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Recent Advance in Intelligent Vehicle
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Recent Advance in Intelligent Vehicle

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 17266

Special Issue Editors

Dr. Biao Yu

E-Mail Website
Guest Editor
Hefei Institutes of Physical Sciences, Chinese Academy of Sciences, Hefei, China
Interests: intelligent vehicles; intelligence test and evaluation
Dr. Linglong Lin

E-Mail Website
Guest Editor
Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
Interests: intelligent vehicles; maneuver decision making; path planning
Dr. Jiajia Chen

E-Mail Website
Guest Editor
School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, China
Interests: intelligent vehicles; maneuver decision making; path planning

Special Issue Information

Dear Colleagues,

Intelligent vehicles have been considered an essential way to improve urban mobility, as well as reduce emission pollution and traffic accidents. With the development of artificial intelligence, such as deep learning, intelligent vehicle technologies have obtained enormous success. However, due to the unmatured of the critical technologies, such as environment perception, motion planning, behavior decision, and motion control, the intelligent vehicle still cannot be deployed in real and complex scenarios.

The intelligent vehicle is a very complicated technical system. A lot of critical technologies from different disciplines such as sensor technology, pattern recognition, control engineering, artificial intelligence, and vehicle engineering can affect its performance. This Special Issue aims to explore the recent progress in these related research fields. Welcome topics include, but are not strictly limited to, the following:

  • Imaging and sensor technology, such as LiDAR, camera, millimeter wave radar, and so on;
  • Environment perception technology, such as vehicle/pedestrian detection, tracking and prediction, travelable area detection, ground segmentation, and so on;
  • Planning and control technology, such as global planning, local planning, behavior decision, motion control, and so on;
  • Navigation and localization technology, such as lidar odometry, vision odometry, simultaneous localization and mapping (SLAM), and so on;
  • Intelligence test and evaluation.

Dr. Biao Yu
Dr. Linglong Lin
Dr. Jiajia Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. World Electric Vehicle Journal is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent vehicles
  • environment perception
  • object detection and tracking
  • behavior decision
  • motion planning
  • motion control
  • intelligence test
  • navigation and localization
  • deep learning
  • reinforcement learning

Published Papers (10 papers)

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Research

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16 pages, 7591 KiB  
Article
Autonomous Parking Space Detection for Electric Vehicles Based on Improved YOLOV5-OBB Algorithm
by Zhaoyan Chen, Xiaolan Wang, Weiwei Zhang, Guodong Yao, Dongdong Li and Li Zeng
World Electr. Veh. J. 2023, 14(10), 276; https://doi.org/10.3390/wevj14100276 - 02 Oct 2023
Cited by 2 | Viewed by 1726
Abstract
Currently, in the process of autonomous parking, the algorithm detection accuracy and rate of parking spaces are low due to the diversity of parking scenes, changes in lighting conditions, and other unfavorable factors. An improved algorithm based on YOLOv5-OBB is proposed to reduce [...] Read more.
Currently, in the process of autonomous parking, the algorithm detection accuracy and rate of parking spaces are low due to the diversity of parking scenes, changes in lighting conditions, and other unfavorable factors. An improved algorithm based on YOLOv5-OBB is proposed to reduce the computational effort of the model and increase the speed of model detection. Firstly, the backbone module is optimized, the Focus module and SSP (Selective Spatial Perception) module are replaced with the general convolution and SSPF (Selective Search Proposals Fusion) modules, and the GELU activation function is introduced to reduce the number of model parameters and enhance model learning. Secondly, the RFB (Receptive Field Block) module is added to fuse different feature modules and increase the perceptual field to optimize the small target detection. After that, the CA (coordinate attention) mechanism is introduced to enhance the feature representation capability. Finally, the post-processing is optimized using spatial location correlation to improve the accuracy of the vehicle position and bank angle detection. The implementation results show that by using the improved method proposed in this paper, the FPS of the model is improved by 2.87, algorithm size is reduced by 1 M, and the mAP is improved by 8.4% on the homemade dataset compared with the original algorithm. The improved model meets the requirements of perceived accuracy and speed of parking spaces in autonomous parking. Full article
(This article belongs to the Special Issue Recent Advance in Intelligent Vehicle)
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16 pages, 2578 KiB  
Article
Research on Reinforcement-Learning-Based Truck Platooning Control Strategies in Highway On-Ramp Regions
by Jiajia Chen, Zheng Zhou, Yue Duan and Biao Yu
World Electr. Veh. J. 2023, 14(10), 273; https://doi.org/10.3390/wevj14100273 - 01 Oct 2023
Viewed by 1263
Abstract
With the development of autonomous driving technology, truck platooning control has become a reality. Truck platooning can improve road capacity by maintaining a minor headway. Platooning systems can significantly reduce fuel consumption and emissions, especially for trucks. In this study, we designed a [...] Read more.
With the development of autonomous driving technology, truck platooning control has become a reality. Truck platooning can improve road capacity by maintaining a minor headway. Platooning systems can significantly reduce fuel consumption and emissions, especially for trucks. In this study, we designed a Platoon-MAPPO algorithm to implement truck platooning control based on multi-agent reinforcement learning for a platooning facing an on-ramp scenario on highway. A centralized training, decentralized execution algorithm was used in this paper. Each truck only computes its actions, avoiding the data computation delay problem caused by centralized computation. Each truck considers the truck status in front of and behind itself, maximizing the overall gain of the platooning and improving the global operational efficiency. In terms of performance evaluation, we used the traditional rule-based platooning following model as a benchmark. To ensure fairness, the model used the same network structure and traffic scenario as our proposed model. The simulation results show that the algorithm proposed in this paper has good performance and improves the overall efficiency of the platoon while guaranteeing traffic safety. The average energy consumption decreased by 14.8%, and the road occupancy rate decreased by 43.3%. Full article
(This article belongs to the Special Issue Recent Advance in Intelligent Vehicle)
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23 pages, 4946 KiB  
Article
Online Multiple Object Tracking Using Min-Cost Flow on Temporal Window for Autonomous Driving
by Hongjian Wei, Yingping Huang, Qian Zhang and Zhiyang Guo
World Electr. Veh. J. 2023, 14(9), 243; https://doi.org/10.3390/wevj14090243 - 02 Sep 2023
Viewed by 1007
Abstract
Multiple object tracking (MOT), as a core technology for environment perception in autonomous driving, has attracted attention from researchers. Combing the advantages of batch global optimization, we present a novel online MOT framework for autonomous driving, consisting of feature extraction and data association [...] Read more.
Multiple object tracking (MOT), as a core technology for environment perception in autonomous driving, has attracted attention from researchers. Combing the advantages of batch global optimization, we present a novel online MOT framework for autonomous driving, consisting of feature extraction and data association on a temporal window. In the feature extraction stage, we design a three-channel appearance feature extraction network based on metric learning by using ResNet50 as the backbone network and the triplet loss function and employ a Kalman Filter with a constant acceleration motion model to optimize and predict the object bounding box information, so as to obtain reliable and discriminative object representation features. For data association, to reduce the ID switches, the min-cost flow of global association is introduced within the temporal window composed of consecutive multi-frame images. The trajectories within the temporal window are divided into two categories, active trajectories and inactive trajectories, and the appearance, motion affinities between each category of trajectories, and detections are calculated, respectively. Based on this, a sparse affinity network is constructed, and the data association is achieved using the min-cost flow problem of the network. Qualitative experimental results on KITTI MOT public benchmark dataset and real-world campus scenario sequences validate the effectiveness and robustness of our method. Compared with the homogeneous, vision-based MOT methods, quantitative experimental results demonstrate that our method has competitive advantages in terms of higher order tracking accuracy, association accuracy, and ID switches. Full article
(This article belongs to the Special Issue Recent Advance in Intelligent Vehicle)
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14 pages, 3627 KiB  
Article
Bird’s-Eye View Semantic Segmentation for Autonomous Driving through the Large Kernel Attention Encoder and Bilinear-Attention Transform Module
by Ke Li, Xuncheng Wu, Weiwei Zhang and Wangpengfei Yu
World Electr. Veh. J. 2023, 14(9), 239; https://doi.org/10.3390/wevj14090239 - 01 Sep 2023
Viewed by 1744
Abstract
Building an autonomous driving system requires a detailed and unified semantic representation from multiple cameras. The bird’s eye view (BEV) has demonstrated remarkable potential as a comprehensive and unified perspective. However, most current research focuses on innovating the view transform module, ignoring whether [...] Read more.
Building an autonomous driving system requires a detailed and unified semantic representation from multiple cameras. The bird’s eye view (BEV) has demonstrated remarkable potential as a comprehensive and unified perspective. However, most current research focuses on innovating the view transform module, ignoring whether the crucial image encoder can construct long-range feature relationships. Hence, we redesign an image encoder with a large kernel attention mechanism to encode image features. Considering the performance gains obtained by the complex view transform module are insignificant, we propose a simple and effective Bilinear-Attention Transform module to lift the dimension completely. Finally, we redesign a BEV encoder with a CNN block of a larger kernel size to reduce the distortion of BEV features away from the ego vehicle. The results on the nuScenes dataset confirm that our model outperforms other models with equivalent training settings on the segmentation task and approaches state-of-the-art performance. Full article
(This article belongs to the Special Issue Recent Advance in Intelligent Vehicle)
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14 pages, 8612 KiB  
Article
A Two-Stage Pillar Feature-Encoding Network for Pillar-Based 3D Object Detection
by Hao Xu, Xiang Dong, Wenxuan Wu, Biao Yu and Hui Zhu
World Electr. Veh. J. 2023, 14(6), 146; https://doi.org/10.3390/wevj14060146 - 03 Jun 2023
Cited by 1 | Viewed by 1427
Abstract
Three-dimensional object detection plays a vital role in the field of environment perception in autonomous driving, and its results are crucial for the subsequent processes. Pillar-based 3D object detection is a method to detect objects in 3D by dividing point cloud data into [...] Read more.
Three-dimensional object detection plays a vital role in the field of environment perception in autonomous driving, and its results are crucial for the subsequent processes. Pillar-based 3D object detection is a method to detect objects in 3D by dividing point cloud data into pillars and extracting features from each pillar. However, the current pillar-based 3D object-detection methods suffer from problems such as “under-segmentation” and false detections in overlapping and occluded scenes. To address these challenges, we propose an improved pillar-based 3D object-detection network with a two-stage pillar feature-encoding (Ts-PFE) module that considers both inter- and intra-relational features among and in the pillars. This novel approach enhances the model’s ability to identify the local structure and global distribution of the data, which improves the distinction between objects in occluded and overlapping scenes and ultimately reduces under-segmentation and false detection problems. Furthermore, we use the attention mechanism to improve the backbone and make it focus on important features. The proposed approach is evaluated on the KITTI dataset. The experimental results show that the detection accuracy of the proposed approach are significantly improved on the benchmarks of BEV and 3D. The improvement of AP for car, pedestrian, and cyclist 3D detection are 1.1%, 3.78%, and 2.23% over PointPillars. Full article
(This article belongs to the Special Issue Recent Advance in Intelligent Vehicle)
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21 pages, 10854 KiB  
Article
Accelerated and Refined Lane-Level Route-Planning Method Based on a New Road Network Model for Autonomous Vehicle Navigation
by Ke He, Haitao Ding, Nan Xu and Konghui Guo
World Electr. Veh. J. 2023, 14(4), 98; https://doi.org/10.3390/wevj14040098 - 06 Apr 2023
Viewed by 1717
Abstract
Lane-level route planning is a critical issue for a lane-level navigation system for autonomous vehicles. Current route-planning methods mainly focus on the road level and applying them directly to search for lane-level routes results in a reduction in search efficiency. In addition, previously [...] Read more.
Lane-level route planning is a critical issue for a lane-level navigation system for autonomous vehicles. Current route-planning methods mainly focus on the road level and applying them directly to search for lane-level routes results in a reduction in search efficiency. In addition, previously developed lane-level methods lack consideration for vehicle characteristics and adaptability to multiple road network structures. To solve this issue, this study proposes an accelerated and refined lane-level route-planning algorithm based on a new lane-level road network model. First, five sub-layers are designed to refine the internal structure of the divided road and intersection areas so that the model can express multiple variations in road network structures. Then, a multi-level route-planning algorithm is designed for sequential planning at the road level, lane group level, lane section level, and lane level to reduce the search space and significantly improve routing efficiency. Last, an optimal lane determination algorithm considering traffic rules, vehicle characteristics, and optimization objectives is developed at the lane level to find the optimal lanes on roads with different configurations, including those with a constant or variable number of lanes while satisfying traffic rules and vehicle characteristics. Tests were performed on simulated road networks and a real road network. The results demonstrate the algorithm’s better adaptability to changing road network structures and vehicle characteristics compared with past hierarchical route planning, and its higher efficiency compared with direct route planning, past hierarchical route planning, and the Apollo route-planning method, which can better support autonomous vehicle navigation. Full article
(This article belongs to the Special Issue Recent Advance in Intelligent Vehicle)
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28 pages, 11674 KiB  
Article
Driving Decisions for Autonomous Vehicles in Intersection Environments: Deep Reinforcement Learning Approaches with Risk Assessment
by Wangpengfei Yu, Yubin Qian, Jiejie Xu, Hongtao Sun and Junxiang Wang
World Electr. Veh. J. 2023, 14(4), 79; https://doi.org/10.3390/wevj14040079 - 23 Mar 2023
Cited by 1 | Viewed by 1954
Abstract
Intersection scenarios are one of the most complex and high-risk traffic scenarios. Therefore, it is important to propose a vehicle driving decision algorithm for intersection scenarios. Most of the related studies have focused on considering explicit collision risks while lacking consideration for potential [...] Read more.
Intersection scenarios are one of the most complex and high-risk traffic scenarios. Therefore, it is important to propose a vehicle driving decision algorithm for intersection scenarios. Most of the related studies have focused on considering explicit collision risks while lacking consideration for potential driving risks. Therefore, this study proposes a deep-reinforcement-learning-based driving decision algorithm to address these problems. In this study, a non-deterministic vehicle driving risk assessment method is proposed for intersection scenarios and introduced into a learning-based intelligent driving decision algorithm. In addition, this study proposes an attention network based on state information. In this study, a typical intersection scenario was constructed using simulation software, and experiments were conducted. The experimental results show that the algorithm proposed in this paper can effectively derive a driving strategy with both driving efficiency and driving safety in the intersection driving scenario. It is also demonstrated that the attentional neural network designed in this study helps intelligent vehicles to perceive the surrounding environment more accurately, improves the performance of intelligent vehicles, as well as accelerates the convergence speed. Full article
(This article belongs to the Special Issue Recent Advance in Intelligent Vehicle)
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17 pages, 3118 KiB  
Article
Coordinated Control of Unmanned Electric Formula Car
by Hua Tao and Baocheng Yang
World Electr. Veh. J. 2023, 14(3), 58; https://doi.org/10.3390/wevj14030058 - 24 Feb 2023
Viewed by 1323
Abstract
The coordinated control method of Unmanned Electric Formula Racing (UEFC) was studied to improve the handling stability of UEFC. The UEFC’s mechanical structure, which is based on the driving system and transmission system, was designed. In accordance with mechanical structure of the designed [...] Read more.
The coordinated control method of Unmanned Electric Formula Racing (UEFC) was studied to improve the handling stability of UEFC. The UEFC’s mechanical structure, which is based on the driving system and transmission system, was designed. In accordance with mechanical structure of the designed racing car, a seven-degree of freedom mathematical model of the UEFC was established. In accordance with the built mathematical model of racing car, the lateral controller of racing car was designed by using a fuzzy neural network method. The longitudinal controller of the racing car was designed by using the method of incremental PID control, and the coordination controller of the racing car was designed by combining the lateral controller and the longitudinal controller so as to realize the lateral and longitudinal coordination control of the UEFC. The experimental results showed that the output parameters such as yaw rate, vehicle speed and heading angle were slightly different from the expected output. It was confirmed that the research method can enhance the handling stability of the UEFC. Full article
(This article belongs to the Special Issue Recent Advance in Intelligent Vehicle)
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15 pages, 5870 KiB  
Article
Interpolation-Based Framework for Generation of Ground Truth Data for Testing Lane Detection Algorithm for Automated Vehicle
by Swapnil Waykole, Nirajan Shiwakoti and Peter Stasinopoulos
World Electr. Veh. J. 2023, 14(2), 48; https://doi.org/10.3390/wevj14020048 - 09 Feb 2023
Cited by 1 | Viewed by 1870
Abstract
Automated vehicles, predicted to be fully electric in future, are expected to reduce road fatalities and road traffic emissions. The lane departure warning system, an important feature of automated vehicles, utilize lane detection and tracking algorithms. Researchers are constrained to test their lane [...] Read more.
Automated vehicles, predicted to be fully electric in future, are expected to reduce road fatalities and road traffic emissions. The lane departure warning system, an important feature of automated vehicles, utilize lane detection and tracking algorithms. Researchers are constrained to test their lane detection algorithms because of the small publicly available datasets. Additionally, those datasets may not represent differences in road geometries, lane marking and other details unique to a particular geographic location. Existing methods to develop the ground truth datasets are time intensive. To address this gap, this study proposed a framework for an interpolation approach for quickly generating reliable ground truth data. The proposed method leverages the advantage of the existing manual and time-slice approaches. A detailed framework for the interpolation approach is presented and the performance of the approach is compared with the existing methods. Video datasets for performance evaluation were collected in Melbourne, Australia. The results show that the proposed approach outperformed four existing approaches with a reduction in time for generating ground truth data in the range from 4.8% to 87.4%. A reliable and quick method for generating ground truth data, as proposed in this study, will be valuable to researchers as they can use it to test and evaluate their lane detection and tracking algorithms. Full article
(This article belongs to the Special Issue Recent Advance in Intelligent Vehicle)
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Review

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31 pages, 2285 KiB  
Review
Exploring Computing Paradigms for Electric Vehicles: From Cloud to Edge Intelligence, Challenges and Future Directions
by Sachin B. Chougule, Bharat S. Chaudhari, Sheetal N. Ghorpade and Marco Zennaro
World Electr. Veh. J. 2024, 15(2), 39; https://doi.org/10.3390/wevj15020039 - 26 Jan 2024
Viewed by 1613
Abstract
Electric vehicles are widely adopted globally as a sustainable mode of transportation. With the increased availability of onboard computation and communication capabilities, vehicles are moving towards automated driving and intelligent transportation systems. The adaption of technologies such as IoT, edge intelligence, 5G, and [...] Read more.
Electric vehicles are widely adopted globally as a sustainable mode of transportation. With the increased availability of onboard computation and communication capabilities, vehicles are moving towards automated driving and intelligent transportation systems. The adaption of technologies such as IoT, edge intelligence, 5G, and blockchain in vehicle architecture has increased possibilities towards efficient and sustainable transportation systems. In this article, we present a comprehensive study and analysis of the edge computing paradigm, explaining elements of edge AI. Furthermore, we discussed the edge intelligence approach for deploying AI algorithms and models on edge devices, which are typically resource-constrained devices located at the edge of the network. It mentions the advantages of edge intelligence and its use cases in smart electric vehicles. It also discusses challenges and opportunities and provides in-depth analysis for optimizing computation for edge intelligence. Finally, it sheds some light on the research roadmap on AI for edge and AI on edge by dividing efforts into topology, content, service segments, model adaptation, framework design, and processor acceleration, all of which stand to gain advantages from AI technologies. Investigating the incorporation of important technologies, issues, opportunities, and Roadmap in this study will be a valuable resource for the community engaged in research on edge intelligence in electric vehicles. Full article
(This article belongs to the Special Issue Recent Advance in Intelligent Vehicle)
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