cruise control (ACC) vehicles, and cooperative adaptive cruise control (CACC) vehicles in the off-ramp diverging area. The car-following behaviors of HVs, ACC vehicles, and CACC vehicles are modeled using [...] Read more.

This paper focuses on modeling mixed traffic flow that comprises human-driven vehicles (HV), adaptive cruise control (ACC) vehicles, and cooperative adaptive cruise control (CACC) vehicles in the off-ramp diverging area. The car-following behaviors of HVs, ACC vehicles, and CACC vehicles are modeled using an intelligent driver model (IDM), ACC car-following model, and CACC car-following model, respectively. The lane-changing behaviors of different types of vehicles in off-ramp diverging areas are modeled using the anticipatory lane change (ALC) model and the mandatory lane change (MLC) model. These models are important for describing the interaction among different types of vehicles in mixed traffic. The safety and efficiency of mixed traffic flow are analyzed by integrating the developed car-following models and lane-changing models in numerical simulation. A one-way, two-lane scenario is established for the simulation. The results reveal that when the proportion of CACC vehicles is about 0.6, the safety and general operating efficiency of mixed traffic flow in the off-ramp area deteriorate significantly. Increasing the conservative MLC zone length can improve the average speed of traffic flow. Guiding drivers in changing lanes is one way to improve the efficiency of traffic flow. Full article

(This article belongs to the Special Issue Intelligent Vehicle-Infrastructure System and Sustainable Transportation)

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21 pages, 5970 KiB

Open AccessArticle

Recognition of Commercial Vehicle Driving Cycles Based on Multilayer Perceptron Model

by Xianbin Wang, Yuqi Zhao and Weifeng Li

Sustainability 2023, 15(3), 2644; https://doi.org/10.3390/su15032644 - 1 Feb 2023

Viewed by 933

Abstract

In this paper, we propose a multilayer perceptron-based recognition method for driving cycles of commercial vehicles. Our method solves the problem of identifying the type of driving cycle for commercial vehicles, and improves the efficiency and sustainability of road traffic. We collect driving [...] Read more.

In this paper, we propose a multilayer perceptron-based recognition method for driving cycles of commercial vehicles. Our method solves the problem of identifying the type of driving cycle for commercial vehicles, and improves the efficiency and sustainability of road traffic. We collect driving condition data of 106,200 km long-distance commercial vehicles to validate our method. We pre-proceed six kinds of quantitative features as the data description; these are average speed, gear ratio, and accelerator pedal opening. Our model includes an input layer, hidden layers, and an output layer. The input layer receives and processes the input as low-dimensional features. The hidden layers consist of the feature extraction module and class regression module. The output layer projects extracted features to the classification space and computes the likelihood for each type. We achieve 99.83%, 97.85%, and 99.40% on the recognition accuracy for the expressway driving cycle, the suburban road driving cycle, and the urban road driving cycle, respectively. The experimental results demonstrate that our model achieves better results than the statistical method using Naive Bayes. Moreover, our method utilizes the data more efficiently and thus gains a better generalization performance. Full article

(This article belongs to the Special Issue Intelligent Vehicle-Infrastructure System and Sustainable Transportation)

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17 pages, 3472 KiB

Open AccessArticle

Optimization Method of Combined Multi-Mode Bus Scheduling under Unbalanced Conditions

by Dalong Li, Benxing Liu, Fangtong Jiao, Ziwen Song, Pengsheng Zhao, Xiaoqing Wang and Feng Sun

Sustainability 2022, 14(23), 15839; https://doi.org/10.3390/su142315839 - 28 Nov 2022

Viewed by 1065

Abstract

In view of the spatial and temporal imbalance of residents’ travel demands and challenges of optimal bus capacity allocation, in this paper the grand station express bus scheduling mode is introduced in the direction of heavy passenger flow during peak hours. Coordinated scheduling [...] Read more.

In view of the spatial and temporal imbalance of residents’ travel demands and challenges of optimal bus capacity allocation, in this paper the grand station express bus scheduling mode is introduced in the direction of heavy passenger flow during peak hours. Coordinated scheduling combining whole-journey and grand station express buses is adopted, and the station correlation calculation model is used to determine the optimal stops of the grand station express bus. Thus, a two-way bus scheduling optimization model for peak passenger flow is established with the goal of minimizing the total cost of passenger travel and enterprise operation. Finally, the nonlinear inertia weight dynamic cuckoo search algorithm is selected for the model’s solution, and the established scheduling optimization model is solved by combining basic data such as the study line’s bus Integrated Circuit (IC) card data. The effectiveness of the model is verified through a comparative study and evaluation of the solution. Full article

(This article belongs to the Special Issue Intelligent Vehicle-Infrastructure System and Sustainable Transportation)

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15 pages, 3329 KiB

Open AccessArticle

Algorithm for Measuring the Outer Contour Dimension of Trucks Using UAV Binocular Stereo Vision

by Shiwu Li, Lihong Han, Ping Dong and Wencai Sun

Sustainability 2022, 14(22), 14978; https://doi.org/10.3390/su142214978 - 12 Nov 2022

Cited by 6 | Viewed by 1408

Abstract

Promoting the management of the over-limit of freight transport vehicles plays an important role in the sustainable development of the highway industry. Vehicle outer contour dimension measurement is a key element in highway over-limit detection. The current detection approaches and research methods, however, [...] Read more.

Promoting the management of the over-limit of freight transport vehicles plays an important role in the sustainable development of the highway industry. Vehicle outer contour dimension measurement is a key element in highway over-limit detection. The current detection approaches and research methods, however, are insufficient for high-precision flow detection. Therefore, this study proposes an algorithm for measuring the dimensions of a truck’s outer contours, using unmanned aerial vehicle (UAV) binocular stereo vision. First, this study leverages a binocular camera mounted on a UAV to reconstruct the 3D point clouds of the truck. Second, the point cloud data are clustered using an FoF (Friends-of-Friends algorithm); this recognizes the cluster of truck points according to the truck’s characteristics. Finally, the principal component analysis and the Gaussian kernel density estimation are used to generate the outer contour dimensions of the trucks. Twenty model vehicles are selected as test objects to verify the reliability of the algorithm. The average error of the algorithm is represented by calculating the average value of the difference between the real size and the predicted size of the three dimensions. The experimental results demonstrate that the average error of this measurement approach is less than 2.5%, and the method is both stable and robust. This approach aligns with national regulations for over-limit detection. Full article

(This article belongs to the Special Issue Intelligent Vehicle-Infrastructure System and Sustainable Transportation)

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16 pages, 5463 KiB

Open AccessArticle

Robust Decoupling Vector Control of Interior Permanent Magnet Synchronous Motor Used in Electric Vehicles with Reduced Parameter Mismatch Impacts

by Shu Xiong, Jian Pan and Yucui Yang

Sustainability 2022, 14(19), 11910; https://doi.org/10.3390/su141911910 - 21 Sep 2022

Cited by 1 | Viewed by 1412

Abstract

Interior permanent magnet synchronous motor (IPMSM) drives have been widely employed in sustainable transport such as electric vehicles (EV). However, the traditional vector control (VC) strategies cannot achieve optimal control due to the intrinsic property of the IPMSMs, which is strong coupling. To [...] Read more.

Interior permanent magnet synchronous motor (IPMSM) drives have been widely employed in sustainable transport such as electric vehicles (EV). However, the traditional vector control (VC) strategies cannot achieve optimal control due to the intrinsic property of the IPMSMs, which is strong coupling. To solve the issue, this paper proposes an improved decoupling VC strategy to improve the steady-state performance of the IPMSMs with reduced parameter mismatch impacts. First, a deviation decoupling strategy is developed, and meanwhile, the parameters that influence the decoupling method are clearly illustrated. This enriches the theory concerning decoupling control and lays the ground for the development of effective solutions to the parameter mismatch issue. Second, the Luenberger observer theory is discussed, based on which the reason why the Luenberger inductance observers are not widely employed is explained for the first time. Third, with the aid of intermediate variables, which are the disturbances caused by the mismatched inductances, a new inductance identification method based on the Luenberger observer is proposed. Finally, the simulation and experimental results prove that the proposed decoupling methods, as well as the parameter identification method, are effective. Full article

(This article belongs to the Special Issue Intelligent Vehicle-Infrastructure System and Sustainable Transportation)

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23 pages, 16068 KiB

Open AccessArticle

Exploring the Spatiotemporal Characteristics and Causes of Rear-End Collisions on Urban Roadways

by Wenhui Zhang, Tuo Liu and Jing Yi

Sustainability 2022, 14(18), 11761; https://doi.org/10.3390/su141811761 - 19 Sep 2022

Cited by 3 | Viewed by 1256

Abstract

Rear-end collisions are caused by drivers misjudging urgent risks while following vehicles ahead in most cases. However, compared with other accident types, rear-end collisions have higher preventability. This study aims to reveal the prone segments and hours of rear-end collisions. First, we extracted [...] Read more.

Rear-end collisions are caused by drivers misjudging urgent risks while following vehicles ahead in most cases. However, compared with other accident types, rear-end collisions have higher preventability. This study aims to reveal the prone segments and hours of rear-end collisions. First, we extracted 1236 cases from traffic accident records in Harbin from 2015 to 2019. These accidents are classified as property damage accidents, injury accidents and fatal accidents according to the collision severity. Second, density analysis in GIS was used to demonstrate the spatial distribution of rear-end collisions. The collision spots considering the density and severity were visually displayed. We counted the hourly and seasonal distribution characteristics according to the statistical data. Finally, LightGBM and random forest classifier models were used to evaluate the substantial factors affecting accident severity. The results have potential practical value in rear-end collision warning and prevention. Full article

(This article belongs to the Special Issue Intelligent Vehicle-Infrastructure System and Sustainable Transportation)

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