Autonomous Vehicles

The goal of this paper is to compute odometry of vehicle using low-level controls combined with inertial measurement unit. The Low-Level control includes design of Drive-by-Wire mechanisms for steering, brake and accelerator systems with appropriate motors and encoder. Experimentation with encoders and DC motors of steering and brake has been carried out first with various embedded modules to choose best suitable module. The experimentation has led to choosing BeagleBone Black (BBB), A low-cost, open-source community-supported development platform for real-time analysis provided by the TI Sitara AM3358 ARM Cortex-A8 processor with Linux-based operating system. Using BBB dedicated hardware module for high CPR (Counts per Revolution) encoders, the vehicle position is evaluated. Using BBB serial cape, it is interfaced to Roboteq motor controller (used for steering and brake motor) and steering encoder for steering wheel position control. The major task of the paper is the evaluation of odometry from using vehicle rear wheel encoders combined with inertial measurement unit. The paper is carried out on a dune buggy; petrol powered motor vehicle with Ackermann drive platform type and mobility. Initially, Drive-by-wire mechanism for steering, brake and accelerator is designed. Autonomous steering control of vehicle is carried out with feedback from steering motor encoder and steering hand wheel encoder connected to axle of steering system. Using IMU (Inertial Measurement Unit) yaw angle and rear wheel axle encoder position value, the odometry of vehicle are computed. Combined with inertial measurement units, they have proven to be a precise and low-cost sensor for vehicle odometry evaluation.

Στα πλαίσια της παρούσας εργασίας χρησιμοποιήθηκε ο Carla simulator, με βάση τον οποίο δημιουργήθηκαν αυτόνομες συμπεριφορές που εφαρμόζονται σε αυτοκινούμενα αυτοκίνητα, με την γλώσσα Python σε σύστημα με AMD Ryzen 5 με 16 BG RAM και κάρτα γραφικών NVIDIA GeForce GTX 1650 με αυτόνομη RAM 4GB σε Windows.
Χρησιμοποιήθηκαν διάφορες εκδόσεις του simulator – η stable 0.8.2 (που μετράει αρκετά έτη πια και τις λείπουν αρκετά χαρακτηριστικά), η 0.9.10, η 0.9.5 – στην οποία υλοποιήθηκε και έτρεξε μέρος του κώδικα, και η 0.9.8 (στην οποία έτρεξαν τα περισσότερα ερωτήματα).

Automatically assessing driving behaviour against traffic rules is a challenging task for improving the safety of Automated Vehicles (AVs). There are no AV specific traffic rules against which AV behaviour can be assessed. Moreover current traffic rules can be imprecisely expressed and are sometimes conflicting making it hard to validate AV driving behaviour. Therefore, in this paper, we propose a Defeasible Deontic Logic (DDL) based driving behaviour assessment methodology for AVs. DDL is used to effectively handle rule exceptions and resolve conflicts in rule norms. A data-driven experiment is conducted to prove the effectiveness of the proposed methodology.

Autonomous Vehicles (AVs) have the potential to make motorized transport safer and more sustainable, by integrating clean technologies and supporting flexible shared-mobility services. Leveraging this new form of transport to transform mobility in cities will depend fundamentally on public acceptance of AVs, and the ways in which individuals choose to use them, to meet their daily travel needs. Empirical studies exploring public attitudes towards automated driving technologies and interest in AVs have emerged in the last few years. However, within this strand of research there is a paucity of theory-driven and behaviourally consistent methodologies to unpack the determinants of user adoption decisions with respect to AVs. In this paper, we seek to fill this gap, by advancing and testing four conceptual frameworks which could be deployed to capture the range of possible behavioural influences on individuals’ AV adoption decisions. The frameworks integrate socio-demographic variables and relevant latent behavioural factors, including perceived benefits and perceived ease of use of AVs, public fears and anxieties regarding AVs, subjective norm, perceived behavioural control, and attitudinal factors covering the environment, technology, collaborative consumption, public transit and car ownership. We demonstrate the utility and validity of the frameworks, by translating the latent variables into indicator items in a structured questionnaire, and administering it online to a random sample of adult individuals (n = 507). Using the survey data in confirmatory factor analyses, we specify and demonstrate scale reliability of indicator items, and convergent and discriminant validity of relationships among latent variables. Ultimately, we advance four measurement models. These theory-grounded measurement models are intended for application in research aimed at understanding and predicting (a) AV interest and adoption intentions, and (b) user adoption decisions regarding three different AV modes: ownership, sharing and public transport.

EMERGING TECHNOLOGIES, LAW AND POLICIES: TELEMATICS, INSURANCE, AND ROAD SAFETY
QATAR UNIVERSITY, CENTER FOR LAW AND DEVELOPMENT
October 2-3, 2022
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In recent years, there are growing concerns about emerging technologies and, more in general, on the impact of science and technology on the society as a whole. Science and technology are often perceived as negative, elitist and consequently disputable at the point of creating dissent and distrust in increasing shares of the population. This type of dissent can be showed against Governments, or multinational companies possessing the technology or exploiting it for commercial reasons. In addition, there are also protests and contestations on how certain technologies collect information and use it for different scopes than the ones for which they have been collected.

By contrast, science and technology have also essential roles in pursuing the improvements of the society, granting access to technology is an extremely important tool for educational purposes in developing and developed countries, it can save lives when directly implemented in the health sector in both the rich and poor countries or regions or when they facilitate access to water or food or energy in countries where this access is not available to all, in particular the most vulnerable groups in the poorest or most disadvantaged countries.

In COVID-19 times, among the most important health crisis around the world, the World Health Organization (WHO) still lists the death by road accidents, especially among adults as a growing concern. According to the global status report on road safety 2018 by World Health Organization, deaths from road traffic crashes have increased to 1.35 million a year (average of 3 700 people dying on the world’s roads every day). One of the saddest statistics revealed in this report is that road accident is the number one cause of death for children and young adults globally. There are several reasons why adults are involved in fatal road accidents: (1) over-confident, (2) poor assessment of hazard, (3) prevalent risk taking, (4) speeding, (5) not wearing seat belts, (6) mobile phone usage and (7) driving at night. Almost all of these factors are behavioral related, which are improvable through well-designed interventions. Today, almost everyone uses a smart hand phone, especially youths. These smarts phones are equipped with accurate built-in GPS and are portable telematics devices.
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Based on the above literature, the main aim of this call for papers is to address telematics and road safety topics to reduce risky driving behavior amongst drivers. This can occur through a feedback mechanism customized for each driver based on risk profiling through obtained telematics data. Artificial intelligence will be designed in the feedback mechanism to provide such customized feedback. The secondary aim will be to create indirect interventions through the usage of telematics data for traffic violations citations and UBI without infringing data security and privacy.
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In addition, this conference wants also to address which instruments and means can be enacted to insure for autonomous vehicles more broadly and the extent to which the use of certain insurance technologies (like telematics) is going to increase inequality and discrimination against those from lower socio-economic levels.
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Based on the aforementioned, this call for papers is seeking contributions addressing the following topics with a focus on regulations, policies, science and technology at domestic, international, comparative and transnational perspective:

The Relations among Science, Technology and Society
Critical Theories on Science and Technology
Insurance Companies, Regulatory Authorities and Telematics
Blockchain Technologies, Autonomous Vehicles and the Insurance Industries
Obligations of the Insurer and the Insured in Autonomous Vehicles
Pricing and Compensation for Injury Caused by Autonomous Vehicles
Balancing the Use of Insurance Technologies (including telematics), Equality Principles and Non-Discrimination
Scientific Developments for Existing Mobile/Smartphone Application in Road Safety
Mobile Applications (apps) that is able to monitor driving behavior with an artificial intelligence (AI)
Nudging, Behavioral Sciences changes in drivers
Interventions of risky driving behavior by using feedback messages from telematics devices
Social sciences in using telematics devices for direct behavior interventions
Legal developments from the perspective of using telematics devices for traffic violations and usage-based insurance
National Insurance Laws Applicable to Telematics
Drafting of Changes to National and International Policies related to the use of telematics and road safety
Road Safety as a Public Health Issue
Privacy concern in terms of data sharing (data handled by 3rd party), especially driving data that could be used against them (accident claims purpose or causing accidents)
Data security aspects: (a) issues with “being monitored”, (b) how open are drivers to having their behavior monitored, (c) data being used by law enforcement or criminal/civil actions
Admissibility of telematics data for legal matters – especially traffic violations (evidence of authenticity)
Should insurance be Usage Based Insurance as a means to promote road safety Innovation.
New Technological Developments Through Citizen’s Science in a Comparative Perspective
Business and Human Rights, Industry Standards and Regulations, Impact Investing
The Role of Intellectual Property, Technology Transfer and Know-How
Bridging the Gap Between Business and Government
Public-Private Partnerships
The Role of Public Administration and the Law in Questioning and Supervision the So-Called “Disruptive Technologies”
Third World Approaches to International Law Regarding IP Law and Technology
Any other topic that is of relevance to this call for papers
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Venue and Dates
A conference will take place online.

Applications
Applications must be submitted by July 30,2022 via email to the attention of Imad Antoine Ibrahim ([email protected])  and Paolo Davide Farah ([email protected])  and also to the following email address: [email protected]

Please include the following information:
•    The author’s name and affiliation;
•    A 300-word abstract [Word file or PDF];
•    The author’s CV, including a list of relevant publications, if applicable;
•    The author’s contact details, including e-mail address and phone number;

Eligibility
The international conference will take place in person, on the premises of the Qatar University, Center for Law and Development (CLD)on October 2-3, 2022

‍Publication Opportunities
The organizers have plans to publish the contributions and intend to submit a book proposal to such publishers as Palgrave MacMillan or other prestigious international publishers.

Conference Chairs
Imad Antoine Ibrahim, Qatar University, College of Law, Center for Law and Development (CLD), Doha, Qatar.

Paolo Davide Farah, West Virginia University, John D. Rockefeller IV School of Policy and Politics, West Virginia, United States and gLAWcal – Global Law Initiatives for Sustainable Development, UK.

‍Organizing Institutions
The international conference is organized by Qatar University, Center for Law and Development (CLD), Qatar. The event is in collaboration with gLAWcal - Global Law Initiatives for Sustainable Development, UK, West Virginia University, Rockefeller School of Policy and Politics, WVU Eberly College Interdisciplinary Research Collaborative on Global Challenges and Local Response Initiative, USA and the American Society of International Law (ASIL) Interest Group on International Law and Technology.

The challenge of learning a new concept, object , or a new medical disease recognition without receiving any examples beforehand is called Zero-Shot Learning (ZSL). One of the major issues in deep learning based methodolo-gies such as in Medical Imaging, Autonomous Systems and other real-world applications is the requirement of feeding a large annotated and labelled datasets, prepared by an expert human to train the network model. ZSL is known for having minimal human intervention by mainly relying only on previously known concepts and current auxiliary information. This is an ever-growing research for the cases where we have very limited or no datasets available and at the same time, the detection/recognition system has human-like characteristics in learning new concepts. Therefore, it makes it applicable in real-world scenarios, from developing autonomous vehicles to medical imaging and COVID-19 Chest X-Ray (CXR) based diagnosis. In this review paper, we present the definition of the problem , we review over fundamentals, and the challenging steps of Zero-Shot Learning, including state-of-the-art categories of solutions as well as our recommended solution, motivations behind each approach, and their advantages over each category to guide the researchers to proceed with the best techniques and practices based on their applications. Inspired from different settings and extensions, we introduce a novel and broaden solution called one/few-shot learning. We then review through different image datasets inducing medical and non-medical images, the variety of splits, and the evaluation protocols proposed so far. Finally, we discuss the recent applications and future directions of ZSL. We aim to convey a useful intuition through this paper towards the goal of handling complex computer vision learning tasks more similar to the way humans learn. We mainly focus on two applications in the current modern yet challenging era: copping with an early and fast diagnosis of COVID-19 cases in the 2020 pandemic, as well as helping to develop safer autonomous systems, such as self-driving cars and ambulances.

The goal of the presented work is to develop an automotive radar sensor behavioral model to outline closed loop interaction of driver and vehicle in a synthetic environment. For this interaction open simulation interface (OSI) and functional mockup interface (FMI) standards are used. This paper describes the architecture overview, working concept of FMI, OSI and different radar functions like radar channel and digital signal processing in detail. The description of the scenario for the verification of radar behavioral model results and development of highly automated driving (HAD) functions for the closed loop simulation is elaborated in this paper.

In this chapter, we give a brief overview of the traditional notion of responsibility and introduce a concept of distributed responsibility within a responsibility network of engineers, driver, and autonomous driving system. In order to evaluate this concept, we explore the notion of man–machine hybrid systems with regard to self-driving cars and conclude that the unit comprising the car and the operator/driver consists of such a hybrid system that can assume a shared responsibility different from the responsibility of other actors in the responsibility network. Discussing certain moral dilemma situations that are structured much like trolley cases, we deduce that as long as there is something like a driver in autonomous cars as part of the hybrid system, she will have to bear the responsibility for making the morally relevant decisions that are not covered by traffic rules.

Multi sensor Data Fusion for Advanced Driver Assistance Systems (ADAS) in Automotive industry has gained a lot of attention lately with the advent of self-driving vehicles and road traffic safety applications. In order to achieve an efficient ADAS, accurate scene object perception in the vicinity of sensor field-of-view (FOV) is vital. It is not only important to know where the objects are, but also the necessity is to predict the object’s behavior in future time space for avoiding the fatalities on the
road. The major challenges in multi sensor data fusion (MSDF) arise due to sensor errors, multiple occluding targets and changing weather conditions. Thus, In this thesis to address some of the challenges a novel cooperative fusion architecture is proposed for road obstacle detection. Also, an architecture for multi target tracking is
designed with robust track management. In order to evaluate the proposed tracker’s performance with different fusion paradigms, a discrete event simulation model is proposed. Experiments and evaluation of the above mentioned methods in real time
and simulated data proves the robustness of the techniques considered for data fusion.