In a human operated vehicle, the alignment of tires aims to strike a balance between ease of steering and a minimization of tire wear. The replacement of the human driver in an autonomous vehicle with low latency computer control of path tracking means that tire alignment can be performed with less emphasis on handling characteristics which contribute to ease of steering and directed towards improvement in tire life. This study uses MATLABs Vehicle Dynamics Blockset and Predictive Driver block to compare the path tracking capability of a passenger vehicle performing a double lane change maneuver under the control of the pure pursuit autonomous path following algorithm as well as a simulated human driver. Validation of the Predictive Driver block is performed by tracking a panel of human drivers performing the double lane change maneuver using GPS for localization in a subcompact electric vehicle. The vehicle model is characterized based on measurements from the test vehicle and sent through the same double lane change in simulation to compare behaviors. Tire alignment parameters are altered to demonstrate their effects on vehicle handling under both types of vehicle control. In the simulation environment, the pure pursuit algorithm tracks the desired path consistently across all parameter variations while the simulated human driver varies in its path tracking capabilities.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.