Abstract
An autonomous vehicle controller based on BP-PID (Back-Propagation - Proportion Integration Differentiation) is designed. Autonomous control is one of the most significant parts for self-driving. Traditional PID control is supposed to take conducive effects on the longitudinal control; nevertheless, it fails to ensure the lateral control stability due to the inflexible parameters setting. To cover the limitation of in-system parameters adjustment issue, the BP (Back-Propagation) neural network is adhibited in traditional PID lateral control. The BP-PID control module updates the incremental PID parameters through self-learning and makes the vehicle operates more smoothly. The learning algorithm flowchart and calculation method of parameters are provided. Moreover, dual mode (manual mode and autonomous mode) control will continue for a comparatively long period. Consequently, the dual mode switch algorithm is presented. A typical measurement is conducted which were then compared with the ordinary PID control results that verified the potential of the proposed method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IOP Conference Series: Earth and Environmental Science
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
