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A Finite State Machine Based Automated Driving Controller and its Stochastic Optimization

[+] Author Affiliations
Mengxuan Zhang, Nan Li, Anouck Girard, Ilya Kolmanovsky

University of Michigan, Ann Arbor, MI

Paper No. DSCC2017-5209, pp. V002T07A002; 10 pages
doi:10.1115/DSCC2017-5209
From:
  • ASME 2017 Dynamic Systems and Control Conference
  • Volume 2: Mechatronics; Estimation and Identification; Uncertain Systems and Robustness; Path Planning and Motion Control; Tracking Control Systems; Multi-Agent and Networked Systems; Manufacturing; Intelligent Transportation and Vehicles; Sensors and Actuators; Diagnostics and Detection; Unmanned, Ground and Surface Robotics; Motion and Vibration Control Applications
  • Tysons, Virginia, USA, October 11–13, 2017
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5828-8
  • Copyright © 2017 by ASME

abstract

In this paper, we develop a finite state machine based automated highway driving controller. The controller is described by feedback control laws in each state and state transition conditions. We test the controller in a traffic simulator and evaluate its performance based on a metric function. Furthermore, we propose a stochastic gradient based optimization approach to achieve optimal calibration of the developed controller. We expect that this controller can serve as a baseline for automated driving algorithm developments.

Copyright © 2017 by ASME

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