scholarly journals A Fail-Operational Control Architecture Approach and Dead-Reckoning Strategy in Case of Positioning Failures

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 442
Author(s):  
Jose Angel Matute-Peaspan ◽  
Joshue Perez ◽  
Asier Zubizarreta
Keyword(s):  
Dead Reckoning ◽  
Human Interaction ◽  
Control Architecture ◽  
Minimal Risk ◽  
Operational Control ◽  
Automated Driving ◽  
Risk Condition ◽  
Driving Task ◽  
System Verification ◽  
Set Up

Presently, in the event of a failure in Automated Driving Systems, control architectures rely on hardware redundancies over software solutions to assure reliability or wait for human interaction in takeover requests to achieve a minimal risk condition. As user confidence and final acceptance of this novel technology are strongly related to enabling safe states, automated fall-back strategies must be assured as a response to failures while the system is performing a dynamic driving task. In this work, a fail-operational control architecture approach and dead-reckoning strategy in case of positioning failures are developed and presented. A fail-operational system is capable of detecting failures in the last available positioning source, warning the decision stage to set up a fall-back strategy and planning a new trajectory in real time. The surrounding objects and road borders are considered during the vehicle motion control after failure, to avoid collisions and lane-keeping purposes. A case study based on a realistic urban scenario is simulated for testing and system verification. It shows that the proposed approach always bears in mind both the passenger’s safety and comfort during the fall-back maneuvering execution.

scholarly journals Driver intervention performance assessment as a key aspect of L3–L4 automated vehicles deployment

2021 ◽  
Vol 11 (1) ◽  
pp. 845-852
Author(s):  
Aleksandra Rodak ◽  
Paweł Budziszewski ◽  
Małgorzata Pędzierska ◽  
Mikołaj Kruszewski
Keyword(s):  
Performance Assessment ◽  
Minimal Risk ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Driving System ◽  
Driving Task ◽  
Driving Conditions

Abstract In L3–L4 vehicles, driving task is performed primarily by automated driving system (ADS). Automation mode permits to engage in non-driving-related tasks; however, it necessitates continuous vigilance and attention. Although the driver may be distracted, a request to intervene may suddenly occur, requiring immediate and appropriate response to driving conditions. To increase safety, automated vehicles should be equipped with a Driver Intervention Performance Assessment module (DIPA), ensuring that the driver is able to take the control of the vehicle and maintain it safely. Otherwise, ADS should regain control from the driver and perform a minimal risk manoeuvre. The paper explains the essence of DIPA, indicates possible measures, and describes a concept of DIPA framework being developed in the project.

80 MPH and out-of-the-loop: Effects of real-world semi-automated driving on driver workload and arousal

2018 ◽  
Vol 62 (1) ◽  
pp. 1878-1882 ◽  
Author(s):  
Francesco N. Biondi ◽  
Monika Lohani ◽  
Rachel Hopman ◽  
Sydney Mills ◽  
Joel M. Cooper ◽  
...  
Keyword(s):  
Human Error ◽  
Response Times ◽  
Detection Task ◽  
Operational Control ◽  
Automated Driving ◽  
Behavioral Measures ◽  
Out Of The Loop ◽  
Driver Workload ◽  
Driving Task ◽  
Assistance Systems

The introduction of semi-automated driving systems is expected to mitigate the safety consequences of human error. Observational findings suggest that relinquishing control of vehicle operational control to assistance systems might diminish driver engagement in the driving task, by reducing levels of arousal. In this study, drivers drove a Tesla Model S with Autopilot in both semi-automated and manual modes. Driver behavior was monitored using a combination of physiological and behavioral measures. Compared to manual driving, a reduction in driver physiological activation was observed during semi-automated driving. Also, performance to the peripheral detection task suffered in semi-automated mode, with slower response times recorded in this condition than during manual driving. Taken together, our data suggest that semi-automated driving might not ease safety consequences of human error. Instead, these findings suggest it might cause a drop in driver monitoring, possibly followed by a spike in automation-generated distraction.

scholarly journals Imaging radar for automated driving functions

2021 ◽  
pp. 1-9
Author(s):  
Hasan Iqbal ◽  
Andreas Löffler ◽  
Mohamed Nour Mejdoub ◽  
Daniel Zimmermann ◽  
Frank Gruson
Keyword(s):  
Automotive Industry ◽  
Short Range ◽  
Dead Reckoning ◽  
Beam Forming ◽  
Parking Functions ◽  
Automated Driving ◽  
Radar Sensors ◽  
Large Step ◽  
Set Up ◽  
Near Future

Abstract This work presents the implementation of a synthetic aperture radar (SAR) at 77 GHz, for automotive applications. This implementation is unique in the sense that it is a radar-only solution for most use-cases. The set-up consists of two radar sensors, one to calculate the ego trajectory and the second for SAR measurements. Thus the need for expensive GNSS-based dead reckoning systems, which are in any case not accurate enough to fulfill the requirements for SAR, is eliminated. The results presented here have been obtained from a SAR implementation which is able to deliver processed images in a matter of seconds from the point where the targets were measured. This has been accomplished using radar sensors which will be commercially available in the near future. Hence the results are easily reproducible since the deployed radars are not special research prototypes. The successful widespread use of SAR in the automotive industry will be a large step forward toward developing automated parking functions which will be far superior to today's systems based on ultrasound sensors and radar (short range) beam-forming algorithms. The same short-range radar can be used for SAR, and the ultrasound sensors can thus be completely omitted from the vehicle.

ORAL HISTORY SOURCES AS LEARNING MATERIALS: A CASE STUDY OF THE NATIONAL UNIVERSITY OF SCIENCE AND TECHNOLOGY

2016 ◽  
Vol 3 (2) ◽  
pp. 82-93
Author(s):  
Gugulethu Shamaine Nkala ◽  
Rodreck David
Keyword(s):  
Oral History ◽  
Learning Experience ◽  
Science And Technology ◽  
Group Discussion ◽  
Human Interaction ◽  
Learning Material ◽  
Wide Range ◽  
National Archives ◽  
National University ◽  
Set Up

Knowledge presented by Oral History (OH) is unique in that it shares the tacit perspective, thoughts, opinions and understanding of the interviewee in its primary form. While teachers, lecturers and other education specialists have at their disposal a wide range of primary, secondary and tertiary sources upon which to relate and share or impart knowledge, OH presents a rich source of information that can improve the learning and knowledge impartation experience. The uniqueness of OH is presented in the following advantages of its use: it allows one to learn about the perspectives of individuals who might not otherwise appear in the historical record; it allows one to compensate for the digital age; one can learn different kinds of information; it provides historical actors with an opportunity to tell their own stories in their own words; and it offers a rich opportunity for human interaction. This article discusses the placement of oral history in the classroom set-up by investigating its use as a source of learning material presented by the National Archives of Zimbabwe to students in the Department of Records and Archives Management at the National University of Science and Technology (NUST). Interviews and a group discussion were used to gather data from an archivist at the National Archives of Zimbabwe, lecturers and students in the Department of Records and Archives Management at NUST, respectively. These groups were approached on the usability, uniqueness and other characteristics that support this type of knowledge about OH in a tertiary learning experience. The findings indicate several qualities that reflect the richness of OH as a teaching source material in a classroom set-up. It further points to weak areas that may be addressed where the source is considered a viable strategy for knowledge sharing and learning. The researchers present a possible model that can be used to champion the use of this rich knowledge source in classroom education at this university and in similar set-ups. 

Safety Enhancement by Automated Driving: What are the Relevant Scenarios?

2020 ◽  
Vol 64 (1) ◽  
pp. 1686-1690
Author(s):  
Niklas Grabbe ◽  
Michael Höcher ◽  
Alexander Thanos ◽  
Klaus Bengler
Keyword(s):  
Traffic Safety ◽  
Road Traffic ◽  
Test Cases ◽  
Automated Driving ◽  
Road Traffic Safety ◽  
Human Driver ◽  
Driving Task ◽  
Promising Solution ◽  
Vehicle Automation ◽  
Safety Enhancement

Automated driving offers great possibilities in traffic safety advancement. However, evidence of safety cannot be provided by current validation methods. One promising solution to overcome the approval trap (Winner, 2015) could be the scenario-based approach. Unfortunately, this approach still results in a huge number of test cases. One possible way out is to show the current, incorrect path in the argumentation and strategy of vehicle automation, and focus on the systemic mechanisms of road traffic safety. This paper therefore argues the case for defining relevant scenarios and analysing them systemically in order to ultimately reduce the test cases. The relevant scenarios are based on the strengths and weaknesses, in terms of the driving task, for both the human driver and automation. Finally, scenarios as criteria for exclusion are being proposed in order to systemically assess the contribution of the human driver and automation to road safety.

Dynamics of Trust in Automation and Interactive Decision Making during Driving Simulation Tasks

2021 ◽  
Vol 65 (1) ◽  
pp. 786-790
Author(s):  
Lucero Rodriguez Rodriguez ◽  
Carlos Bustamante Orellana ◽  
Jayci Landfair ◽  
Corey Magaldino ◽  
Mustafa Demir ◽  
...  
Keyword(s):  
Decision Making ◽  
Automated System ◽  
Automated Driving ◽  
Safety And Efficacy ◽  
High Performing ◽  
Interactive Decision Making ◽  
Driving Task ◽  
Preliminary Study ◽  
Self Driving Cars ◽  
The Impact

As technological advancements and lowered costs make self-driving cars available to more people, it becomes important to understand the dynamics of human-automation interactions for safety and efficacy. We used a dynamical approach to examine data from a previous study on simulated driving with an automated driving assistant. To maximize effect size in this preliminary study, we focused the current analysis on the two lowest and two highest-performing participants. Our visual comparisons were the utilization of the automated system and the impact of perturbations. Low-performing participants toggled and maintained reliance either on automation or themselves for longer periods of time. Decision making of high-performing participants was using the automation briefly and consistently throughout the driving task. Participants who displayed an early understanding of automation capabilities opted for tactical use. Further exploration of individual differences and automation usage styles will help to understand the optimal human-automation-team dynamic and increase safety and efficacy.

scholarly journals Supervision of a self-driving vehicle unmasks latent sleepiness relative to manually controlled driving

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Erin E. Flynn-Evans ◽  
Lily R. Wong ◽  
Yukiyo Kuriyagawa ◽  
Nikhil Gowda ◽  
Patrick F. Cravalho ◽  
...  
Keyword(s):  
Human Error ◽  
Reaction Times ◽  
Modern Society ◽  
Accident Risk ◽  
Road Accidents ◽  
Automated Driving ◽  
High Prevalence ◽  
Driving Task ◽  
Sleep Deficiency ◽  
Monitoring Performance

AbstractHuman error has been implicated as a causal factor in a large proportion of road accidents. Automated driving systems purport to mitigate this risk, but self-driving systems that allow a driver to entirely disengage from the driving task also require the driver to monitor the environment and take control when necessary. Given that sleep loss impairs monitoring performance and there is a high prevalence of sleep deficiency in modern society, we hypothesized that supervising a self-driving vehicle would unmask latent sleepiness compared to manually controlled driving among individuals following their typical sleep schedules. We found that participants felt sleepier, had more involuntary transitions to sleep, had slower reaction times and more attentional failures, and showed substantial modifications in brain synchronization during and following an autonomous drive compared to a manually controlled drive. Our findings suggest that the introduction of partial self-driving capabilities in vehicles has the potential to paradoxically increase accident risk.

Physiological Indicators of Driver Workload During Car-Following Scenarios and Takeovers in Highly Automated Driving

2021 ◽  
Author(s):  
Vishnu Radhakrishnan ◽  
Natasha Merat ◽  
Tyron Louw ◽  
Rafael Goncalves ◽  
Wei Lyu ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Electrodermal Activity ◽  
Automated Driving ◽  
Physiological Indicators ◽  
Car Following ◽  
Time Headway ◽  
Driver Workload ◽  
Driving Task ◽  
Vehicle Automation ◽  
Lead Vehicle

This driving simulator study, conducted as a part of Horizon2020-funded L3Pilot project, investigated how different car-following situations affected driver workload, within the context of vehicle automation. Electrocardiogram (ECG) and electrodermal activity (EDA)-based physiological metrics were used as objective indicators of workload, along with self-reported workload ratings. A total of 32 drivers were divided into two equal groups, based on whether they engaged in a non-driving related task (NDRT) during automation or monitored the drive. Drivers in both groups were exposed to two counterbalanced experimental drives, lasting ~18 minutes each, of Short (0.5 s) and Long (1.5 s) Time Headway conditions during automated car-following (ACF), which was followed by a takeover that happened with or without a lead vehicle. We observed that the workload on the driver due to the NDRT was significantly higher than both monitoring the drive during ACF and manual car-following (MCF). Furthermore, the results indicated that shorter THWs and the presence of a lead vehicle can significantly increase driver workload during takeover scenarios, potentially affecting the safety of the vehicle. This warrants further research into understanding safe time headway thresholds to be maintained by automated vehicles, without placing additional mental or attentional demands on the driver. To conclude, our results indicated that ECG and EDA signals are sensitive to variations in workload, and hence, warrants further investigation on the value of combining these two signals to assess driver workload in real-time, to help the system respond appropriately to the limitations of the driver and predict their performance in driving task if and when they have to resume manual control of the vehicle.

scholarly journals The L3Pilot Data Management Toolchain for a Level 3 Vehicle Automation Pilot

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 809 ◽  
Author(s):  
Johannes Hiller ◽  
Sami Koskinen ◽  
Riccardo Berta ◽  
Nisrine Osman ◽  
Ben Nagy ◽  
...  
Keyword(s):  
Data Management ◽  
Reference Architecture ◽  
Automated Driving ◽  
Iterative Processing ◽  
Vehicle Owner ◽  
Level 3 ◽  
System Functioning ◽  
Comprehensive Test ◽  
Vehicle Automation ◽  
Set Up

As industrial research in automated driving is rapidly advancing, it is of paramount importance to analyze field data from extensive road tests. This paper investigates the design and development of a toolchain to process and manage experimental data to answer a set of research questions about the evaluation of automated driving functions at various levels, from technical system functioning to overall impact assessment. We have faced this challenge in L3Pilot, the first comprehensive test of automated driving functions (ADFs) on public roads in Europe. L3Pilot is testing ADFs in vehicles made by 13 companies. The tested functions are mainly of Society of Automotive Engineers (SAE) automation level 3, some of them of level 4. In this context, the presented toolchain supports various confidentiality levels, and allows cross-vehicle owner seamless data management, with the efficient storage of data and their iterative processing with a variety of analysis and evaluation tools. Most of the toolchain modules have been developed to a prototype version in a desktop/cloud environment, exploiting state-of-the-art technology. This has allowed us to efficiently set up what could become a comprehensive edge-to-cloud reference architecture for managing data in automated vehicle tests. The project has been released as open source, the data format into which all vehicular signals, recorded in proprietary formats, were converted, in order to support efficient processing through multiple tools, scalability and data quality checking. We expect that this format should enhance research on automated driving testing, as it provides a shared framework for dealing with data from collection to analysis. We are confident that this format, and the information provided in this article, can represent a reference for the design of future architectures to implement in vehicles.

scholarly journals Setting Up Telemedicine: Internet of Things to Support Rural Oncology Clinics in a Low- and Middle-Income Country—A Pilot Project in Kenya

2020 ◽  
Vol 6 (Supplement_1) ◽  
pp. 58-58
Author(s):  
Lamech Sigu ◽  
Fredrick Chite ◽  
Emma Achieng ◽  
Andrew Koech
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Length Of Hospital Stay ◽  
Human Interaction ◽  
Sub Saharan Africa ◽  
Patient Treatment ◽  
The Internet ◽  
Tumor Boards ◽  
Iot Devices ◽  
Set Up

PURPOSE The Internet of Things (IoT) is a technology that involves all things connected to the Internet that share data over a network without requiring human-to-human interaction or human-to-computer interaction. Information collected from IoT devices can help physicians identify the best treatment process for patients and reach accurate and expected outcomes. METHODS The International Cancer Institute is partnering to set up remote oncology clinics in sub-Saharan Africa. Medical oncologists and expert teams from across the world connect with oncology clinics in other Kenyan counties—Kisumu, Meru, Makueni, Garissa, Kakamega, Bungoma, Siaya, and Vihiga counties. The furthest county is Garissa, approximately 651.1 km from Eldoret, and the nearest is Vihiga at 100.4 km from Eldoret. This study began July 2019, and as of November 30th, the team has hosted 21 sessions with an average of 11 participants attending a session led by a medical oncologist. RESULTS IoT devices have become a way by which a patient gets all the information he or she needs from a physician without going to the clinic. Patient monitoring can be done in real time, allowing access to real-time information with improved patient treatment outcomes and a decrease in cost. Through IoT-enabled devices, the International Cancer Institute has set up weekly virtual tumor boards during which cancer cases are presented and discussed by all participating counties. An online training module on cancer is also offered. Furthermore, remote monitoring of a patient’s health helps to reduce the length of hospital stay and prevents readmissions. CONCLUSION In our setting, which has a few oncologists, use of IoT and tumor boards has helped to improve patient decision support as well as training for general physicians.

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