scholarly journals Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4289 ◽  
Author(s):  
Jin ◽  
Yin ◽  
Chen
Keyword(s):  
Control Systems ◽  
Steering System ◽  
System Dynamic ◽  
Dynamic State ◽  
Future Research ◽  
Active Safety ◽  
Automated Driving ◽  
Estimation Techniques ◽  
Stability Performance ◽  
Vehicle System

In order to improve handling stability performance and active safety of a ground vehicle, a large number of advanced vehicle dynamics control systems—such as the direct yaw control system and active front steering system, and in particular the advanced driver assistance systems—towards connected and automated driving vehicles have recently been developed and applied. However, the practical effects and potential performance of vehicle active safety dynamics control systems heavily depend on real-time knowledge of fundamental vehicle state information, which is difficult to measure directly in a standard car because of both technical and economic reasons. This paper presents a comprehensive technical survey of the development and recent research advances in vehicle system dynamic state estimation. Different aspects of estimation strategies and methodologies in recent literature are classified into two main categories—the model-based estimation approach and the data-driven-based estimation approach. Each category is further divided into several sub-categories from the perspectives of estimation-oriented vehicle models, estimations, sensor configurations, and involved estimation techniques. The principal features of the most popular methodologies are summarized, and the pros and cons of these methodologies are also highlighted and discussed. Finally, future research directions in this field are provided.

Special Issue on Intelligent Vehicle Technologies for Safety and Active Life

2015 ◽  
Vol 27 (6) ◽  
pp. 609-609
Author(s):  
Hidehisa Yoshida ◽  
Manabu Omae ◽  
Takahiro Wada
Keyword(s):  
Social Acceptance ◽  
New Technologies ◽  
Active Safety ◽  
Special Issue ◽  
Automated Driving ◽  
Cooperative Driving ◽  
Technical Developments ◽  
Vehicle System ◽  
The Social ◽  
Vehicle Technologies

Intelligent automobiles equipped with active safety methods whose objective is to reduce accidents caused by human problems such as careless driving and loss of consciousness, have got the social acceptance widely which are recognized by introducing collision avoidance brakes to the market. Active safety and automated driving technologies of intelligent automobiles were featured at TokyoMotor Show 2015. Their displays attracted much attention.It is necessary to propose new technologies continuously by the technical subject of active safety and automated driving, for example, relation of cooperative driving by a vehicle system and a driver, from the restrictions of originality of technical developments and the liability issues at the time of an accident.Even so, users worldwide expect much from the dynamic future of transportation for safety and active life.This special issue features seven papers carefully reviewed by field specialists.I would like to express my heartfelt appreciation to the authors who have contributed their valuable research results to this special issue and to the reviewers who provided their invaluable expertise.I would also like to thank members of the Journal of Robotics and Mechatronics board for giving me the unique opportunity to plan and coordinate this issue.

scholarly journals Concept of an Ontology for Automated Vehicle Behavior in the Context of Human-Centered Research on Automated Driving Styles

Information ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 21
Author(s):  
Johannes Ossig ◽  
Stephanie Cramer ◽  
Klaus Bengler
Keyword(s):  
Driving Behavior ◽  
Automation System ◽  
Automated Driving ◽  
Driving Style ◽  
Driving Experience ◽  
Automated Vehicle ◽  
Future Design ◽  
Vehicle System ◽  
The Future ◽  
Conceptual Distinction

In the human-centered research on automated driving, it is common practice to describe the vehicle behavior by means of terms and definitions related to non-automated driving. However, some of these definitions are not suitable for this purpose. This paper presents an ontology for automated vehicle behavior which takes into account a large number of existing definitions and previous studies. This ontology is characterized by an applicability for various levels of automated driving and a clear conceptual distinction between characteristics of vehicle occupants, the automation system, and the conventional characteristics of a vehicle. In this context, the terms ‘driveability’, ‘driving behavior’, ‘driving experience’, and especially ‘driving style’, which are commonly associated with non-automated driving, play an important role. In order to clarify the relationships between these terms, the ontology is integrated into a driver-vehicle system. Finally, the ontology developed here is used to derive recommendations for the future design of automated driving styles and in general for further human-centered research on automated driving.

First encounter effects in testing of highly automated vehicles during two experimental occasions – The need for recurrent testing

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jonas Andersson ◽  
Azra Habibovic ◽  
Daban Rizgary
Keyword(s):  
Driver Behavior ◽  
Added Value ◽  
Future Research ◽  
Gaze Behavior ◽  
Repeated Testing ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Perceived Safety ◽  
Repeated Interaction ◽  
Over Time

Abstract To explore driver behavior in highly automated vehicles (HAVs), independent researchers are mainly conducting short experiments. This limits the ability to explore drivers’ behavioral changes over time, which is crucial when research has the intention to reveal human behavior beyond the first-time use. The current paper shows the methodological importance of repeated testing in experience and behavior related studies of HAVs. The study combined quantitative and qualitative data to capture effects of repeated interaction between drivers and HAVs. Each driver ( n = 8 n=8 ) participated in the experiment on two different occasions (∼90 minutes) with one-week interval. On both occasions, the drivers traveled approximately 40 km on a rural road at AstaZero proving grounds in Sweden and encountered various traffic situations. The participants could use automated driving (SAE level 4) or choose to drive manually. Examples of data collected include gaze behavior, perceived safety, as well as interviews and questionnaires capturing general impressions, trust and acceptance. The analysis shows that habituation effects were attenuated over time. The drivers went from being exhilarated on the first occasion, to a more neutral behavior on the second occasion. Furthermore, there were smaller variations in drivers’ self-assessed perceived safety on the second occasion, and drivers were faster to engage in non-driving related activities and become relaxed (e. g., they spent more time glancing off road and could focus more on non-driving related activities such as reading). These findings suggest that exposing drivers to HAVs on two (or more) successive occasions may provide more informative and realistic insights into driver behavior and experience as compared to only one occasion. Repeating an experiment on several occasions is of course a balance between the cost and added value, and future research should investigate in more detail which studies need to be repeated on several occasions and to what extent.

Myoelectric Control of Robotic Leg Prostheses and Exoskeletons: A Review

2021 ◽  
Author(s):  
Ali Nasr ◽  
Brokoslaw Laschowski ◽  
John McPhee
Keyword(s):  
Control Systems ◽  
Myoelectric Control ◽  
Machine Learning Algorithms ◽  
Torque Control ◽  
Future Research ◽  
Reactive Control ◽  
Rhythmic Movements ◽  
Myoelectric Signals ◽  
Joint Torques ◽  
Robotic Leg

Abstract Myoelectric signals from the human motor control system can improve the real-time control and neural-machine interface of robotic leg prostheses and exoskeletons for different locomotor activities (e.g., walking, sitting down, stair ascent, and non-rhythmic movements). Here we review the latest advances in myoelectric control designs and propose future directions for research and innovation. We review the different wearable sensor technologies, actuators, signal processing, and pattern recognition algorithms used for myoelectric locomotor control and intent recognition, with an emphasis on the hierarchical architectures of volitional control systems. Common mechanisms within the control architecture include 1) open-loop proportional control with fixed gains, 2) active-reactive control, 3) joint mechanical impedance control, 4) manual-tuning torque control, 5) adaptive control with varying gains, and 6) closed-loop servo actuator control. Based on our review, we recommend that future research consider using musculoskeletal modeling and machine learning algorithms to map myoelectric signals from surface electromyography (EMG) to actuator joint torques, thereby improving the automation and efficiency of next-generation EMG controllers and neural interfaces for robotic leg prostheses and exoskeletons. We also propose an example model-based adaptive impedance EMG controller including muscle and multibody system dynamics. Ongoing advances in the engineering design of myoelectric control systems have implications for both locomotor assistance and rehabilitation.

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