An Evaluation Schema for the Ethical Use of Autonomous Robotic Systems in Security Applications

We propose a multi-step evaluation schema designed to help procurement agencies and others to examine the ethical dimensions of autonomous systems to be applied in the security sector, including autonomous weapons systems

A Review of Platforms for the Development of Agent Systems

Agent-based computing is an active field of research with the goal of building autonomous software of hardware entities. This task is often facilitated by the use of dedicated, specialized frameworks. For almost thirty years, many such agent platforms have been developed. Meanwhile, some of them have been abandoned, others continue their development and new platforms are released. This paper presents a up-to-date review of the existing agent platforms and also a historical perspective of this domain. It aims to serve as a reference point for people interested in developing agent systems. This work details the main characteristics of the included agent platforms, together with links to specific projects where they have been used. It distinguishes between the active platforms and those no longer under development or with unclear status. It also classifies the agent platforms as general purpose ones, free or commercial, and specialized ones, which can be used for particular types of applications.Comment: 40 pages, 2 figures, 9 tables, 83 reference

END-TO-END LEARNING UTILIZING TEMPORAL INFORMATION FOR VISION- BASED AUTONOMOUS DRIVING

End-to-End learning models trained with conditional imitation learning (CIL) have demonstrated their capabilities in driving autonomously in dynamic environments. The performance of such models however is limited as most of them fail to utilize the temporal information, which resides in a sequence of observations. In this work, we explore the use of temporal information with a recurrent network to improve driving performance. We propose a model that combines a pre-trained, deeper convolutional neural network to better capture image features with a long short-term memory network to better explore temporal information. Experimental results indicate that the proposed model achieves performance gain in several tasks in the CARLA benchmark, compared to the state-of-the-art models. In particular, comparing with other CIL-based models in the most challenging task, navigation in dynamic environments, we achieve a 96% success rate while other CIL-based models had 82-92% in training conditions; we also achieved 88% while other CIL-based models did 42-90% in the new town and new weather conditions. The subsequent ablation study also shows that all the major features of the proposed model are essential for improving performance. We, therefore, believe that this work contributes significantly towards safe, efficient, clean autonomous driving for future smart cities

Autonomous Vehicles in Road Tunnels : A Risk Safety Perspective

This study examines the challenges associated with deploying autonomous vehicles (AVs) in road tunnels, focusing on both operational aspects and vehicle-human interaction. This work explored that road tunnels present unique constraints, such as limited visibility and confined spaces, which necessitate careful consideration for AV integration. It is observed that factors like varying light conditions and restricted communication capabilities within tunnels impact AVs' performance. Additionally, the study investigates and categorizes challenges related to tunnel geometries, infrastructure modifications, sensor technologies, emergency situations, and human-machine interaction. Furthermore, this work comprehensively explored academic and non-academic literature, gathering contemporary knowledge on AVs in road tunnels in one place to provide a foundational base for researchers on this topic. In this regard, the adopted methodological framework is also presented for researchers' review. The other notable contribution is to specifically highlight the critical operational issues of human-AV interaction in tunnel environments. In the last section, it also proposes potential solutions to these issues. In doing so, it keeps the directional approach open for other researchers as there are insightful risk-related implications for further research in this significant domain

The Survey, Taxonomy, and Future Directions of Trustworthy AI: A Meta Decision of Strategic Decisions

When making strategic decisions, we are often confronted with overwhelming information to process. The situation can be further complicated when some pieces of evidence are contradicted each other or paradoxical. The challenge then becomes how to determine which information is useful and which ones should be eliminated. This process is known as meta-decision. Likewise, when it comes to using Artificial Intelligence (AI) systems for strategic decision-making, placing trust in the AI itself becomes a meta-decision, given that many AI systems are viewed as opaque "black boxes" that process large amounts of data. Trusting an opaque system involves deciding on the level of Trustworthy AI (TAI). We propose a new approach to address this issue by introducing a novel taxonomy or framework of TAI, which encompasses three crucial domains: articulate, authentic, and basic for different levels of trust. To underpin these domains, we create ten dimensions to measure trust: explainability/transparency, fairness/diversity, generalizability, privacy, data governance, safety/robustness, accountability, reproducibility, reliability, and sustainability. We aim to use this taxonomy to conduct a comprehensive survey and explore different TAI approaches from a strategic decision-making perspective