2018
DOI: 10.1007/978-981-10-6571-2_69
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Cooperative Vehicle Sensing and Obstacle Avoidance for Intelligent Driving Based on Bayesian Frameworks

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(2 citation statements)
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“…It should be emphasized that in the literature of wireless sensor networks (WSNs), some target tracking algorithms [23][24][25] have been developed for simultaneous localization and tracking (SLAT). In [24], a Bayesian filtering framework was proposed to infer the joint posterior distribution of both target and multiple sensors.…”
Section: Introductionmentioning
confidence: 99%
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“…It should be emphasized that in the literature of wireless sensor networks (WSNs), some target tracking algorithms [23][24][25] have been developed for simultaneous localization and tracking (SLAT). In [24], a Bayesian filtering framework was proposed to infer the joint posterior distribution of both target and multiple sensors.…”
Section: Introductionmentioning
confidence: 99%
“…Variational method [26] was used to approximate the joint state during the measurements correction stage. In [25], a dynamic non-parametric belief propagation (DNBP) method was proposed for cooperative vehicle sensing. However, most works in SLAT tend to track one target by using multiple static or moving sensors, thus, restricting their application to more complex scenarios where the number of targets can vary at different times.…”
Section: Introductionmentioning
confidence: 99%