A Fractional Derivative-Based Lateral Preview Driver Model for Autonomous Automobile Path Tracking

<div>The vehicle responses at bend 1 in the case of driver models with different orders, <svg height="8.99888pt" id="M72" style="vertical-align:-3.18151pt" version="1.1" viewbox="-0.0498162 -5.81737 10.8789 8.99888" width="10.8789pt" 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="M219 86C216 168 211 250 206 337C201 410 189 448 163 448C131 448 79 396 43 344L60 322C91 359 110 375 118 375S132 358 136 298C141 238 152 81 155 -12H182C242 62 331 177 390 258C435 321 451 360 451 391C450 424 432 448 408 448C390 448 372 435 366 419C362 410 362 401 366 394C373 383 376 367 376 350C376 283 262 138 221 86H219Z" id="g185-40"></path></g><g transform="matrix(.0091,0,0,-0.0091,5.811,3.132)"><path d="M389 0V32C297 38 291 46 291 118V635C234 613 175 595 109 583V556L161 554C203 552 207 547 207 497V118C207 46 201 38 110 32V0H389Z" id="g50-50"></path></g></svg>=30m/s. (a) The yaw rate responses. (b) The lateral speed responses.</div>

Mathematical Problems in Engineering

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Figure 11

Figure 11: A Fractional Derivative-Based Lateral Preview Driver Model for Autonomous Automobile Path Tracking 