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The journal Automotive Innovation is sponsored by China SAE, published through Springer, distributed around the world, and reflects the top-level research and technical advance of automotive engineering. Automotive Innovation newsletter in April includes the following contents： 1. Article Recommendation——Three papers on Intelligent Vehicles 2. China SAE News: · WNEVC 2023 will be held at IAA Mobility in Munich in September |
  
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Parameter Effects of the Potential-Field-Driven Model Predictive Controller for Shared Control Mingjun Li, Chao Jiang, Xiaolin Song & Haotian Cao |
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| Parameter effects of the potential-field-driven model predictive control (PF-MPC) method on performances of shared control systems during obstacles avoidance are investigated. The PF-MPC controllers of autonomous driving and shared control systems are designed based on the constructed potential fields and model predictive control method, and the driver-vehicle dynamics and the driver-related costs are also considered in the design of the shared controller. To explore a potential approach of alleviating driver-automation conflicts of the shared control systems, different motion planning results generated by the PF-MPC controller are explored by adjusting effects of potential fields’ parameters, which provides possibilities to decrease driver-automation conflicts between the planned trajectory and driver’s target path. Moreover, two case studies are designed to discuss different frameworks and parameters effects on shared control systems. Results show that the proposed shared control frameworks considering driver-vehicle dynamics and the driver-related cost show better performances regarding driver-automation conflicts management and driving safety than the decentralized control framework. And the longitudinal normalized constant of potential fields parameters shows influences on the driver-automation conflicts management and driving safety performances of shared control. | |||
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Keywords: PF-MPC method · Potential field · Parameter effect · Driver-automation conflict Li, M., Jiang, C., Song, X. et al.: Parameter effects of the potential-field-driven model predictive controller for shared control. Automot. Innov. 6(1), 48–61 (2023) |
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Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems Caizhi Zhang, Weifeng Huang, Tong Niu, Zhitao Liu, Guofa Li & Dongpu Cao |
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| Clustering is an unsupervised learning technology, and it groups information (observations or datasets) according to similarity measures. Developing clustering algorithms is a hot topic in recent years, and this area develops rapidly with the increasing complexity of data and the volume of datasets. In this paper, the concept of clustering is introduced, and the clustering technologies are analyzed from traditional and modern perspectives. First, this paper summarizes the principles, advantages, and disadvantages of 20 traditional clustering algorithms and 4 modern algorithms. Then, the core elements of clustering are presented, such as similarity measures and evaluation index. Considering that data processing is often applied in vehicle engineering, finally, some specific applications of clustering algorithms in vehicles are listed and the future development of clustering in the era of big data is highlighted. The purpose of this review is to make a comprehensive survey that helps readers learn various clustering algorithms and choose the appropriate methods to use, especially in vehicles. | |||
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Keywords: Unsupervised learning · Clustering · Similarity measures · Vehicle
Zhang, C., Huang, W., Niu, T. et al.: Review of clustering technology and its application in coordinating vehicle subsystems. Automot. Innov. 6(1), 89–115 (2023) |
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Density-Based Road Segmentation Algorithm for Point Cloud Collected by Roadside LiDAR Yang He, Lisheng Jin, Baicang Guo, Zhen Huo, Huanhuan Wang & Qiukun Jin |
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| This paper proposes a novel density-based real-time segmentation algorithm, to extract ground point cloud in real time from point cloud data collected by roadside LiDAR. The algorithm solves the problems such as the large amount of original point cloud data collected by LiDAR, which leads to heavy computational burden in ground point search. First, point cloud data is filtered by straight-through filtering method and rasterized to improve the real-time performance of the algorithm. Then, the density of the point cloud in horizontal plane is calculated, and the threshold of the density is selected to extract the low-density regional point cloud according to the density statistical histogram and 95% loci. Finally, the low-density regional point cloud is used as the initial ground seeds for iterative optimization of ground parameters, and the ground point cloud is extracted by the fitted ground model to realize road point cloud extraction. The experimental results on 1055 frames of continuous data collected on real scenes show that the average time consumption of the proposed method is 0.11 s, and the average segmentation precision is 92.48%. This shows that the density-based road segmentation algorithm can reduce the time of point cloud traversal in the process of ground parameter fitting and improve the real-time performance of the algorithm while maintaining the accuracy of ground extraction. | |||
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Keywords: Intelligent transportation system · Point cloud segmentation · Ground extraction · Point cloud density He, Y., Jin, L., Guo, B. et al.: Density-based road segmentation algorithm for point cloud collected by roadside LiDAR. Automot. Innov. 6(1), 116–130 (2023) |
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| WNEVC 2023 will be held at IAA Mobility in Munich in September | |||
 China SAE and the International Organization of the World New Energy Vehicle Congress (preparatory) will collaborate with the German Association of the Automotive Industry (VDA) to hold the 2023 World New Energy Vehicle Congress from September 6th to 7th, during the International Mobility Platform (IAA Mobility) in Munich. We are looking forward to meeting you at the 2023 WNEVC in Munich to discuss the transformation and upgrading of the automotive industry, and jointly plan for safer, greener and more sustainable future. To learn more about WNEVC 2023, please visit our website at www.wnevc.org.cn . |
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| CICV 2023 will be held at Beijing on May 15-18 | |||
 The 10th International Intelligent Connected Vehicle Technology Annual Conference (CICV 2023) is set to take place in Beijing from May 15-18, 2023. The conference will feature a range of exciting events, including two closed-door sessions, three thematic summits, 18 seminars, Robotaxi dynamic display and experience event, and a series of other engaging activities. More than 200 top experts from the industry will be invited to give speeches, and over 80 organizations will make demonstrations. The conference will attract more than 2,000 professional representatives from over 300 organizations, making it an excellent opportunity to network and learn from the best in the field of intelligent connected vehicle technology. For further details about the conference, please click here . |
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| Youth Automobile Innovation Collecting Campaign | |||
 To further promote car culture, enhance the scientific literacy of teenagers, and guide them to embrace green and environmental concepts, the “2023 Youth Automobile Innovation Collecting Campaign” has been officially launched by China SAE. This event will be held during the 2023 World New Energy Vehicle Congress. Finalists will have the opportunity to attend lectures by top-level car designers and exchange ideas with globally renowned scientists in the automotive industry. For further details, please click here to view. |
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Automotive Innovation Sponsored by China SAE and published globally via Springer Nature, Automotive Innovation aims to be a world-class journal that provides abundant sources of innovative findings for automotive engineers and scientists. The journal is published quarterly, ensuring high-quality papers satisfying international standards. With the editorial board consisting of world-renowned experts, it has attracted readers from 72 countries and regions. The highest download of a single article wins more than 32,000. The journal is indexed in Ei Compendex, ESCI, and Scopus (CiteScore=5.5). The journal provides a forum for the research of principles, methodologies, designs, theoretical background, and cutting-edge technologies in connection with the development of vehicle and mobility. The main topics cover: energy-saving, electrification, intelligent and connected, safety, and emerging vehicle technologies. Editors-in-Chief Jun Li, Academician of CAE, President of China SAE, Professor of Tsinghua University Frank Zhao, Honorary Lifetime President of FISITA, Director of Tsinghua Automotive Strategy Research Institute, Professor of Tsinghua University Honorary and Founding Executive Editor-in-Chief Prof. Fangwu (Mike) Ma Executive Associate Editor-in-Chief Prof. Xinjie Zhang, Professor of Jilin University |
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Paper submission and browse www.ChinaSAEJournal.com.cn www.springer.com/42154 Contacts: Ms. Lily Lu Tel: +86-10-50950101 Email: jai@sae-china.org 
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