World Electric Vehicle Journal

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14 pages, 1421 KiB

Open AccessArticle

Systematic Evaluation of a Connected Vehicle-Enabled Freeway Incident Management System

by Hao Yang and Jinghui Wang

World Electr. Veh. J. 2025, 16(2), 59; https://doi.org/10.3390/wevj16020059 - 21 Jan 2025

Viewed by 440

Abstract

Freeway incidents block road lanes and result in increasing travel time delays. The intense lane changes of upstream vehicles may also lead to capacity drop and more congestion. Connected vehicles (CVs) offer a viable solution to minimize the impact of such incidents via [...] Read more.

Freeway incidents block road lanes and result in increasing travel time delays. The intense lane changes of upstream vehicles may also lead to capacity drop and more congestion. Connected vehicles (CVs) offer a viable solution to minimize the impact of such incidents via monitoring the status of the incidents and providing real-time driving guidance. This paper evaluates the performance of an existing CV-enabled incident management system, which minimizes travel time by effectively leading CVs to bypass incident spots. This study comprehensively quantifies the effects of system parameters (speed weight and lane-changing inertia), control segment length, and road information-updating intervals. This analysis identifies the optimal settings for the incident management system to minimize vehicle travel time delays. Additionally, this paper evaluates the influence of CV market penetration rates (MPRs), network volume-to-capacity ratios, and incident settings to understand the system benefits under varying connected environments and traffic conditions. The results reveal that with the control of the proposed system, overall travel delays can be reduced by up to 45% and that road congestion caused by incidents can be mitigated quickly. Full article

(This article belongs to the Special Issue Vehicle-Road Collaboration and Connected Automated Driving)

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34 pages, 1009 KiB

Open AccessArticle

A Review of Environmental Perception Technology Based on Multi-Sensor Information Fusion in Autonomous Driving

by Boquan Yang, Jixiong Li and Ting Zeng

World Electr. Veh. J. 2025, 16(1), 20; https://doi.org/10.3390/wevj16010020 - 2 Jan 2025

Viewed by 1233

Abstract

Environmental perception is a key technology for autonomous driving, enabling vehicles to analyze and interpret their surroundings in real time to ensure safe navigation and decision-making. Multi-sensor information fusion, which integrates data from different sensors, has become an important approach to overcome the [...] Read more.

Environmental perception is a key technology for autonomous driving, enabling vehicles to analyze and interpret their surroundings in real time to ensure safe navigation and decision-making. Multi-sensor information fusion, which integrates data from different sensors, has become an important approach to overcome the limitations of individual sensors. Each sensor has unique advantages. However, its own limitations, such as sensitivity to lighting, weather, and range, require fusion methods to provide a more comprehensive and accurate understanding of the environment. This paper describes multi-sensor information fusion techniques for autonomous driving environmental perception. Various fusion levels, including data-level, feature-level, and decision-level fusion, are explored, highlighting how these methods can improve the accuracy and reliability of perception tasks such as object detection, tracking, localization, and scene segmentation. In addition, this paper explores the critical role of sensor calibration, focusing on methods to align data in a unified reference frame to improve fusion results. Finally, this paper discusses recent advances, especially the application of machine learning in sensor fusion, and highlights the challenges and future research directions required to further enhance the environmental perception of autonomous systems. This study provides a comprehensive review of multi-sensor fusion technology and deeply analyzes the advantages and challenges of different fusion methods, providing a valuable reference and guidance for the field of autonomous driving. Full article

(This article belongs to the Special Issue Vehicle-Road Collaboration and Connected Automated Driving)

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14 pages, 13514 KiB

Open AccessArticle

A Nighttime Driving-Scene Segmentation Method Based on Light-Enhanced Network

by Lihua Bi, Wenjiao Zhang, Xiangfei Zhang and Canlin Li

World Electr. Veh. J. 2024, 15(11), 490; https://doi.org/10.3390/wevj15110490 - 27 Oct 2024

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Abstract

To solve the semantic segmentation problem of night driving-scene images, which often have low brightness, low contrast, and uneven illumination, a nighttime driving-scene segmentation method based on a light-enhanced network was proposed. Firstly, we designed a light enhancement network, which comprises two parts: [...] Read more.

To solve the semantic segmentation problem of night driving-scene images, which often have low brightness, low contrast, and uneven illumination, a nighttime driving-scene segmentation method based on a light-enhanced network was proposed. Firstly, we designed a light enhancement network, which comprises two parts: a color correction module and a parameter predictor. The color correction module mitigates the impact of illumination variations on the segmentation network by adjusting the color information of the image. Meanwhile, the parameter predictor accurately predicts the parameters of the image filter through the analysis of global content, including factors such as brightness, contrast, hue, and exposure level, thereby effectively enhancing the image quality. Subsequently, the output of the light enhancement network is input into the segmentation network to obtain the final segmentation prediction. Experimental results show that the proposed method achieves mean Intersection over Union (mIoU) values of 59.4% on the Dark Zurich-test dataset, outperforming other segmentation algorithms for nighttime driving-scenes. Full article

(This article belongs to the Special Issue Vehicle-Road Collaboration and Connected Automated Driving)

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14 pages, 2976 KiB

Open AccessArticle

Off-Road Environment Semantic Segmentation for Autonomous Vehicles Based on Multi-Scale Feature Fusion

by Xiaojing Zhou, Yunjia Feng, Xu Li, Zijian Zhu and Yanzhong Hu

World Electr. Veh. J. 2023, 14(10), 291; https://doi.org/10.3390/wevj14100291 - 13 Oct 2023

Cited by 1 | Viewed by 2916

Abstract

For autonomous vehicles driving in off-road environments, it is crucial to have a sensitive environmental perception ability. However, semantic segmentation in complex scenes remains a challenging task. Most current methods for off-road environments often have the problems of single scene and low accuracy. [...] Read more.

For autonomous vehicles driving in off-road environments, it is crucial to have a sensitive environmental perception ability. However, semantic segmentation in complex scenes remains a challenging task. Most current methods for off-road environments often have the problems of single scene and low accuracy. Therefore, this paper proposes a semantic segmentation network based on LiDAR called Multi-scale Augmentation Point-Cylinder Network (MAPC-Net). The network uses a multi-layer receptive field fusion module to extract features from objects of different scales in off-road environments. Gated feature fusion is used to fuse PointTensor and Cylinder for encoding and decoding. In addition, we use CARLA to build off-road environments for obtaining datasets, and employ linear interpolation to enhance the training data to solve the problem of sample imbalance. Finally, we design experiments to verify the excellent semantic segmentation ability of MAPC-Net in an off-road environment. We also demonstrate the effectiveness of the multi-layer receptive field fusion module and data augmentation. Full article

(This article belongs to the Special Issue Vehicle-Road Collaboration and Connected Automated Driving)

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19 pages, 1093 KiB

Open AccessArticle

Toward Synthetic Data Generation to Enhance Skidding Detection in Winter Conditions

by Bryan McKenzie, Sousso Kelouwani and Marc-André Gaudreau

World Electr. Veh. J. 2022, 13(12), 231; https://doi.org/10.3390/wevj13120231 - 2 Dec 2022

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Abstract

In this paper, we propose the use of a neural network to identify lateral skidding events of road vehicles used during winter driving conditions. Firstly, data from a simulation model was used to identify the essential vehicle dynamics variables needed and to create [...] Read more.

In this paper, we propose the use of a neural network to identify lateral skidding events of road vehicles used during winter driving conditions. Firstly, data from a simulation model was used to identify the essential vehicle dynamics variables needed and to create the network structure. Then this network was retrained to classify real-world vehicle skidding events. The final network consists of a 3 layer network with 10, 5 and 1 output neurons 13 inputs, 4 outputs and a 5 step time delay. The retrained network was used on a limited set of real vehicle data and confirmed the effectiveness of the network classifying lateral skidding events. Full article

(This article belongs to the Special Issue Vehicle-Road Collaboration and Connected Automated Driving)

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15 pages, 5111 KiB

Open AccessArticle

Research on Short-Term Driver Following Habits Based on GA-BP Neural Network

by Cheng Wu, Bo Li, Shaoyi Bei, Yunhai Zhu, Jing Tian, Hongzhen Hu and Haoran Tang

World Electr. Veh. J. 2022, 13(9), 171; https://doi.org/10.3390/wevj13090171 - 14 Sep 2022

Cited by 5 | Viewed by 1802

Abstract

The current commercial intelligent driving systems still take the optimal strategy judged by the machine to be the only goal. Therefore, in order to improve the driving experience of the intelligent driving following scene, based on the assumption that environmental factors remain unchanged [...] Read more.

The current commercial intelligent driving systems still take the optimal strategy judged by the machine to be the only goal. Therefore, in order to improve the driving experience of the intelligent driving following scene, based on the assumption that environmental factors remain unchanged for a short time, five important parameters affecting the following scene are selected through correlation analysis, and vehicle-following research is carried out. This paper adopts a driver-following model based on a Genetic Algorithm (GA)-optimized Back Propagation (BP) neural network. Based on the data of next-generation simulation (ngsim), this paper selects vehicle 32 (32 represents the ID of the vehicle in the ngsim project) as the main vehicle in order to study short-term driving habits. A BP neural network is built using MATLAB; 60% of the data of vehicles 32 and 29 is used for the training set, 20% is used for the verification set, and 20% for the test set. Because short-term prediction requires high timeliness, the genetic algorithm is used to optimize the initial weights of the neural network, which not only accelerates the convergence speed but also plays a role in avoiding the local optimal solution. The experimental results show that compared with the traditional stimulus-response vehicle-following model, this model has a following ability that is more in line with the driver’s driving habits in terms of ensuring following safety. Full article

(This article belongs to the Special Issue Vehicle-Road Collaboration and Connected Automated Driving)

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15 pages, 7493 KiB

Open AccessArticle

Research on Automatic Driving Path Tracking Control of Open-Pit Mine Transportation Vehicles with Delay Compensation

by Zhiyong Lei, Xiaolong Ma, Xiwen Yuan and Chuan He

World Electr. Veh. J. 2022, 13(8), 134; https://doi.org/10.3390/wevj13080134 - 26 Jul 2022

Cited by 3 | Viewed by 2230

Abstract

The transportation environment of the open-pit mine is complex, the steering actuator of the mine vehicle has a large delay and poor response accuracy, and there are a lot of bumpy roads, large undulating ramps, and narrow-area curves in the mining area. These [...] Read more.

The transportation environment of the open-pit mine is complex, the steering actuator of the mine vehicle has a large delay and poor response accuracy, and there are a lot of bumpy roads, large undulating ramps, and narrow-area curves in the mining area. These road sections seriously reduce the tracking accuracy of the mine vehicle path. Tracking control presents great challenges. Therefore, this study first conducts a simulation comparison study on commonly used path tracking methods such as pure pursuit control, Stanley control, and model predictive control (MPC), and then designs a path tracking control strategy for automatic driving of open-pit mine transportation vehicles based on the MPC algorithm. Finally, the proposed control strategy was verified through actual mining vehicle tests. The results showed that the maximum lateral deviation obtained by the MPC-based path tracking control strategy was reduced from 0.55 m to 0.08 m under the C-shaped reference path compared with the traditional method. Under the S-shaped reference path, the lateral deviation is reduced from 0.4 m to 0.16 m. Full article

(This article belongs to the Special Issue Vehicle-Road Collaboration and Connected Automated Driving)

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Other

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20 pages, 4152 KiB

Open AccessViewpoint

Path-Following Control of Unmanned Vehicles Based on Optimal Preview Time Model Predictive Control

by Xinyu Wang, Xiao Ye, Yipeng Zhou and Cong Li

World Electr. Veh. J. 2024, 15(6), 221; https://doi.org/10.3390/wevj15060221 - 21 May 2024

Viewed by 952

Abstract

In order to reduce the lateral error of path-following control of unmanned vehicles under variable curvature paths, we propose a path-following control strategy for unmanned vehicles based on optimal preview time model predictive control (OP-MPC). The strategy includes the longitudinal speed limit, the [...] Read more.

In order to reduce the lateral error of path-following control of unmanned vehicles under variable curvature paths, we propose a path-following control strategy for unmanned vehicles based on optimal preview time model predictive control (OP-MPC). The strategy includes the longitudinal speed limit, the optimal preview time surface, and the model predictive control (MPC)controller. The longitudinal speed limit controls speed to prevent vehicle rollover and sideslip. The optimal preview time surface adjusts the preview time according to the vehicle speed and path curvature. The preview point determined by the preview time is used as the reference waypoint of OP-MPC controller. Finally, the effectiveness of the strategy was verified through simulation and with the real unmanned vehicle. The maximum lateral deviation obtained by the OP-MPC controller was reduced from 0.522 m to 0.145 m under the simulation compared with an MPC controller. The maximum lateral deviation obtained by the OP-MPC controller was reduced from 0.5185 m to 0.2298 m under the real unmanned vehicle compared with the MPC controller. Full article

(This article belongs to the Special Issue Vehicle-Road Collaboration and Connected Automated Driving)

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