How to keep drivers engaged while supervising driving automation? A literature survey and categorization of six solution areas

This work aimed to organise recommendations for keeping people engaged during human supervision of driving automation, encouraging a safe and acceptable introduction of automated driving systems. First, heuristic knowledge of human factors, ergonomics, and psychological theory was used to propose solution areas to human supervisory control problems of sustained attention. Driving and non-driving research examples were drawn to substantiate the solution areas. Automotive manufacturers might (1) avoid this supervisory role altogether, (2) reduce it in objective ways or (3) alter its subjective experiences, (4) utilize conditioning learning principles such as with gamification and/or selection/training techniques, (5) support internal driver cognitive processes and mental models and/or (6) leverage externally situated information regarding relations between the driver, the driving task, and the driving environment. Second, a cross-domain literature survey of influential human-automation interaction research was conducted for how to keep engagement/attention in supervisory control. The solution areas (via numeric theme codes) were found to be reliably applied from independent rater categorisations of research recommendations. Areas (5) and (6) were addressed by around 70% or more of the studies, areas (2) and (4) in around 50% of the studies, and areas (3) and (1) in less than around 20% and 5%, respectively. The present contribution offers a guiding organisational framework towards improving human attention while supervising driving automation.submittedVersio

The Balance Between Privacy and Safety in Police UAV Use: The Power of Threat and Its Effect on People’s Receptivity

Unmanned aerial vehicles (UAVs), also known as drones, are an innovative technology that has received significant interest from the law enforcement community. The size and ability, technological capability, and cost effectiveness of UAVs make them an attractive tool for law enforcement agencies to utilize in the course of operations, including domestic surveillance. Despite the potential benefits to the society, public perception of police UAV use is mixed, and “Not Over My Backyard (NOMBY)” attitudes relevant to Fourth Amendment privacy concerns are consistently demonstrated across studies related to public perceptions on this emerging technology. The present study focuses on the relative impact of privacy threats and other situational factors on individuals’ perceptions of police and their use of UAV technology. Using Stephan and Renfro’s revised reintegrated threat theory (2002), the present research used a scenario- based experimental design to examine: (1) the impact perceived threat from police UAV use on people’s attitudes toward police and their use of UAVs? (2) the attitudinal differences of the degree of participants’ connection to the target of surveillance, and (3) the effect of the people’s pre-existing perceptions of police on participants’ attitudinal differences, and (4) the structural relationships, followed by the theory, between perceived threats, antecedents (i.e., relations between groups, individual difference variables, cultural dimensions, situational factors) to intergroup threat, and the people’s perceptions, as well as demographic or other socio-economic factors. The findings provide some significant socio-psychological implications concerning police-community intergroup relations. First, the quality of the interpersonal treatment or relations (i.e., individual differences) they had previously received from police officers was the strongest indicator in predicting their attitudes toward police UAV use. Second, the outcome of UAV activity also influenced their evaluations of police. Lastly, people’s attitudes were more extreme when the level of connection to the target of surveillance was farther away from them and it was interacted with policing strategies (i.e., reactive v proactive policing)

Military Unmanned Aerial Vehicle Operations Through the lens of a high reliability system: Challenges and Opportunities

This study examines the impact of regulations and standard procedures on safety outcomes in unmanned aerial vehicle (UAV) operations, specifically focussing on Norwegian military UAV systems, from a high reliability organization (HRO) perspective. By analysing data from existing regulations, accident reports, and interviews with military drone pilots using thematic analysis, we identify key recurring themes. Our findings highlight the importance of fatigue and exhaustion due to the absence of regulations on resting time for military drone pilots. This poses substantial risks and increases the likelihood of accidents and incidents in UAV operations. Additionally, we uncover gaps in safety reporting and accountability for military UAV pilots, indicating the need for improved reporting procedures that consider the unique operational elements of UAVs. Effective communication between stakeholders, including drone pilots, ground crew, and air traffic controllers, emerges as a critical factor in maintaining situational awareness. This emphasis on communication is consistent with HRO principles and supports the essential safety tasks of UAV pilots, namely sense-making, decision making, and performance. By uncovering the impact of regulations and operational procedures on safety outcomes and addressing fatigue in UAV operations, this research contributes to enhancing the safety and reliability of Norwegian military UAV systems.publishedVersio

A Review of Automatic Classification of Drones Using Radar:Key Considerations, Performance Evaluation and Prospects

Automatic target classification or recognition is a critical capability in non-cooperative surveillance with radar in several defence and civilian applications. It is a well-established research field and numerous techniques exist for recognising targets, including miniature unmanned air systems or drones (i.e., small, mini, micro and nano platforms), from their radar signatures. These algorithms have notably benefited from advances in machine learning (e.g., deep neural networks) and are increasingly able to achieve remarkably high accuracies. Such classification results are often captured by standard, generic, object recognition metrics and originate from testing on simulated or real radar measurements of drones under high signal to noise ratios. Hence, it is difficult to assess and benchmark the performance of different classifiers under realistic operational conditions. In this paper, we first review the key challenges and considerations associated with the automatic classification of miniature drones from radar data. We then present a set of important performance measures, from an end-user perspective. These are relevant to typical drone surveillance system requirements and constraints. Selected examples from real radar observations are shown for illustration. We also outline here various emerging approaches and future directions that can produce more robust drone classifiers for radar

Adoption of vehicular ad hoc networking protocols by networked robots

This paper focuses on the utilization of wireless networking in the robotics domain. Many researchers have already equipped their robots with wireless communication capabilities, stimulated by the observation that multi-robot systems tend to have several advantages over their single-robot counterparts. Typically, this integration of wireless communication is tackled in a quite pragmatic manner, only a few authors presented novel Robotic Ad Hoc Network (RANET) protocols that were designed specifically with robotic use cases in mind. This is in sharp contrast with the domain of vehicular ad hoc networks (VANET). This observation is the starting point of this paper. If the results of previous efforts focusing on VANET protocols could be reused in the RANET domain, this could lead to rapid progress in the field of networked robots. To investigate this possibility, this paper provides a thorough overview of the related work in the domain of robotic and vehicular ad hoc networks. Based on this information, an exhaustive list of requirements is defined for both types. It is concluded that the most significant difference lies in the fact that VANET protocols are oriented towards low throughput messaging, while RANET protocols have to support high throughput media streaming as well. Although not always with equal importance, all other defined requirements are valid for both protocols. This leads to the conclusion that cross-fertilization between them is an appealing approach for future RANET research. To support such developments, this paper concludes with the definition of an appropriate working plan

Service Provisioning in Edge-Cloud Continuum Emerging Applications for Mobile Devices

Disruptive applications for mobile devices can be enhanced by Edge computing facilities. In this context, Edge Computing (EC) is a proposed architecture to meet the mobility requirements imposed by these applications in a wide range of domains, such as the Internet of Things, Immersive Media, and Connected and Autonomous Vehicles. EC architecture aims to introduce computing capabilities in the path between the user and the Cloud to execute tasks closer to where they are consumed, thus mitigating issues related to latency, context awareness, and mobility support. In this survey, we describe which are the leading technologies to support the deployment of EC infrastructure. Thereafter, we discuss the applications that can take advantage of EC and how they were proposed in the literature. Finally, after examining enabling technologies and related applications, we identify some open challenges to fully achieve the potential of EC, and also research opportunities on upcoming paradigms for service provisioning. This survey is a guide to comprehend the recent advances on the provisioning of mobile applications, as well as foresee the expected next stages of evolution for these applications

Management system for Unmanned Aircraft Systems teams

This thesis investigates new schemes to improve the operability of heterogeneous Unmanned Aircraft Systems (UAS) teams through the exploitation of inter-vehicular communications. Releasing ground links from unnecessary data exchanges saves resources (power, bandwidth, etc) and alleviates the inherent scalability problem resulting from the increase in the number of UAS to be controlled simultaneously. In first place, a framework to classify UAS according to their level of autonomy is presented along with efficient methodologies to assess the autonomy level of either individual or multiple UAS. An architecture based on an aerial Mobile Ad-hoc Network (MANET) is proposed for the management of the data exchange among all the vehicles in the team. A performance evaluation of the two most relevant MANET approaches for path discovery (namely, reactive and proactive) has been carried out by means of simulation of two well-known routing protocols: Ad-hoc On-demand Distance Vector (AODV) and Destination Sequenced Distance Vector (DSDV). Several network configurations are generated to emulate different possible contingencies that might occur in real UAS team operations. Network topology evolution, vehicle flight dynamics and data traffic patterns are considered as input parameters to the simulation model. The analysis of the system behaviour for each possible network configuration is used to evaluate the appropriateness of both approaches in different mission scenarios. Alternative network solutions based on Delay Tolerant Networking (DTN) for situations of intermittent connectivity and network partitioning are outlined. Finally, an assessment of the simulation results is presented along with a discussion about further research challenges