scholarly journals Safety model construction for a complex automatic transportation system

Dependability ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 31-37
Author(s):  
А. V. Ozerov ◽  
А. М. Olshansky
Keyword(s):  
Machine Learning ◽  
Systems Approach ◽  
Process Analysis ◽  
Hazard Analysis ◽  
Autonomous Systems ◽  
Safety Evaluation ◽  
Transportation Systems ◽  
Control Loops ◽  
Transport Control ◽  
Local Risk

The Aim of the paper is to consider approaches to the analysis of a safety model of complex multi-loop transportation systems comprising not completely supervised subsystems. Method. For the description of a safety model, the paper uses systems theoretic process analysis (STPA) methods and the principles specified in ISO/PAS 21448:2019 (SOTIF). Result. The paper shows drawbacks of the FTA and FMEA local risk analysis methods and demonstrates a demand for some universal approach based on the combination of STPA and control theory. It gives an overview of the major stages of such analysis for the safety model of complex transportation systems exemplified by the Moscow Central Circle, which provide a feedback for safety evaluation of a transport control system under development. The paper analyzes the feasibility of using a virtual model for control purposes in the form of a so-called “supervised artificial neural network”.Conclusion. Today, railways are actively testing autonomous systems (with no driver onboard) that apply as their subsystems automatic perception modules using machine learning. The introduction of the latter into the control loop complicates the task of hazard analysis and safety evaluation of such systems using conventional FTA and FMEA methods. The construction of a safety model of such complex multi-loop transportation systems comprising not completely supervised subsystems that use machine learning methods with not completely predictable behavior requires the application of a systems approach to the analysis of unsafe scenarios along with the compilation of a scenario library and the formalization of a hazard model’s description, pertaining to the boundaries of various control loops as well, in order to reduce the regions of unknown unsafe scenarios for autonomous transportation systems under development.

Hazard Analysis of Action Loops for Automated Vehicle Remote Operation

2021 ◽  
Vol 65 (1) ◽  
pp. 732-736
Author(s):  
Amudha V. Kamaraj ◽  
Joshua E. Domeyer ◽  
John D. Lee
Keyword(s):  
Human Factors ◽  
Situation Awareness ◽  
Process Analysis ◽  
Hazard Analysis ◽  
System Level ◽  
Automated Vehicles ◽  
Control Loops ◽  
Remote Operation ◽  
Multiple Vehicles ◽  
Human Operators

One way to compensate for the limitations of automated vehicles is to use a remote operator as a fallback controller. Indeed, this has been proposed for fleet management and intermittent vehicle control. However, existing remote operation applications have demonstrated control challenges, such as latency and bandwidth, that inhibit the effectiveness of human operators. Additionally, human factors challenges arising due to the roles of multiple remote operators managing multiple vehicles further complicates these interventions. This paper uses the Systems Theoretic Process Analysis hazard analysis technique to identify system-level issues related to the remote operation of automated vehicles. Human factors challenges are identified through the lens of two control loops that link remote drivers, dispatchers, and vehicle automation. These control loops reveal familiar challenges, such as situation awareness and mental model mismatches, as well as novel challenges, such as poorly synchronized and misaligned control.

scholarly journals Cybersafety Approach to Cybersecurity Analysis and Mitigation for Mobility-as-a-Service and Internet of Vehicles

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1220
Author(s):  
Chee Wei Lee ◽  
Stuart Madnick
Keyword(s):  
Autonomous Vehicles ◽  
Process Analysis ◽  
Hazard Analysis ◽  
Urban Mobility ◽  
Internet Of Vehicles ◽  
On Demand ◽  
Software Updates ◽  
Levels Of Automation ◽  
New Generation ◽  
Convergence Of Technologies

Urban mobility is in the midst of a revolution, driven by the convergence of technologies such as artificial intelligence, on-demand ride services, and Internet-connected and self-driving vehicles. Technological advancements often lead to new hazards. Coupled with the increased levels of automation and connectivity in the new generation of autonomous vehicles, cybersecurity is emerging as a key threat affecting these vehicles. Traditional hazard analysis methods treat safety and security in isolation and are limited in their ability to account for interactions among organizational, sociotechnical, human, and technical components. In response to these challenges, the cybersafety method, based on System Theoretic Process Analysis (STPA and STPA-Sec), was developed to meet the growing need to holistically analyze complex sociotechnical systems. We applied cybersafety to coanalyze safety and security hazards, as well as identify mitigation requirements. The results were compared with another promising method known as Combined Harm Analysis of Safety and Security for Information Systems (CHASSIS). Both methods were applied to the Mobility-as-a-Service (MaaS) and Internet of Vehicles (IoV) use cases, focusing on over-the-air software updates feature. Overall, cybersafety identified additional hazards and more effective requirements compared to CHASSIS. In particular, cybersafety demonstrated the ability to identify hazards due to unsafe/unsecure interactions among sociotechnical components. This research also suggested using CHASSIS methods for information lifecycle analysis to complement and generate additional considerations for cybersafety. Finally, results from both methods were backtested against a past cyber hack on a vehicular system, and we found that recommendations from cybersafety were likely to mitigate the risks of the incident.

scholarly journals Robust TOA-Based UAS Navigation under Model Mismatch in GNSS-Denied Harsh Environments

2020 ◽  
Vol 12 (18) ◽  
pp. 2928
Author(s):  
Jan Mortier ◽  
Gaël Pagès ◽  
Jordi Vilà-Valls
Keyword(s):  
Intelligent Transportation Systems ◽  
Autonomous Systems ◽  
Real Life ◽  
Transportation Systems ◽  
Urban Environments ◽  
Global Navigation Satellite Systems ◽  
Ultra Wideband ◽  
Time Of Arrival ◽  
Navigation Systems ◽  
Satellite Systems

Global Navigation Satellite Systems (GNSS) is the technology of choice for outdoor positioning purposes but has many limitations when used in safety-critical applications such Intelligent Transportation Systems (ITS) and Unmanned Autonomous Systems (UAS). Namely, its performance clearly degrades in harsh propagation conditions and is not reliable due to possible attacks or interference. Moreover, GNSS signals may not be available in the so-called GNSS-denied environments, such as deep urban canyons or indoors, and standard GNSS architectures do not provide the precision needed in ITS. Among the different alternatives, cellular signals (LTE/5G) may provide coverage in constrained urban environments and Ultra-Wideband (UWB) ranging is a promising solution to achieve high positioning accuracy. The key points impacting any time-of-arrival (TOA)-based navigation system are (i) the transmitters’ geometry, (ii) a perfectly known transmitters’ position, and (iii) the environment. In this contribution, we analyze the performance loss of alternative TOA-based navigation systems in real-life applications where we may have both transmitters’ position mismatch, harsh propagation environments, and GNSS-denied conditions. In addition, we propose new robust filtering methods able to cope with both effects up to a certain extent. Illustrative results in realistic scenarios are provided to support the discussion and show the performance improvement brought by the new methodologies with respect to the state-of-the-art.

Roboterbasierte spanende Bearbeitung*/Robot-Based Milling Operation. Machine Learning Algorithm for a model-based feed-forward torque control

2019 ◽  
Vol 109 (05) ◽  
pp. 352-357
Author(s):  
C. Brecher ◽  
L. Gründel ◽  
L. Lienenlüke ◽  
S. Storms
Keyword(s):  
Machine Learning ◽  
Position Control ◽  
Learning Algorithm ◽  
Milling Process ◽  
Industrial Robots ◽  
Torque Control ◽  
Feed Forward ◽  
Control Loops ◽  
Model Based ◽  
Machine Learning Approach

Die Lageregelung von konventionellen Industrierobotern ist nicht auf den dynamischen Fräsprozess ausgelegt. Eine Möglichkeit, das Verhalten der Regelkreise zu optimieren, ist eine modellbasierte Momentenvorsteuerung, welche in dieser Arbeit aufgrund vieler Vorteile durch einen Machine-Learning-Ansatz erweitert wird. Hierzu wird die Umsetzung in Matlab und die simulative Evaluation erläutert, die im Anschluss das Potenzial dieses Konzeptes bestätigt.   The position control of conventional industrial robots is not designed for the dynamic milling process. One possibility to optimize the behavior of the control loops is a model-based feed-forward torque control which is supported by a machine learning approach due to many advantages. The implementation in Matlab and the simulative evaluation are explained, which subsequently confirms the potential of this concept.

A Systems Approach to the Design and Development of Interactive Videodisc Training

1989 ◽  
Vol 17 (4) ◽  
pp. 255-271 ◽  
Author(s):  
Jeffrey A. Cantor
Keyword(s):  
Formative Evaluation ◽  
Systems Approach ◽  
System Development ◽  
Process Analysis ◽  
Evaluation Process ◽  
Interactive Videodisc ◽  
Systematic Design ◽  
Design And Development ◽  
Test And Evaluation ◽  
Instructional Product

This article describes a four-phased process used by the U.S. Navy for the systematic design and development of interactive videodisc (IVD) courseware (ICW). Phase One of the process, Analysis and Alternatives, describes the methodology for analyzing job/task data for the purpose of verifying the appropriateness of interactive videodisc as the medium of instructional choice, and for analyzing the proposed problem, and scoping out the approach and solution. Phase Two, System Design, describes the process for the layout and design of the IVD instructional product. Phase Three, System Development, discusses the process followed to actually develop and construct an IVD system. Lastly, Phase Four, System Test and Evaluation, will describe the formative evaluation process through which the IVD product and system is proven ready for use.

scholarly journals COVID-19: Examining the case growth-rate due to Visitor vs Local Mobility using Machine Learning: A Study in the United States

2021 ◽  
Author(s):  
satya katragadda ◽  
ravi teja bhupatiraju ◽  
vijay raghavan ◽  
ziad ashkar ◽  
raju gottumukkala
Keyword(s):  
United States ◽  
Machine Learning ◽  
Statistical Significance ◽  
Disease Outbreaks ◽  
The United States ◽  
Mobile Phone Data ◽  
Infectious Disease Outbreaks ◽  
Local Mobility ◽  
Local Risk ◽  
The Impact

Abstract Background: Travel patterns of humans play a major part in the spread of infectious diseases. This was evident in the geographical spread of COVID-19 in the United States. However, the impact of this mobility and the transmission of the virus due to local travel, compared to the population traveling across state boundaries, is unknown. This study evaluates the impact of local vs. visitor mobility in understanding the growth in the number of cases for infectious disease outbreaks. Methods: We use two different mobility metrics, namely the local risk and visitor risk extracted from trip data generated from anonymized mobile phone data across all 50 states in the United States. We analyzed the impact of just using local trips on infection spread and infection risk potential generated from visitors' trips from various other states. We used the Diebold-Mariano test to compare across three machine learning models. Finally, we compared the performance of models, including visitor mobility for all the three waves in the United States and across all 50 states. Results: We observe that visitor mobility impacts case growth and that including visitor mobility in forecasting the number of COVID-19 cases improves prediction accuracy by 34. We found the statistical significance with respect to the performance improvement resulting from including visitor mobility using the Diebold-Mariano test. We also observe that the significance was much higher during the first peak March to June 2020. Conclusion: With presence of cases everywhere (i.e. local and visitor), visitor mobility (even within the country) is shown to have significant impact on growth in number of cases. While it is not possible to account for other factors such as the impact of interventions, and differences in local mobility and visitor mobility, we find that these observations can be used to plan for both reopening and limiting visitors from regions where there are high number of cases.

scholarly journals PSHA after a strong earthquake: hints for the recovery

2016 ◽  
Vol 59 ◽  
Author(s):  
L. Peruzza ◽  
R. Gee ◽  
B. Pace ◽  
G. Roberts ◽  
O. Scotti ◽  
...  
Keyword(s):  
Hazard Analysis ◽  
Central Italy ◽  
Safety Evaluation ◽  
Preliminary Evidence ◽  
Source Model ◽  
Aftershock Sequence ◽  
Earthquake Occurrence ◽  
Central Italy Earthquake ◽  
Complex Fault ◽  
Fault Source

<p>We perform aftershock probabilistic seismic hazard analysis (APSHA) of the ongoing aftershock sequence following the Amatrice August 24th, 2016 Central Italy earthquake. APSHA is a time-dependent PSHA calculation where earthquake occurrence rates decrease after the occurrence of a mainshock following an Omori-type decay. In this paper we propose a fault source model based on preliminary evidence of the complex fault geometry associated with the mainshock. We then explore the possibility that the aftershock seismicity is distributed either uniformly or non-uniformly across the fault source. The hazard results are then computed for short-intermediate exposure periods (1-3 months, 1 year). They are compared to the background hazard and intended to be useful for post-earthquake safety evaluation.</p>

scholarly journals Development of Fire Safety Evaluation Indices for Long-term Care Hospitals for the Elderly through Analytic Hierarchy Process Analysis

2021 ◽  
Vol 35 (6) ◽  
pp. 61-67
Author(s):  
Soo-Kyung Shin ◽  
Young-Hoon Bae ◽  
Jun-Ho Choi
Keyword(s):  
Analytic Hierarchy Process ◽  
Fire Safety ◽  
Long Term Care ◽  
Process Analysis ◽  
Safety Evaluation ◽  
The Elderly ◽  
Analytic Hierarchy ◽  
Term Care ◽  
Hierarchy Process

Long-term care hospitals for the elderly are places for the elderly and patients with impaired mobility to live in, but these places face a high risk of great damage in the event of a fire. The standards for fire safety at long-term care hospitals for the elderly are limited to inspection of firefighting facilities and training plans, with no index to evaluate the evacuation plans, facilities for evacuation in case of fire, and the fire response manuals of long-term care hospitals for the elderly. Therefore, this study tries to carry out a basic analysis and establish fire safety evaluation indices for long-term care hospitals for the elderly. To that end, the study derives the importance and priorities of the indices related to fire safety in long-term care hospitals for the elderly through an analytic hierarchy process questionnaire surveying 44 firefighting experts. Finally, considering the importance and priorities of the indices, this study presents fire safety evaluation standards (drafts) for long-term care hospitals for the elderly.

scholarly journals Reports of the 2013 AAAI Spring Symposium Series

AI Magazine ◽  
2013 ◽  
Vol 34 (3) ◽  
pp. 93-98 ◽  
Author(s):  
Vita Markman ◽  
Georgi Stojanov ◽  
Bipin Indurkhya ◽  
Takashi Kido ◽  
Keiki Takadama ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cognitive Development ◽  
Behavior Change ◽  
Autonomous Systems ◽  
Data Driven ◽  
Intelligent Robots ◽  
Symposium Series ◽  
Development Data ◽  
Weakly Supervised

The Association for the Advancement of Artificial Intelligence was pleased to present the AAAI 2013 Spring Symposium Series, held Monday through Wednesday, March 25-27, 2013. The titles of the eight symposia were Analyzing Microtext, Creativity and (Early) Cognitive Development, Data Driven Wellness: From Self-Tracking to Behavior Change, Designing Intelligent Robots: Reintegrating AI II, Lifelong Machine Learning, Shikakeology: Designing Triggers for Behavior Change, Trust and Autonomous Systems, and Weakly Supervised Learning from Multimedia. This report contains summaries of the symposia, written, in most cases, by the cochairs of the symposium.

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