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Driver telematics analysis
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Driver telematics analysis
1.
DRIVER TELEMATICS ANALYSIS Use
telematic data to identify a driver signature
2.
乂仂 舒从亠 driver
telematics? 舒于仂仄仂弍仍 仗仂仄舒 仗亠舒仍仆亳亶 仗亳仍舒亟 亳亶 于亳亰仆舒舒 仆舒从仍从亳 弍亠亰仗亠仆仂 于仂亟亶 于仂亟亳 仆舒亟亳仍舒 亟舒仆 舒仂于亶 从仂仄仗舒仆 丱仂仂 于仂亟 亠从仂仆仂仄仍 亞仂 仆舒 舒于舒仆仆 仂亞舒仆 于仂亟 仆亠 亠从仂仆仂仄仍 弌舒仂于舒 从仂仄仗舒仆 亳亰亳从 仄亠仆亠 . Profit!!!
4.
仂弍仍亠仄舒 aka 仂舒仆仂于从舒
亰舒亟舒 舒于仂 仗仍舒亳亳 亰舒 仗亳亶.丼亳 仆亠 仄仂亢仆舒 于亳从仂亳舒亳GPS 磻亳亶 于 舒弍仂仍ム仆仂 弍仍仂 于仂亟于 DRIVER FINGERPRINT
5.
舒仆 2736 于仂亟于
亟仍 从仂亢仆仂亞仂 亟舒仆 200-舒 仗仂亟仂仂亢亠亶 (仂亠从仆亟仆 亰亰亳 仗仂亰亳) 亰 亳 200 仗仂亟仂仂亢亠亶 仗亠于仆舒 从仍从 仆亠 仆舒仍亠亢舒 于亟仗仂于亟仆仂仄 于仂亟 GOAL: 于亳亰仆舒亳亳 亶仄仂于仆 仆舒仍亠亢仆仂 从仂亢仆仂 仗仂亟仂仂亢 亟仂 于亟仗仂于亟仆仂亞仂 亶 于仂亟
6.
Supervised 仗亟亟 仂亰亟从亳 于仂亟
仂 仆舒 从舒于亳 亳仗舒亟从仂于 仗仂亰亟从亳 仆亳 于仂亟于 从舒: 1 从舒: 0 仍从舒 仗仂亳 仂亰仆舒从 + SVM
7.
Unsupervised 仗亟亟 Anomaly/Outlier
detection: GMM + EM
8.
Supervised again Gradient boosted
regression trees 仄亠仆 仍亳于 亟仂 outlier 于 于亳弍 弍仍 亠亠从亳于仆 亟仂 仆亠亰弍舒仍舒仆仂于舒仆亳 从仍舒于 仂于 仂亰仆舒从亳: 仂亰仗仂亟仍 于亳亟从仂 仂亰仗仂亟仍 仗亳从仂亠仆仆/亞舒仍仄于舒仆仆 仗仂 于亳亟从仂 仂亰仗仂亟仍 于亟亠仆仂于仂亞仂 仗亳从仂亠仆仆 舒 亰仗亳仆仂从
9.
亳从仍亳从亳 舒 仆亠于亟舒
仂从舒仍仆舒 仂仆从舒 亠亰仍舒于 Kalman filter 仍亳亠 仗仂亞亳于 亠亰仍舒亳 Principal component analysis ~
10.
亳仆仂于从亳 亰舒亞舒于 亰
舒仍亞仂亳仄舒仄亳 亰舒仄 仂亞仂 仂弍 亟仂仍亟亢于舒亳 仗亳舒亳 弍仍 仆亟亳从舒亳于仆 仂亰仆舒从亳 AXA 仆舒亟舒仍舒 仆亠 亟亢亠 磻仆 亟舒仆 弍舒从 亟仂于亟 舒 舒
11.
磻 亰舒 于舒亞!
Editor's Notes
仄 仗亳于! 丐亠仄舒 仄仂亞仂 从仂于仂亞仂 仗仂亠从舒 舒仆舒仍亰 亠仍亠仄舒亳从亳 于仂亟.
亂 弌弌 丿 亂 弌丐丐 弌丱 丱 乂弌亂 丶
PCI
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