An Online Semisupervised Learning Model for Pedestrians’ Crossing Intention Recognition of Connected Autonomous Vehicle Based on Mobile Edge Computing Applications

<div>The impact of the size of <svg height="11.927pt" id="M98" style="vertical-align:-3.291101pt" version="1.1" viewbox="-0.0498162 -8.6359 10.4483 11.927" width="10.4483pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M600 480C600 590 528 650 384 650H143L137 622C222 614 225 607 210 531L130 127C113 41 106 36 23 28L17 0H294L300 28C204 36 195 42 212 127L243 284L314 263C327 263 339 263 352 264C465 271 600 337 600 480ZM508 481C508 351 402 304 329 304C289 304 265 311 250 317L295 559C302 594 310 606 323 611C335 616 350 619 367 619C455 619 508 573 508 481Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,6.656,3.132)"><path d="M329 433H203L239 587L230 596L147 534L123 433H57L30 395L34 388H115L61 129C37 16 59 -12 85 -12C147 -12 222 58 260 98L241 125C212 95 160 62 144 62C132 62 127 71 138 126L192 386L305 394L329 433Z"></path></g></svg> on the error rate of the algorithm.</div>

Wireless Communications and Mobile Computing

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Figure 12

Figure 12: An Online Semisupervised Learning Model for Pedestrians’ Crossing Intention Recognition of Connected Autonomous Vehicle Based on Mobile Edge Computing Applications 

Figure 12 | An Online Semisupervised Learning Model for Pedestrians’ Crossing Intention Recognition of Connected Autonomous Vehicle Based on Mobile Edge Computing Applications 