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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 December includes the following contents： 1. Article Recommendation——Three papers on Autonomous Vehicles 2. China SAE News: · ISC 2023 Call for Papers |
  
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Hybrid Adaptive Event-Triggered Platoon Control with Package Dropout Jiawei Wang, Fangwu Ma, Liang Wu, Guanpu Wu |
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| A novel hybrid adaptive event-triggered platoon control strategy is proposed to achieve the balanced coordination between communication resource utilization and vehicle-following performance considering the effect of package dropout. To deal with the disturbance caused by the event-triggered scheme, the parameter space approach is adopted to derive the feasible region from which cooperative adaptive cruise control controller satisfies internal stability, distance accuracy, and string stability. Subsequently, the Bernoulli random distribution process is employed to depict the phenomenon of package dropout, and the hybrid coefficient is proposed to realize the allocation between the adaptive trigger threshold strategy and the adaptive headway strategy. The simulation of a six-vehicle platoon is carried out to verify the effectiveness of the designed control strategy. Results show that about 78.76% of communication resources have been saved by applying the event-triggered scheme, while guaranteeing the desired vehicle-following performance. And in the non-ideal communication environment with frequent package dropouts, the hybrid adaptive strategy achieves the coordination among communication resource utilization, string stability margin, distance accuracy, and traffic efficiency. | |||
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Keywords: Cooperative adaptive cruise control · Event-triggered scheme · Hybrid adaptive strategy · Package dropout · Parameter space approach Wang, J., Ma, F., Wu, L. et al.: Hybrid adaptive event-triggered platoon control with package dropout. Automot. Innov. 5(4), 347-358 (2022) |
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Robust Identification of Road Surface Condition Based on Ego-Vehicle Trajectory Reckoning Cheng Tian, Bo Leng, Xinchen Hou, Yuyao Huang, Wenrui Zhao, Da Jin, Lu Xiong, Junqiao Zhao |
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| The type of road surface condition (RSC) will directly affect the driving performance of vehicles. Monitoring the type of RSC is essential for both transportation agencies and individual drivers. However, most existing methods are solely based on a dynamics-based method or an image-based method, which is susceptible to road excitation limitations and interference from the external environment. Therefore, this paper proposes a decision-level fusion identification framework of the RSC based on ego-vehicle trajectory reckoning to accurately obtain the type of RSC that the front wheels of the vehicle will experience. First, a road feature extraction model based on multi-task learning is conducted, which can simultaneously segment the drivable area and road cast shadow. Second, the optimized candidate regions of interest are classified with confidence levels by ShuffleNet. Considering environmental interference, candidate regions of interest regarded as virtual sensors are fused by improved Dempster-Shafer evidence theory to obtain the fusion results. Finally, the ego-vehicle trajectory reckoning module based on the kinematic bicycle model is added to the proposed fusion method to extract the RSC experienced by the front wheels. The performance of the entire framework is verified on a specific dataset with shadow and split curve roads. The results reveal that the proposed method can identify the RSC with accurate predictions in real time. | |||
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Keywords: Road surface identification · Ego-Vehicle trajectory reckoning · Multi-task learning · Dempster-Shafer evidence theory · Autonomous vehicle
Tian, C., Leng, B., Hou, X. et al.: Robust identification of road surface condition based on ego-vehicle trajectory reckoning. Automot. Innov. 5(4), 376-387 (2022) |
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Approximate Optimal Filter Design for Vehicle System through Actor-Critic Reinforcement Learning Yuming Yin, Shengbo Eben Li, Kaiming Tang, Wenhan Cao, Wei Wu, Hongbo Li |
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| Precise state and parameter estimations are essential for identification, analysis and control of vehicle engineering problems, especially under significant model and measurement uncertainties. The widely used filtering/estimation algorithms, such as Kalman series like Kalman filter, extended Kalman filter, unscented Kalman filter, and particle filter, generally aim to approach the true state/parameter distribution via iteratively updating the filter gain at each time step. However, the optimality of these filters would be deteriorated by unrealistic initial condition or significant model error. Alternatively, this paper proposes to approximate the optimal filter gain by considering the effect factors within infinite time horizon, on the basis of estimation-control duality. The proposed approximate optimal filter (AOF) problem is designed and subsequently solved by actor-critic reinforcement learning (RL) method. The AOF design transforms the traditional optimal filtering problem with the minimum expected mean square error into an optimal control problem with the minimum accumulated estimation error, in which the estimation error is used as the surrogate system state and the infinite-horizon filter gain is the control input. The estimation-control duality is proved to hold when certain conditions about initial vehicle state distributions and policy structure are maintained. In order to evaluate of the effectiveness of AOF, a vehicle state estimation problem is then demonstrated and compared with the steady-state Kalman filter. The results showed that the obtained filter policy via RL with different discount factors can converge to theoretical optimal gain with an error within 5%, and the average estimation errors of vehicle slip angle and yaw rate are less than 1.5×10–4. | |||
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Keywords: Vehicle state estimation · Kalman filter · Estimation-control duality · Reinforcement learning Yin, Y., Li, S.E., Tang, K. et al.: Approximate optimal filter design for vehicle system through Actor-Critic Reinforcement Learning. Automot. Innov. 5(4), 415-426 (2022) |
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| ISC 2023 Call for Papers | |||
 China SAE and FISITA are delighted to confirm that the fifth FISITA Intelligent Safety Conference (ISC 2023) will take place summer 2023, in Chongqing, China, with physical and online participation available to registered participants. ISC 2023 will again see an international speaker line up considering some of the most important topics within the safety of future mobility arena, including SOTIF, Cybersecurity, Human Factors, AI, and Intelligent Safety Protection. The ISC Committee invites you to submit a technical paper for ISC 2023. Paper submission deadline : March 30, 2023 Submission website www.fisita.com/isc . |
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| FISITA Technology of Mobility Conference & Exhibition 2023 to be held on 12-15 Sept. 2023 in Barcelona, Spain | |||
 FISITA is launching its first Technology of Mobility Conference and Exhibition, the largest gathering of automotive and mobility engineers in the world, in Barcelona on the 12-15 September 2023. The four-day event will include the 39th FISITA World Congress; the annual FISITA World Mobility Summit; EuroBrake, the world’s largest gathering of braking specialists; and the inaugural European Intelligent Safety Conference. Throughout the four days, FISITA’s working groups will deliver updates on their latest specialist areas which include: carbon neutral mobility, digitalisation, industry disruption, intelligent safety, international supply chains and next generation mobility. For information of the event and how to become a partner or exhibitor click here . |
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| Automotive Innovation launched Directory of AUIN Published Papers | |||
 Automotive Innovation launched the Directory of AUIN Published Papers. It offers access to locate all papers published on Automotive Innovation. All articles published in the journal since 2018 are listed according to the technical field. Please click here to download the file. |
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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 29,000. The journal is indexed in Ei Compendex, ESCI, and Scopus (CiteScore=5.0). 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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