Abstract
Path tracking is one of the most important aspects of autonomous vehicles. The current research focuses on designing path-tracking controllers taking into account the stability of the yaw and the nonholonomic constraints of the vehicle. In most cases, the lateral controller design relies on identifying a path reference point, the one with the shortest distance to the vehicle giving the current state of the vehicle. That restricts the controller’s ability to handle sudden changes of the trajectory heading angle. The present article proposes a new approach that imitates human behavior while driving. It is based on a discrete prediction model that anticipates the future states of the vehicle, allowing the use of the control algorithm in future predicted states augmented with the current controller output. The performance of the proposed approach is verified through several simulations on V-REP simulator with different types of maneuvers (double lane change, hook road, S road, and curved road) and a wide range of velocities. Predictive Stanley controller was used compared to the original Stanley controller. The obtained results of the proposed control approach show the advantage and the performance of the technique in terms of minimizing the lateral error and ensuring yaw stability by an average of 53% and 22%, respectively.
Highlights
Research in autonomous systems has shown promising results in substituting redundant efforts done by human beings with personal assistants at homes or factories
For the lateral control system, the new control approach or the proposed controller is compared to the basic Stanley (BS) controller through different types of maneuvers and velocities
A nonlinear discrete bicycle model is used as a prediction model, anticipating the future states of the vehicle
Summary
Research in autonomous systems has shown promising results in substituting redundant efforts done by human beings with personal assistants at homes or factories. Other efforts have been exerted for providing high-resolution images using simple segmentation maps or even producing hard maneuvers through obstacles at very high speed using different driving strategies and lateral control algorithms. A new control approach is developed, which imitates human behavior while driving on the basis of a discrete prediction model for anticipating the future states of the vehicle. With this in mind, the use of the control algorithm in future predicted states augmented with the current controller output will be allowed
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