Abstract
Among the most important characteristics of autonomous vehicles are the safety and robustness in various traffic situations and road conditions. In this paper, we focus on the development and analysis of the extended version of the canonical proportional-derivative PD controllers that are known to provide a good quality of steering on non-slippery (dry) roads. However, on slippery roads, due to the poor yaw controllability of the vehicle (suffering from understeering and oversteering), the quality of control of such controllers deteriorates. The proposed predicted PD controller (PPD controller) overcomes the main drawback of PD controllers, namely, the reactiveness of their steering behavior. The latter implies that steering output is a direct result of the currently perceived lateral- and angular deviation of the vehicle from its intended, ideal trajectory, which is the center of the lane. This reactiveness, combined with the tardiness of the yaw control of the vehicle on slippery roads, results in a significant lag in the control loop that could not be compensated completely by the predictive (derivative) component of these controllers. In our approach, keeping the controller efforts at the same level as in PD controllers by avoiding (i) complex computations and (ii) adding additional variables, the PPD controller shows better quality of steering than that of the evolved (via genetic programming) models.
Highlights
Every year, the demand for autonomously controlled road motor vehicles is rising, and they could be used both as a taxi [1] or as personal cars.the demand for precise control models that provide the safest and fastest transit of the passengers to their destinations is growing
In order to address the above-mentioned challenges of the canonical PD controllers, in which the control output is calculated as a weighted sum of the control errors and their derivatives, in our previous research we proposed a PID steering controller featuring an arbitrary internal structure, developed heuristically via genetic programming [6]
By using the predicted value of just one perceived variable, pertinent to the state of the car—the lateral deviation from the center of the road—we demonstrated that the quality of steering of the car on slippery roads could be significantly improved with the same set of perception information of the controller; yet, assuming the availability of the map of the road ahead
Summary
Every year, the demand for autonomously controlled road motor vehicles (hereafter referred to as cars) is rising, and they could be used both as a taxi [1] or as personal cars.the demand for precise control models that provide the safest and fastest transit of the passengers to their destinations is growing. When humans drive the car, we dynamically adapt our steering behavior depending on the features of the car (e.g., length, width, mass,) and the road conditions (dry, wet, snowy, etc.) in a way that is difficult to mimic in both PD and PID controllers due to their hard structure with a small number of variables. Anotherthought thought that mentioned before in the Section is a special the trajectory that has a linear curvature profile, which is comfortable for the turning car with high speed. In order to compare the obtained by PPD controller trajectory shape with clothoid, we combined speed. Clothoid be constructed by solving trajectory the systemshape of differential equations In aorder compare thecould obtained by PPD controller with clothoid, we below, called the “reconstruction equation”. A clothoid is uniquely defined by: Coordinates and heading at which it starts: (x0 , y0 , θ0 ); length
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