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Intelligent and connected vehicle technology is in the ascendant. High-level autonomous driving places more stringent requirements on the accuracy and reliability of…
Intelligent and connected vehicle technology is in the ascendant. High-level autonomous driving places more stringent requirements on the accuracy and reliability of environmental perception. Existing research works on multitarget tracking based on multisensor fusion mostly focuses on the vehicle perspective, but limited by the principal defects of the vehicle sensor platform, it is difficult to comprehensively and accurately describe the surrounding environment information.
In this paper, a multitarget tracking method based on roadside multisensor fusion is proposed, including a multisensor fusion method based on measurement noise adaptive Kalman filtering, a global nearest neighbor data association method based on adaptive tracking gate, and a Track life cycle management method based on M/N logic rules.
Compared with fixed-size tracking gates, the adaptive tracking gates proposed in this paper can comprehensively improve the data association performance in the multitarget tracking process. Compared with single sensor measurement, the proposed method improves the position estimation accuracy by 13.5% and the velocity estimation accuracy by 22.2%. Compared with the control method, the proposed method improves the position estimation accuracy by 23.8% and the velocity estimation accuracy by 8.9%.
A multisensor fusion method with adaptive Kalman filtering of measurement noise is proposed to realize the adaptive adjustment of measurement noise. A global nearest neighbor data association method based on adaptive tracking gate is proposed to realize the adaptive adjustment of the tracking gate.
In this chapter, we draw on our study involving interviews with Australians who identify as current self-trackers to discuss why and how they monitor themselves. Our…
In this chapter, we draw on our study involving interviews with Australians who identify as current self-trackers to discuss why and how they monitor themselves. Our approach for analysing self-tracking practices is based on a sociomaterial perspective, viewing enactments of voluntary self-tracking as shifting heterogeneous assemblages, bringing together diverse actors who are both human and non-human. We use vignettes to illustrate the ways in which our participants enacted self-tracking and to identify some of the diverse meanings and motivations that mediate decisions to self-track and resultant uses of the information thus generated. We found that a varied range of self-tracking practices were taken up by our interviewees, including not only digital devices and methods, but also recording their details using pen-and-paper, or simply maintaining mental awareness and using memory. We identified several agential capacities in our participants’ accounts of why and how they monitor themselves. These capacities are interrelated, but can be loosely grouped under the headings of ‘self-improvement’, ‘exerting control’ and ‘identifying patterns and achieving goals’. They are motivators and facilitators of monitoring practices. The broader sociocultural contexts in which monitoring of the body/self is undertaken were also revealed in the participants’ accounts. These include ideas about the moral virtues of self-responsibility and the individual management of life circumstances to avoid chaos and risk, and the notion that monitoring practices can successfully achieve these virtues.
This chapter provides an insider perspective on the Quantified Self (QS) community. It is argued that the overall approach and methods used in the QS community have not…
This chapter provides an insider perspective on the Quantified Self (QS) community. It is argued that the overall approach and methods used in the QS community have not been adequately described. Consequently, the aim of the chapter is to give an account of the work performed by self-trackers in what we coin the 1-Person-Laboratory (1PL). Additionally, the chapter describes other aspects of the 1PL, for example the methods, procedures and instrumentation that are being used and the knowledge sharing taking place in the QS community. With a point of departure in empirical cases it is demonstrated how QS self-trackers put their own questions, observations and subjective experience front and centre by using their own instrumentation and data sets in their personal laboratories. In the 1PL, the causalities that are looked for are not aimed at generalisation to an entire population; on the contrary, the causal connections on the level of the person are essential for discovery by the individual.
The debate concerning the Quantified-Self Movement (QS) has been extremely polarised. As Tamar Sharon has pointed out, each aspect of the lifestyle promoted by Gary Wolf…
The debate concerning the Quantified-Self Movement (QS) has been extremely polarised. As Tamar Sharon has pointed out, each aspect of the lifestyle promoted by Gary Wolf and Kevin Kelly has provoked opposite reactions, generating a debate that revolves around some basic conceptual dichotomies: empowerment versus surveillance, self-awareness versus reductionism, and personalised healthcare versus disintegration of public assistance (Sharon, 2017). The aim of this chapter is to provide a critique of QS, namely an assessment of its limits and its (technological and social) conditions of possibility. In particular, the author’s analysis will focus on the relationship between technology and subjectivity, and its main theoretical framework will be Michel Foucault’s research on the notion of ‘care for the self’ (Foucault, 1986, 2005). Quantification is an essential and unescapable aspect of our present technological environment. The devices that make our onlife (Floridi, 2014) possible are connected with a complex technological system made of GPSs, satellites, computers, and networks. Health is no longer managed through a distinct set of practices within the limits of a well-defined space (the hospital or the ambulatory), but it rather becomes a dataset integrated into a system where all aspects of life (health, law, leisure, work, social relations) are treated and managed simultaneously. This technological condition implies a new form of cognitive and practical delegation (Ippolita, 2016; Morozov, 2013), which makes the very notion of ‘self-tracking’ at least problematic. Individuals do not track themselves anymore: on the contrary, they are tracked by prosthetic extensions of their own bodies. This, however, does not mean that they do nothing. Our digital devices require a specific set of practices, a determinate way of life. The author will argue that these practices are the product of design, understood as a specific way of conceiving and organising the interaction between subject and technical object (Flusser, 1999). Through our technological environment, design reshapes the social and political function of bodies, their interaction and the set of practices connected to them (Bratton, 2015; Dyer, 2016; Vial, 2014). Automated quantification is an aspect of our designed user experience. As such, this chapter discusses design as a key element to understand the role of quantification in our digital milieu. It analyses the QS movement as a specific way of responding to our new technological condition. The main research question will be the following: is QS to be regarded as a simple acceptance of a new form of delegated – and thus alienated – subjectivity, or is it a kind of practice that allows the subject to overcome his passivity, and to take part in the process through which quantification is designed and managed? Is it possible to understand QS as a technology of the self (Foucault, 1988, 2005)?
In this chapter, the concept, use, evolution, problems and implications of tracking techniques in tourism and hospitality research are addressed. First, the concept of…
In this chapter, the concept, use, evolution, problems and implications of tracking techniques in tourism and hospitality research are addressed. First, the concept of tracking is defined and its applications in different sciences and, particularly, in tourism and hospitality are explained. Then, the past, present and uncertain future of tracking techniques is briefly discussed, including the evolution of the different types of tools used to track the places visited by tourists. Afterward, this chapter continues pointing to the limitations of tracking tools and it points to combining different tracking techniques as a key element to gather more accurate data from tourists. Last, this chapter focuses on the implications of data gathered through tracking tools for destination and industry managers. This chapter may serve to students interested in understanding how the generation of tourism statistics is expected to evolve during next years and to practitioners pretending to improve the management of tourism destinations or enterprises.