|
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 February includes the following contents： 1. Article Recommendation——Four papers on Autonomous Vehicles 2. China SAE News: · Automotive Innovation launched Directory of AUIN Published Papers |
  
|
Review and Perspectives on Human Emotion for Connected Automated Vehicles Wenbo Li, Guofa Li, Ruichen Tan, Cong Wang, Zemin Sun, Ying Li, Gang Guo, Dongpu Cao, Keqiang Li |
||
| The progression toward automated driving and the latest advancement in vehicular networking have led to novel and natural human-vehicle-road systems, in which affective human-vehicle interaction is a crucial factor affecting the acceptance, safety, comfort, and traffic efficiency of connected and automated vehicles (CAVs). This development has inspired increasing interest in how to develop affective interaction framework for intelligent cockpit in CAVs. To enable affective human-vehicle interactions in CAVs, knowledge from multiple research areas is needed, including automotive engineering, transportation engineering, human–machine interaction, computer science, communication, as well as industrial engineering. However, there is currently no systematic survey considering the close relationship between human-vehicle-road and human emotion in the human-vehicle-road coupling process in the CAV context. To facilitate progress in this area, this paper provides a comprehensive literature survey on emotion-related studies from multi-aspects for better design of affective interaction in intelligent cockpit for CAVs. This paper discusses the multimodal expression of human emotions, investigates the human emotion experiment in driving, and particularly emphasizes previous knowledge on human emotion detection, regulation, as well as their applications in CAVs. The promising research perspectives are outlined for researchers and engineers from different research areas to develop CAVs with better acceptance, safety, comfort, and enjoyment for users. | |||
|
Keywords: Intelligent vehicles, Intelligent cockpit, Human-machine interaction, Emotion recognition, Emotion regulation Li, W., Li, G., Tan, R. et al.: Review and Perspectives on Human Emotion for Connected Automated Vehicles. Automot. Innov. 7, 4–44 (2024) |
|||
| Full Paper Reading>> | |||
|
Driver Steering Behaviour Modelling Based on Neuromuscular Dynamics and Multi-Task Time-Series Transformer Yang Xing, Zhongxu Hu, Xiaoyu Mo, Peng Hang, Shujing Li, Yahui Liu, Yifan Zhao, Chen Lv |
||
| Driver steering intention prediction provides an augmented solution to the design of an onboard collaboration mechanism between human driver and intelligent vehicle. In this study, a multi-task sequential learning framework is developed to predict future steering torques and steering postures based on upper limb neuromuscular electromyography signals. The joint representation learning for driving postures and steering intention provides an in-depth understanding and accurate modelling of driving steering behaviours. Regarding different testing scenarios, two driving modes, namely, both-hand and single-right-hand modes, are studied. For each driving mode, three different driving postures are further evaluated. Next, a multi-task time-series transformer network (MTS-Trans) is developed to predict the future steering torques and driving postures based on the multi-variate sequential input and the self-attention mechanism. To evaluate the multi-task learning performance and information-sharing characteristics within the network, four distinct two-branch network architectures are evaluated. Empirical validation is conducted through a driving simulator-based experiment, encompassing 21 participants. The proposed model achieves accurate prediction results on future steering torque prediction as well as driving posture recognition for both two-hand and single-hand driving modes. These findings hold significant promise for the advancement of driver steering assistance systems, fostering mutual comprehension and synergy between human drivers and intelligent vehicles. | |||
|
Keywords: Driver steering behaviours, Neuromuscular dynamics, Multi-task learning, Sequential transformer, Intelligent vehicles
Xing, Y., Hu, Z., Mo, X. et al.: Driver Steering Behaviour Modelling Based on Neuromuscular Dynamics and Multi-Task Time-Series Transformer. Automot. Innov. 7, 45–58 (2024) |
|||
| Full Paper Reading>> | |||
|
Curve Trajectory Model for Human Preferred Path Planning of Automated Vehicles Gergo Ferenc Igneczi, Erno Horvath, Roland Toth, Krisztian Nyilas |
||
| Automated driving systems are often used for lane keeping tasks. By these systems, a local path is planned ahead of the vehicle. However, these paths are often found unnatural by human drivers. In response to this, this paper proposes a linear driver model, which can calculate node points reflective of human driver preferences and based on these node points a human driver preferred motion path can be designed for autonomous driving. The model input is the road curvature, effectively harnessed through a self-developed Euler-curve-based curve fitting algorithm. A comprehensive case study is undertaken to empirically validate the efficacy of the proposed model, demonstrating its capacity to emulate the average behavioral patterns observed in human curve path selection. Statistical analyses further underscore the model's robustness, affirming the authenticity of the established relationships. This paradigm shift in trajectory planning holds promising implications for the seamless integration of autonomous driving systems with human driving preferences. | |||
|
Keywords: Naturalistic driving, Identification, Driver models, Path planning Igneczi, G.F., Horvath, E., Toth, R. et al.: Curve Trajectory Model for Human Preferred Path Planning of Automated Vehicles. Automot. Innov. 7, 59–70 (2024) |
|||
| Full Paper Reading>> | |||
|
A Hierarchical LSTM-Based Vehicle Trajectory Prediction Method Considering Interaction Information Haitao Min, Xiaoyong Xiong, Pengyu Wang, Zhaopu Zhang |
||
| Trajectory prediction is an essential component in autonomous driving systems, as it can forecast the future movements of surrounding vehicles, thereby enhancing the decision-making and planning capabilities of autonomous driving systems. Traditional models relying on constant acceleration and constant velocity often experience a reduction in prediction accuracy as the forecasted timeframe extends. This limitation makes it challenging to meet the demands for medium to long-term trajectory prediction. Conversely, data-driven models, particularly those based on Long Short-Term Memory (LSTM) neural networks, have demonstrated superior performance in medium to long-term trajectory prediction. Therefore, this study introduces a hierarchical LSTM-based method for vehicle trajectory prediction. Considering the difficulty of using a single LSTM model to predict trajectories for all driving intentions, the trajectory prediction task is decomposed into three sequential steps: driving intention prediction, lane change time prediction, and trajectory prediction. Furthermore, given that the driving intent and trajectory of a vehicle are always subject to the influence of the surrounding traffic flow, the predictive model proposed in this paper incorporates the interactional information of neighboring vehicle movements into the model input. The proposed method is trained and validated on the real vehicle trajectory dataset Next Generation Simulation. The results show that the proposed hierarchical LSTM method has a lower prediction error compared to the integral LSTM model. | |||
|
Keywords: Autonomous vehicles, Trajectory prediction, Long Short-Term Memory, Driving intention prediction Min, H., Xiong, X., Wang, P. et al.: A Hierarchical LSTM-Based Vehicle Trajectory Prediction Method Considering Interaction Information. Automot. Innov. 7, 71–81 (2024) |
|||
| Full Paper Reading>> | |||
| 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 during 2023, are listed according to the technical field. Please click Autonomous Driving, Battery & Energy Management, Automotive Lightweight, to access to the articles in the different technical field. |
|||
| Preview of the key issues about China SAE in 2024 | |||
 China SAE released plans for important research and significant annual events for 2024. These include research reports on technology roadmap, science and technology innovation strategy, carbon neutral collaborative innovation platform for commercial vehicles, industrial policy research and international strategy analysis, as well as the expected time and location of important conferences throughout the year. For more detailed information please click here . |
|||
| Notice on the third Power Battery Technology Innovation Challenge | |||
 The China SAE is scheduled to hold the third Power Battery Technology Innovation Challenge in 2024. This competition will further optimize and expand the track design, rule requirements, and participants on the basis of the previous two sessions. It will also highlight the investigation of key technologies of power batteries and the selection of innovative teams. For more detailed 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 32,000. The journal is indexed in Ei Compendex, ESCI, and Scopus (IF2022=6.1). 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 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
|
|