On the Effects of Forced Trust on Implementations of Small Smart Cities

As an increasing number of cities pursue the idea of becoming smart cities, the variety in different approaches to reach this goal also grows. They cover the use of a spectrum of implementations for, inter alia, information systems, smart networks, and public services. In order to operate, these smart cities have to process multiple types of data including personal information. Ultimately, the systems and services that process these data are decided by the city with limited opportunities for their citizens to influence the details of their implementations. In these situations the citizens have no choice but to trust their city with the operation of these systems and the processing of their personal information. This type of a relationship, forced trust, affects the smart city implementation both directly and indirectly. These effects include additional considerations by the city to guarantee the protection of the citizens’ privacy and the security of their personal data, as well as the impacts of forced trust on the willingness of the citizens to adopt the offered services. In this thesis, privacy protection, data protection and security, system reliability and safety, and user avoidance were identified as the four major domains of concern for citizens with regard to forced trust. These domains cover most of the main impacts smart city projects have on their citizens, such as ubiquitous data collection, scarcity of control over the utilisation of one’s personal data, and uncertainty of the dependability of critical information systems. Additionally, technological and methodological approaches were proposed to address each of the discussed concerns. These include implementation of privacy by design in the development of the smart city, use of trusted platforms in data processing, detection and alleviation of potential fault chains, and providing the citizens the means to monitor their personal data. Finally, these recommendations were considered in the context of a small smart city. The Salo smart city project was used as an example and the recommendations were applied to the planned aspects of the upcoming smart city, such as knowledge-based management, a smart city application for information sharing, and increased transparency and justifiability in governance

Development and experimental validation of high performance embedded intelligence and fail-operational urban surround perception solutions of the PRYSTINE project

Automated Driving Systems (ADSs) commend a substantial reduction of human-caused road accidents while simultaneously lowering emissions, mitigating congestion, decreasing energy consumption and increasing overall productivity. However, achieving higher SAE levels of driving automation and complying with ISO26262 C and D Automotive Safety Integrity Levels (ASILs) is a multi-disciplinary challenge that requires insights into safety-critical architectures, multi-modal perception and real-time control. This paper presents an assorted effort carried out in the European H2020 ECSEL project—PRYSTINE. In this paper, we (1) investigate Simplex, 1oo2d and hybrid fail-operational computing architectures, (2) devise a multi-modal perception system with fail-safety mechanisms, (3) present a passenger vehicle-based demonstrator for low-speed autonomy and (4) suggest a trust-based fusion approach validated on a heavy-duty truck.</p

The 12th International Conference on Ambient Systems, Networks and Technologies (ANT) / The 4th International Conference on Emerging Data and Industry 4.0 (EDI40) / Affiliated Workshops

This paper presents a conceptual trust model for the perceptual sensor data of autonomous vehicles to improve their reliability and safety of operation. In particular, the model aims to ensure the trustworthiness of the data used in the automated decision-making processes of vehicles. The presented model forms a comprehensive view of factors that affect trust in the perceptual sensor data. These factors, namely environment, operation, security, and technical limitations, further cover a number of related trust parameters used to evaluate trust values. Each parameter is evaluated using Dempster-Shafer theory using a trust-distrust frame. Mass values are based on instantaneous sensor data and sensor behaviour tested under similar circumstances. As a result, this model is viable for use in conjunction with data fusion and decision-making to improve their reliability by enabling the tracking of the development of sensor trustworthiness.</p

Development and Experimental Validation of High Performance Embedded Intelligence and Fail-Operational Urban Surround Perception Solutions of the PRYSTINE Project

Automated Driving Systems (ADSs) commend a substantial reduction of human-caused road accidents while simultaneously lowering emissions, mitigating congestion, decreasing energy consumption and increasing overall productivity. However, achieving higher SAE levels of driving automation and complying with ISO26262 C and D Automotive Safety Integrity Levels (ASILs) is a multi-disciplinary challenge that requires insights into safety-critical architectures, multi-modal perception and real-time control. This paper presents an assorted effort carried out in the European H2020 ECSEL project&mdash;PRYSTINE. In this paper, we (1) investigate Simplex, 1oo2d and hybrid fail-operational computing architectures, (2) devise a multi-modal perception system with fail-safety mechanisms, (3) present a passenger vehicle-based demonstrator for low-speed autonomy and (4) suggest a trust-based fusion approach validated on a heavy-duty truck