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Abstract
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Introduction
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Discussion
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Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey

Profile image of Anthony PerryAnthony Perry

IEEE Access

https://doi.org/10.1109/ACCESS.2019.2902812
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26 pages

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Abstract

Autonomous Vehicles are becoming a reality in places with advanced infrastructure to support their operations. In crowded places, harsh environments, missions that require these vehicles to be aware of the context in which they are operating, and situations requiring continuous coordination with humans such as in disaster relief, Advanced-Vehicle Systems (AVSs) need to be better contextually aware. The vast literature referring to "context-aware systems" is still sparse, focusing on very limited forms of contextual awareness. It requires a structured approach to bring it together to truly realise contextual awareness in AVSs. This paper uses a Human-AVSs (HAVSs) lens to polarise the literature in a coherent form suitable for designing distributed HAVSs. We group the relevant literature into two categories: contextual-awareness related to the vehicle infrastructure itself that enables AVSs to operate, and contextualawareness related to HAVSs. The former category focuses on the communication backbone for AVSs including ad-hoc networks, services, wireless communication, radio systems, and the cyber security and privacy challenges that arise in these contexts. The latter category covers recommender systems, which are used to coordinate the actions that sit at the interface of the human and AVSs, human-machine interaction issues, and the activity recognition systems as the enabling technology for recommender systems to operate autonomously. The structured analysis of the literature has identified a number of open research questions and opportunities for further research in this area.

Figures (8)
Fernandez-Rojas et al.: Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey FIGURE |: The three building blocks for situation awareness as a function of the information available to an agent through its sensors. Jj,4n 1S the minimum information required to form a context. The Invariance operator acts on Im;n Over time to identify situations, while the Interpretation operator transforms digital information into meaning forming the awareness of an agent. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: D qo oe) 10.1109/ACCESS.2019.2902812, IEEE Access
Fernandez-Rojas et al.: Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey FIGURE |: The three building blocks for situation awareness as a function of the information available to an agent through its sensors. Jj,4n 1S the minimum information required to form a context. The Invariance operator acts on Im;n Over time to identify situations, while the Interpretation operator transforms digital information into meaning forming the awareness of an agent. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: D qo oe) 10.1109/ACCESS.2019.2902812, IEEE Access
TABLE |: Examples of frameworks from the literature for context-aware computing. With the increasing ease of deployment of various sensors and improvements in data-acquisition techniques, it comes as no surprise that a number of studies in recent years have focussed on decentralized and distributed systems, studying various aspects, such as distributed reasoning [31 ]—[33], con- text sharing [34], [35], distributed infrastructure [36], and distributed cognition [35], [37]-[44]. Acquisition is the cornerstone of context knowledge and how systems can know or reason about their context [45]. Context acquisition in distributed systems can be realised and explained in different ways. Although, sensors are seen as tangible hardware devices, they can be referred as a data source to acquire context information [24], [46]. For instance, Hofer et al. [13] defined two context dimensions for learning context, physical and logical context. The physical context involves context that can be obtained by hardware sensors, such as temperature, altitude, pressure, etc. The logical acquisition refers to contextual information gained
TABLE |: Examples of frameworks from the literature for context-aware computing. With the increasing ease of deployment of various sensors and improvements in data-acquisition techniques, it comes as no surprise that a number of studies in recent years have focussed on decentralized and distributed systems, studying various aspects, such as distributed reasoning [31 ]—[33], con- text sharing [34], [35], distributed infrastructure [36], and distributed cognition [35], [37]-[44]. Acquisition is the cornerstone of context knowledge and how systems can know or reason about their context [45]. Context acquisition in distributed systems can be realised and explained in different ways. Although, sensors are seen as tangible hardware devices, they can be referred as a data source to acquire context information [24], [46]. For instance, Hofer et al. [13] defined two context dimensions for learning context, physical and logical context. The physical context involves context that can be obtained by hardware sensors, such as temperature, altitude, pressure, etc. The logical acquisition refers to contextual information gained
TABLE 2: Number of papers resultant from each stage of the literature search.
TABLE 2: Number of papers resultant from each stage of the literature search.
FIGURE 2: The number of papers forming the pool of papers for this survey distributed over the last 10 years. number of papers per year selected to form the pool of papers for this survey is shown in Figure 2.
FIGURE 2: The number of papers forming the pool of papers for this survey distributed over the last 10 years. number of papers per year selected to form the pool of papers for this survey is shown in Figure 2.
Fernandez-Rojas et a/.: Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey IGURE 3: Structure of the survey presenting contextual awareness for human-vehicle interaction.
Fernandez-Rojas et a/.: Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey IGURE 3: Structure of the survey presenting contextual awareness for human-vehicle interaction.
Fernandez-Rojas et a/.: Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey FIGURE 4: The relationship between different functions required for contextual awareness and action production.
Fernandez-Rojas et a/.: Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey FIGURE 4: The relationship between different functions required for contextual awareness and action production.
FIGURE 5: An overall synthesis of the benefits, use and means of contextual awareness. awareness and the opportunity to optimize the overall system performance? There are two types of cost with relevance to this discussion: computational and energy costs. The latter depends on the former, albeit not sole resources for representing, learning and y. Contextual awareness demand s computational optimising con- texts. Moreover, the fusion component of the system needs to be adaptive to form a general hypothesis gener- ation module that is not restricted to a preconceived set of fusion functions. This mode of continuous discovery needs to compete against all other functions within HAVSs for computational resources. This competition along with the computational resources required to im- prove the contextual awareness of a system increase energy consumption.
FIGURE 5: An overall synthesis of the benefits, use and means of contextual awareness. awareness and the opportunity to optimize the overall system performance? There are two types of cost with relevance to this discussion: computational and energy costs. The latter depends on the former, albeit not sole resources for representing, learning and y. Contextual awareness demand s computational optimising con- texts. Moreover, the fusion component of the system needs to be adaptive to form a general hypothesis gener- ation module that is not restricted to a preconceived set of fusion functions. This mode of continuous discovery needs to compete against all other functions within HAVSs for computational resources. This competition along with the computational resources required to im- prove the contextual awareness of a system increase energy consumption.
fNIRS and EEG.
fNIRS and EEG.

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Andry Rakotonirainy

2005

This paper argues that the driving task exhibits the properties of complex systems. Driving behavior emerges from the intricate and complex interactions between the driver, the vehicle and the environment. An emerging driving behavior could not necessary be linearly predicted. However a context-aware system could assist the driver in augmenting the probability of undertaking safe behavior. Unlike existing context aware systems which isolate one characteristic such as road or driver workload and concentrate on it in exclusion of the others factors, this seminal research proposes a context awareness design which considers the driver, vehicle and environment as a whole. We focus on the principles that underlie the system in order to model it with the view of understanding, predicting and improving driver behavior. This approach places context-aware design within a wider framework that takes into account information related to the driver, environment and vehicle. It recommends approaches for building safety critical contextaware systems. Such an approach aids in the design of safe in-vehicle context-aware systems.

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Context-Aware Driver Assistance System
Tarek Sheltami

Procedia Computer Science, 2012

In the last few years, significant improvements have been made in the area of vehicular communication systems. In fact, vehicle-to-vehicle communication and vehicle-to-infrastructure are considered a key concept for keeping roads safe. An efficient implementation of these systems is necessary to ensure the safety and to reduce the car collision rates. This paper proposes a Context-Aware Driver Assistance System that links drivers with the physical environment surrounding them. This is achieved by developing a vehicular warning system that assists drivers to avoid collisions and improve their response times. The proposed system architecture consists of a set of components to process the user's request, and provide responses and advices when needed. These components include communication, knowledge exchange, knowledge update, and context-history. Also, it includes other processes such as context-history manipulation, hazard detection, and hazard detection control. Over all, the main goal of the system is to reduce the number of car accidents and improve driver's decisions. To demonstrate the feasibility of the proposed system, it is developed and demonstrated on NXT Robots environment.

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Design of context-aware systems for vehicles using complex system paradigms
Andry Rakotonirainy

Conference on Modeling and Using Context, 2005

This paper argues that the driving task exhibits the prop- erties of complex systems. Driving behavior emerges from the intricate and complex interactions between the driver, the vehicle and the envi- ronment. An emerging driving behavior could not necessary be linearly predicted. However a context-aware system could assist the driver in augmenting the probability of undertaking safe behavior. Unlike exist-

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Beyond context-awareness: Driver-vehicle-environment adaptivity. from the comunicar project to the aide concept
Francesco Bellotti

2005

Abstract: This paper presents the principle of the design of the “intelligent core” of next generation driver vehicle interaction systems towards the objective to obtain a safe and sustainable mobility. Mobility in the future has to be characterised by a reduction both in number and in severity of accidents, has to facilitate the movement of every user and should be promoted towards “intermodality” to reduce traffic congestion and optimise travel planning. This causes an increasing demand for on board information systems.

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Cited by

Human Performance Operating Picture for Shepherding a Swarm of Autonomous Vehicles
Essam Debie

2020

Due to many factors that range from ethical considerations and accountability to technological imperfection in autonomous systems, humans will continue to be an integral part of any meaningful autonomous system. While shepherding offers a technological concept that allows a human to operate a significantly larger number of autonomous systems that a human can handle in today's environment, it is important to realise that a significant amount of accidents today are due to human error. The scalability promise that shepherding offers comes with possible challenges including those arising from the cognitive load imposed on human operators and the need to smoothly integrate the human, as a biological autonomous system, with the wider multi-agent autonomous system of future operating environments. In this chapter, we bring together the dimensions of this complex problem. We present carefully selected factors to cover human performance, especially for cognitively demanding tasks and situation awareness, and how these factors contribute to trust in the system. We then present the Human Factors Operating Picture (H-FOP), which offers a real-time situation awareness picture on human performance in this complex environment.

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A systematic review of neurophysiological sensing for the assessment of acute pain
Roland Goecke

npj digital medicine, 2023

Pain is a complex and personal experience that presents diverse measurement challenges. Different sensing technologies can be used as a surrogate measure of pain to overcome these challenges. The objective of this review is to summarise and synthesise the published literature to: (a) identify relevant non-invasive physiological sensing technologies that can be used for the assessment of human pain, (b) describe the analytical tools used in artificial intelligence (AI) to decode pain data collected from sensing technologies, and (c) describe the main implications in the application of these technologies. A literature search was conducted in July 2022 to query PubMed, Web of Sciences, and Scopus. Papers published between January 2013 and July 2022 are considered. Forty-eight studies are included in this literature review. Two main sensing technologies (neurological and physiological) are identified in the literature. The sensing technologies and their modality (unimodal or multimodal) are presented. The literature provided numerous examples of how different analytical tools in AI have been applied to decode pain. This review identifies different non-invasive sensing technologies, their analytical tools, and the implications for their use. There are significant opportunities to leverage multimodal sensing and deep learning to improve accuracy of pain monitoring systems. This review also identifies the need for analyses and datasets that explore the inclusion of neural and physiological information together. Finally, challenges and opportunities for designing better systems for pain assessment are also presented.

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