A survey of self-awareness and its application in computing systems

Novel computing systems are increasingly being composed of large numbers of heterogeneous components, each with potentially different goals or local perspectives, and connected in networks which change over time. Management of such systems quickly becomes infeasible for humans. As such, future computing systems should be able to achieve advanced levels of autonomous behaviour. In this context, the system's ability to be self-aware and be able to self-express becomes important. This paper surveys definitions and current understanding of self-awareness and self-expression in biology and cognitive science. Subsequently, previous efforts to apply these concepts to computing systems are described. This has enabled the development of novel working definitions for self-awareness and self-expression within the context of computing systems

Self-Positioning Smart Buoys, The \u27Un-Buoy\u27 Solution: Logistic Considerations Using Autonomous Surface Craft Technology and Improved Communications Infrastructure

Moored buoys have long served national interests, but incur high development, construction, installation, and maintenance costs. Buoys which drift off-location can pose hazards to mariners, and in coastal waters may cause environmental damage. Moreover, retrieval, repair and replacement of drifting buoys may be delayed when data would be most useful. Such gaps in coastal buoy data can pose a threat to national security by reducing maritime domain awareness. The concept of self-positioning buoys has been advanced to reduce installation cost by eliminating mooring hardware. We here describe technology for operation of reduced cost self-positioning buoys which can be used in coastal or oceanic waters. The ASC SCOUT model is based on a self-propelled, GPS-positioned, autonomous surface craft that can be pre-programmed, autonomous, or directed in real time. Each vessel can communicate wirelessly with deployment vessels and other similar buoys directly or via satellite. Engineering options for short or longer term power requirements are considered, in addition to future options for improved energy delivery systems. Methods of reducing buoy drift and position-maintaining energy requirements for self-locating buoys are also discussed, based on the potential of incorporating traditional maritime solutions to these problems. We here include discussion of the advanced Delay Tolerant Networking (DTN) communications draft protocol which offers improved wireless communication capabilities underwater, to adjacent vessels, and to satellites. DTN is particularly adapted for noisy or loss-prone environments, thus it improves reliability. In addition to existing buoy communication via commercial satellites, a growing network of small satellites known as PICOSATs can be readily adapted to provide low-cost communications nodes for buoys. Coordination with planned vessel Automated Identification Systems (AIS) and International Maritime Organization standards for buoy and vessel notificat- - ion systems are reviewed and the legal framework for deployment of autonomous surface vessels is considered

Self-Aware LiDAR Sensors in Autonomous Systems using a Convolutional Neural Network

Autonomous systems, as found in autonomous driving and highly automated production systems, require an increased reliability in order to achieve their high economic potential. Self-aware sensors are a key component in highly reliable autonomous systems. In this paper we highlight a proof of concept (PoC) of a deep learning method that enables a LiDAR (Light detection and ranging) sensor to detect functional impairment. More specifically, a deep convolutional neural network (CNN) is developed and trained with labelled LiDAR data in the form of point clouds to classify the degree of impairment of its functionality. The results are statistically significant and can be regarded as a general classifier for objects within LiDAR data, applied to selected cases of sensor impairment. In detecting impairment and evaluating the correctness of the captured data, the sensor gains a basic form of self-awareness. The presented methods and insights pave the way for improved safety of autonomous systems by the means of more sophisticated “self-aware” neural networks

Situational awareness and adherence to the principle of distinction as a necessary condition for lawful autonomy

As a contribution to the CCW’s third informal meeting of experts on lethal autonomous weapon systems (LAWS), this briefing paper focuses on the implications of the requirement of situational awareness for autonomous action – whether by humans, machines or complex human-machine systems. For the purposes of this paper, ‘autonomy’ refers to self-directed action, and more specifically the action-according-to-rule that comprises military discipline. Unlike the algorithmic sense of a rule as that term is used in Artificial Intelligence (AI), military rules always require interpretation in relation to a specific situation, or situational awareness. Focusing on the principle of distinction, I argue that International Humanitarian Law (IHL) presupposes capacities of situational awareness that it does not, and cannot, fully specify. At the same time, autonomy or ‘self-direction’ in the case of machines requires the adequate specification (by human designers) of the conditions under which associated actions should be taken. This requirement for unambiguous specification of condition/action rules marks a crucial difference between autonomy as a legally accountable human capacity, and machine autonomy. The requirement for situational awareness in the context of combat, as a prerequisite for action that adheres to IHL, raises serious doubts regarding the feasibility of lawful autonomy in weapon systems

Situational awareness and adherence to the principle of distinction as a necessary condition for lawful autonomy

As a contribution to the CCW’s third informal meeting of experts on lethal autonomous weapon systems (LAWS), this briefing paper focuses on the implications of the requirement of situational awareness for autonomous action – whether by humans, machines or complex human-machine systems. For the purposes of this paper, ‘autonomy’ refers to self-directed action, and more specifically the action-according-to-rule that comprises military discipline. Unlike the algorithmic sense of a rule as that term is used in Artificial Intelligence (AI), military rules always require interpretation in relation to a specific situation, or situational awareness. Focusing on the principle of distinction, I argue that International Humanitarian Law (IHL) presupposes capacities of situational awareness that it does not, and cannot, fully specify. At the same time, autonomy or ‘self-direction’ in the case of machines requires the adequate specification (by human designers) of the conditions under which associated actions should be taken. This requirement for unambiguous specification of condition/action rules marks a crucial difference between autonomy as a legally accountable human capacity, and machine autonomy. The requirement for situational awareness in the context of combat, as a prerequisite for action that adheres to IHL, raises serious doubts regarding the feasibility of lawful autonomy in weapon systems

Self-awareness in autonomous automotive systems

Self-awareness has been used in many research fields in order to add autonomy to computing systems. In automotive systems, we face several system layers that must be enriched with self-awareness to build truly autonomous vehicles. This includes functional aspects like autonomous driving itself, its integration on the hardware/software platform, and among others dependability, real-time, and security aspects. However, self-awareness mechanisms of all layers must be considered in combination in order to build a coherent vehicle self-awareness that does not cause conflicting decisions or even catastrophic effects. In this paper, we summarize current approaches for establishing self-awareness on those layers and elaborate why self-awareness needs to be addressed as a cross-layer problem, which we illustrate by practical examples

Self-aware computing systems:from psychology to engineering

At the current time, there are several fundamental changes in the way computing systems are being developed, deployed and used. They are becoming increasingly large, heterogeneous, uncertain, dynamic and decentralised. These complexities lead to behaviours during run time that are difficult to understand or predict. One vision for how to rise to this challenge is to endow computing systems with increased self-awareness, in order to enable advanced autonomous adaptive behaviour. A desire for self-awareness has arisen in a variety of areas of computer science and engineering over the last two decades, and more recently a more fundamental understanding of what self-awareness concepts might mean for the design and operation of computing systems has been developed. This draws on self-awareness theories from psychology and other related fields, and has led to a number of contributions in terms of definitions, architectures, algorithms and case studies. This paper introduces some of the main aspects of self-awareness from psychology, that have been used in developing associated notions in computing. It then describes how these concepts have been translated to the computing domain, and provides examples of how their explicit consideration can lead to systems better able to manage trade-offs between conflicting goals at run time in the context of a complex environment, while reducing the need for a priori domain modelling at design or deployment time

An intelligent security system for autonomous cars based on infrared sensors

Safety and non-safety applications in the external communication systems of self-driving vehicles require authentication of control data, cooperative awareness messages and notification messages. Traditional security systems can prevent attackers from hacking or breaking important system functionality in autonomous vehicles. This paper presents a novel security system designed to protect vehicular ad hoc networks in self-driving and semi-autonomous vehicles that is based on Integrated Circuit Metric technology (ICMetrics). ICMetrics has the ability to secure communication systems in autonomous vehicles using features of the autonomous vehicle system itself. This security system is based on unique extracted features from vehicles behaviour and its sensors. Specifically, features have been extracted from bias values of infrared sensors which are used alongside semantically extracted information from a trace file of a simulated vehicular ad hoc network. The practical experimental implementation and evaluation of this system demonstrates the efficiency in identifying of abnormal/malicious behaviour typical for an attack

Satellite Navigation for the Age of Autonomy

Global Navigation Satellite Systems (GNSS) brought navigation to the masses. Coupled with smartphones, the blue dot in the palm of our hands has forever changed the way we interact with the world. Looking forward, cyber-physical systems such as self-driving cars and aerial mobility are pushing the limits of what localization technologies including GNSS can provide. This autonomous revolution requires a solution that supports safety-critical operation, centimeter positioning, and cyber-security for millions of users. To meet these demands, we propose a navigation service from Low Earth Orbiting (LEO) satellites which deliver precision in-part through faster motion, higher power signals for added robustness to interference, constellation autonomous integrity monitoring for integrity, and encryption / authentication for resistance to spoofing attacks. This paradigm is enabled by the 'New Space' movement, where highly capable satellites and components are now built on assembly lines and launch costs have decreased by more than tenfold. Such a ubiquitous positioning service enables a consistent and secure standard where trustworthy information can be validated and shared, extending the electronic horizon from sensor line of sight to an entire city. This enables the situational awareness needed for true safe operation to support autonomy at scale.Comment: 11 pages, 8 figures, 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS

Aerodrome situational awareness of unmanned aircraft: an integrated self-learning approach with Bayesian network semantic segmentation

It is expected that soon there will be a significant number of unmanned aerial vehicles (UAVs) operating side-by-side with manned civil aircraft in national airspace systems. To be able to integrate UAVs safely with civil traffic, a number of challenges must be overcome first. This paper investigates situational awareness of UAVs’ autonomous taxiing in an aerodrome environment. The research work is based on a real outdoor experimental data collected at the Walney Island Airport, the United Kingdom. It aims to further develop and test UAVs’ autonomous taxiing in a challenging outdoor environment. To address various practical issues arising from the outdoor aerodrome such as camera vibration, taxiway feature extraction and unknown obstacles, we develop an integrated approach that combines the Bayesian-network based semantic segmentation with a self-learning method to enhance situational awareness of UAVs. Detailed analysis for the outdoor experimental data shows that the integrated method developed in this paper improves robustness of situational awareness for autonomous taxiing