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Review and Perspectives on Human Emotion for Connected Automated vehicles

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Automotive Innovation 2024年第1期7卷 4-44页

作者： Wenbo Li Guofa Li Ruichen Tan Cong Wang Zemin Sun Ying Li Gang Guo Dongpu Cao Keqiang Li School of Vehicle and Mobility Tsinghua University30 Shuangqing RdBeijing 100084China College of Mechanical and Vehicle Engineering Chongqing University174 Shazheng RdChongqing 400044China College of Computer Science and Technology Jilin University2699 Qianjin RdChangchun 130012China School of Mechanical Engineering Beijing Institute of Technology5 Zhongguancun South RdBeijing 100081China Lyles School of Civil Engineering Purdue University550 Stadium Mall DriveWest LafayetteIN 47907USA

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 inter-est 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.

关键词： intelligent vehicles intelligent cockpit Human-machine interaction Emotion recognition Emotion regulation

Driver Steering Behaviour Modelling Based on Neuromuscular Dynamics and Multi‑Task Time‑Series Transformer

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Automotive Innovation 2024年第1期7卷 45-58页

作者： Yang Xing Zhongxu Hu Xiaoyu Mo Peng Hang Shujing Li Yahui Liu Yifan Zhao Chen Lv School of Aerospace Transport and ManufacturingCranfield UniversityCranfield MK430ALUnited Kingdom School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore College of Transportation Engineering Tongji UniversityShanghai 200092China College of Computer Science and Technology Jilin UniversityChangchun 130012China School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China

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 pre-dict 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 pro-posed 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.

关键词： Driver steering behaviours Neuromuscular dynamics Multi-task learning Sequential transformer intelligent vehicles

Autonomous parking control for intelligent vehicles based on a novel algorithm

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The Journal of China Universities of Posts and Telecommunications 2017年第4期24卷 51-56页

作者： Gao Hongbo Xie Guotao Zhang Xinyu Cheng Bo State Key Laboratory of Automotive Safety and Energy Tsinghua University Beijing 100084 China Information Technology Center Tsinghua University Beijing 100084 China

Along with the increasing number of vehicles, parking space becomes narrow gradually, safety parking puts forward higher requirements on the driver＇s driving technology. How to safely, quickly and accurately park the vehiclo to parking space right？ This paper presents an automatic parking scheme based on trajectory planning, which analyzing the mechanical model oftbe vehicle, establishing vehicle steering model and parking model, coming to the conclusion that it is the turning radius is independent of the vehicle speed at low speed. The Matlab simulation environment verifies the correctness and effectiveness of the proposed algorithm for parking. A class of the automatic parking problem of intelligent vehicles is solved.

关键词： intelligent vehicles control algorithm autonomous parking

Off-road testing scenario design and library generation for intelligent vehicles

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Green Energy and intelligent Transportation 2022年第3期1卷 1-12页

作者： Yuchun Wang Jianwei Gong Boyang Wang Peng Jia Tansyou Kyo School of Mechanical Engineering Beijing Institute of TechnologyHaidian DistrictBeijing100081China Kanagawa University Rokkakubashi1-28-12-505Yokohama Kanagawa-kuKANAGAWA221-0802Japan

To realize the widespread application and continuous functional development of intelligent vehicles,test and evaluation of vehicle's functionality and Safety Performance in complex off-road scenarios are fundamental.Since traditional distance-based road tests cannot meet the evolving test requirements,a method to design the function-based off-road testing scenario library for intelligent vehicles(IV)is proposed in this paper.The testing scenario library is defined as a critical set of scenarios that can be used for IV tests.First,for the complex and diverse off-road scenarios,a hierarchical,structural model of the test scenario is built.Then,the critical test scenarios are selected adaptively according to the vehicle model to be tested.Next,those parameters representing the challenging test scenarios are selected.The selected parameters need to fit the natural distribution probability of scenarios.The critical test-scenario library is built combing these parameters with the structural model.Finally,the test scenarios that are most approximate to the natural driving scenario are determined with importance sampling theory.The test-scenario library built with this method can provide more critical test scenarios,and is widely applicable despite different vehicle models.Verified by simulation in the off-road interaction scenarios,test would be accelerated significantly with this method,about 800 times faster than testing in the natural road environment.

关键词： Off-road testing scenario design intelligent vehicles Off-road scenario model

Optimal control methods in intelligent vehicles

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Journal of Control and Decision 2017年第1期4卷 32-56页

作者： Hong Chen Lulu Guo Ting Qu Bingzhao Gao Fei Wang State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchunChina Department of Control Science and Engineering Jilin UniversityChangchunChina

In recent years,intelligent vehicles(IVs)have become a hot spot in automotive industry.Key technologies of IVs range over the field of sensing,decision-making and control.Among them,control technology provides an enabling support for improving autonomous driving safety,reducing energy consumption and carbon emission.This paper focuses on some aspects of applying advanced control methodologies in IVs through several selected examples including eco-driving and MPC-based driver modelling.

关键词： Control technology intelligent vehicles eco-driving MPC-based driver modelling

Attacks and defences on intelligent connected vehicles:a survey

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Digital Communications and Networks 2020年第4期6卷 399-421页

作者： Mahdi Dibaei Xi Zheng Kun Jiang Robert Abbas Shigang Liu Yuexin Zhang Yang Xiang Shui Yu Department of Computing Macquarie UniversitySydneyNSWAustralia China State Key Lab of Automotive Safety and Energy Tsinghua UniversityBeijingChina School of Engineering Macquarie UniversitySydneyNSWAustralia School of Software and Electrical Engineering Swinburne University of TechnologyHawthornVICAustralia School of Software University of Technology SydneyAustralia

intelligent vehicles are advancing at a fast speed with the improvement of automation and connectivity,which opens up new possibilities for different cyber-attacks,including in-vehicle attacks(e.g.,hijacking attacks)and vehicle-to-everything communicationattacks(e.g.,data theft).These problems are becoming increasingly serious with the development of 4G LTE and 5G communication technologies.Although many efforts are made to improve the resilience to cyber attacks,there are still many unsolved challenges.This paper first identifies some major security attacks on intelligent connected vehicles.Then,we investigate and summarize the available defences against these attacks and classify them into four categories:cryptography,network security,software vulnerability detection,and malware detection.Remaining challenges and future directions for preventing attacks on intelligent vehicle systems have been discussed as well.

关键词： intelligent vehicles Vehicular networks Software vulnerabilities Deep learning 3GPP Software defined security

Research on Optimizing User Requirement of intelligent vehicles Based on Scenario Analysis

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Research on Optimizing User Requirement of Intelligent Vehic...

A Novel Prediction Method of Optimal Driving Speed for intelligent vehicles in Urban Traffic Scenarios

作者： Min Zhang Wenzhe Li School of Mechanical and Vehicle Engineering Hunan University

Aiming to the inaccurate definition of user requirement during the preliminary development stage of vehicle, a scenario-oriented model to evaluate vehicle intelligent function is proposed in this paper. The proposed model use scenario analysis to describe the driving status, demand tasks and response measures. KANO model is applied to screen user’s requirements. In the process of requirements evaluation, the importance of each function is determined by rough set theory. The ambiguity and subjectivity of user’s requirements is considered in proposed model, therfore, the accuracy of evaluation results is improved. Furthermore, a typical commute scenario is presented as an example to verify the feasibility of the model.

关键词： intelligent vehicles Scenario Analysis Optimizing User Requirement

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A Novel Prediction Method of Optimal Driving Speed for Intel...

A Real-time Motion Planner for intelligent vehicles Based on A Fast SSTG Method

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作者： Yeqing Zhang Mailing Wang Tong Liu School of Automation Beijing Institute of Technology

Due to sensitively reflect traffic conditions in road network, driving speed is a vital index to evaluate the capability of urban traffic network and intelligent transportation system. The research on optimal driving speed is beneficial for improving the stability and safety of intelligent vehicles in urban traffic scenarios. This paper puts forward a novel prediction method of optimal driving speed for urban intelligent vehicles based on road design principles and traffic flow theories. Firstly, driving factors of urban traffic scenarios are selected, quantized and stored in the optimal-driving geographic information database established for intelligent vehicles. Secondly, multivariate linear equations are investigated using regression analysis methods to reveal the relationship between driving speed of intelligent vehicles and related factors, including urban road parameters, real-time traffic conditions and vehicle information. Thirdly, urban traffic variable-length model is built to explore dynamic characteristics of traffic evolution, further macroscopic constraint equations of driving speed for intelligent vehicles are deduced based on traffic fundamental diagram. Finally, regarding instant travel time, total travel time and total travel distance as evaluation metrics, the optimal solution of the multivariate linear equation and macroscopic constraint equations is calculated ultimately, which is the optimal driving speed for intelligent vehicles in the urban traffic network. Simulation results have proved that the proposed prediction method can provide safe, feasible and efficient driving speed advice for intelligent vehicles in urban traffic scenarios.

关键词： Multivariate Linear Regression Variable Length Model Traffic Fundamental Diagram Geographic Information System intelligent vehicles

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A Real-time Motion Planner for Intelligent Vehicles Based on...