|
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 September includes the following contents： 1. Article Recommendation——Four papers on Autonomous Vehicles 2. China SAE News: · Call for Papers: Feature Topic on Human Driver Behaviours for Intelligent Vehicles |
  
|
A Review of Testing Object-Based Environment Perception for Safe Automated Driving Michael Hoss, Maike Scholtes, Lutz Eckstein |
||
| Safety assurance of automated driving systems must consider uncertain environment perception. This paper reviews literature addressing how perception testing is realized as part of safety assurance. The paper focuses on testing for verification and validation purposes at the interface between perception and planning, and structures the analysis along the three axes (1) test criteria and metrics, (2) test scenarios, and (3) reference data. Furthermore, the analyzed literature includes related safety standards, safety-independent perception algorithm benchmarking, and sensor modeling. It is found that the realization of safety-oriented perception testing remains an open issue since challenges concerning the three testing axes and their interdependencies currently do not appear to be sufficiently solved. | |||
|
Keywords: Automated driving · Environment perception · Safety assurance · Testing Hoss, M., Scholtes, M. & Eckstein, L.: A Review of Testing Object-Based Environment Perception for Safe Automated Driving. Automot. Innov. 5, 223–250 (2022) |
|||
| Full Paper Reading>> | |||
|
RGB Image- and Lidar-Based 3D Object Detection Under Multiple Lighting Scenarios Wentao Chen, Wei Tian, Xiang Xie & Wilhelm Stork |
||
| In recent years, camera- and lidar-based 3D object detection has achieved great progress. However, the related researches mainly focus on normal illumination conditions; the performance of their 3D detection algorithms will decrease under low lighting scenarios such as in the night. This work attempts to improve the fusion strategies on 3D vehicle detection accuracy in multiple lighting conditions. First, distance and uncertainty information is incorporated to guide the “painting” of semantic information onto point cloud during the data preprocessing. Moreover, a multitask framework is designed, which incorporates uncertainty learning to improve detection accuracy under low-illumination scenarios. In the validation on KITTI and Dark-KITTI benchmark, the proposed method increases the vehicle detection accuracy on the KITTI benchmark by 1.35% and the generality of the model is validated on the proposed Dark-KITTI dataset, with a gain of 0.64% for vehicle detection. | |||
|
Keywords: 3D object detection · Multi-sensor fusion · Uncertainty estimation · Semantic segmentation · PointPainting
Chen, W., Tian, W., Xie, X., et al. RGB Image- and Lidar-Based 3D Object Detection Under Multiple Lighting Scenarios. Automot. Innov. 5(3), 251–259 (2022) |
|||
| Full Paper Reading>> | |||
|
PTMOT: A Probabilistic Multiple Object Tracker Enhanced by Tracklet Confidence for Autonomous Driving Kun Jiang, Yining Shi, Taohua Zhou, Mengmeng Yang & Diange Yang |
||
| Real driving scenarios, due to occlusions and disturbances, provide disordered and noisy measurements, which makes the task of multi-object tracking quite challenging. Conventional approach is to find deterministic data association; however, it has unstable performance in high clutter density. This paper proposes a novel probabilistic tracklet-enhanced multiple object tracker (PTMOT), which integrates Poisson multi-Bernoulli mixture (PMBM) filter with confidence of tracklets. The proposed method is able to realize efficient and robust probabilistic association for 3D multi-object tracking (MOT) and improve the PMBM filter’s continuity by smoothing single target hypothesis with global hypothesis. It consists of two key parts. First, the PMBM tracker based on sets of tracklets is implemented to realize probabilistic fusion of disordered measurements. Second, the confidence of tracklets is smoothed through a smoothing-while-filtering approach. Extensive MOT tests on nuScenes tracking dataset demonstrate that the proposed method achieves superior performance in different modalities. | |||
|
Keywords: 3D multi-object tracking · Random finite set · Probabilistic association · Tracklet confidence smoothing Jiang, K., Shi, Y., Zhou, T. et al. PTMOT: A probabilistic multiple object tracker enhanced by tracklet confidence for autonomous driving. Automot. Innov. 5(3), 260–271 (2022) |
|||
| Full Paper Reading>> | |||
|
Review of In-Vehicle Optical Fiber Communication Technology Wenwei Wang, Shiyao Yu, Wanke Cao & Kaidi Guo |
||
| With the continuous development of automotive intelligent networking and autonomous driving technologies, the number of in-vehicle electronic systems and applications is increasing rapidly. This change increases the amount of data to be transmitted in the vehicle and puts forward further requirements of higher speed and safety for in-vehicle communication. Traditional vehicle bus technologies are no longer sufficient to meet today’s high-speed transmission requirements, in which copper cables are used extensively, resulting in serious electromagnetic interference (EMI). Vehicle optical fiber communication technology, besides greatly improving the data transmission rate, has the advantages of anti-EMI, reducing cable space and vehicle mass. This paper first presents the motivation of applying vehicle optical fiber communication technology and reviews the development history of vehicle optical fiber communication technology. Then, the paper researches the development trend of automotive electrical and electronic architecture (EEA), from distributed EEA to domain centralized EEA and zone-oriented EEA. Based on the discussion of the development trend of automotive EEA, an EEA based on vehicle optical fiber communication technology is proposed. Finally, the key points and future directions of vehicle optical fiber communication technology research are highlighted, including vehicle multi-mode optical fiber technology, vehicle optical fiber network protocol, and topology. | |||
|
Keywords: In-vehicle network · Optical fiber communication · Multi-mode optical fiber · Electrical and electronic architecture Wang, W., Yu, S., Cao, W. et al.: Review of In-Vehicle Optical Fiber Communication Technology. Automotive Innovation 5(3), 272–284 (2022) |
|||
| Full Paper Reading>> | |||
| Call for Papers: Feature Topic on Human Driver Behaviours for Intelligent Vehicles | |||
 Guest Editors-in-Chief: · Dr. Dongpu Cao, Professor, Tsinghua University, China · Dr. Argyrios Zolotas, Professor, Cranfield University, UK · Dr. Meng Wang, Professor, Technische Universität Dresden, Germany · Dr. Mohammad Pirani, Research Assistant Professor, University of Waterloo, Canada · Dr. Wenbo Li, Postdoctoral Research Fellow, Tsinghua University, China Topics: · Computational driver behaviour modelling · Abnormal driver behaviour detection · Driver-automation collaboration · Cognitive and affective computing · Driver skill learning and behaviour adaptation · Cognitive intelligence and driver social behaviours Submission deadline: May 31, 2023 Submit at: www.springer.com/42154 To download call for paper, please click here . |
|||
| Automotive Innovation Workshop on Oct.14 | |||
 Automotive Innovation Workshop will be held at 20:00-21:40 CST, on Oct. 14, 2022. The event is organized by China SAE and Automotive Innovation, and co-organized by National Engineering Research Center of Electric Vehicles. The workshop will be live-streamed on the official Wechat platform of China SAE. We invited the Associate Editor-in-Chief of Automotive Innovation, Dr. Basilio Lenzo from University of Padova and Dr. Yan Chen from Arizona State University to discuss “Vehicle Dynamics Control for Automated Vehicles”. To join the online session please visit https://zoom.us/ and enter the following information: Zoom room ID: 849 8540 5890 Zoom code: 123456 |
|||
| SAECCE 2022 registration starts | |||
 SAECCE 2022 will be held on Nov. 22-24, 2022 in Shanghai, China. With the theme of "Automobile Plus, Collaborative Innovation", SAECCE 2022 will focus on Electrification, Intelligence, Connected and Sharing, and discuss how to quickly promote technological innovation and reshape the new industrial structure. SAECCE 2022 is expected to assemble 500+ technical reports from industry leaders, executives and experts, 70+ technical sessions on hot topics, 100+ exhibitors, and 3500 professional participants. Registration : https://sae.corpit.com.cn/SAEMeeting/ Submission website: http://www.saecce.org.cn |
|||
| Launch of “ICV Laws and Regulations White Paper” | |||
 On September 17, 2022, the Secretary General of China Industry Innovation Alliance for the Intelligent and Connected Vehicles, Gong Weijie, officially released the "ICV Laws and Regulations White Paper". The commercial application of intelligent and connected vehicles (ICVs) remains a “zero to one” challenge. To solve this problem, the white paper identifies the challenges of China's laws and regulations for ICVs from the four major areas of road testing, product management, road traffic management, and basic support. And through the analysis and comparative study of global legislation, the white paper provides references for the improvement of China's legal and regulatory system in the field of ICVs. In addition, the white paper also analyzes in detail the progress of China’s innovative exploration of laws and regulations. For more information, please click here. |
|||
|
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 22,000. The journal is indexed in Ei Compendex, ESCI, and Scopus (CiteScore=3.6). 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 |
||||
|
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 
|
|
|||
Sponsored by
|
Published by
|
|||