Regression-based supervised learning of autosteering of a road car featuring a delayed steering response

Nikulin, Vsevolod; Podusenko, Albert; Tanev, Ivan; Shimohara, Katsunori

doi:10.1007/s41060-018-0140-z

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…It represents the canonical approach of defining the instant value of the steering angle in autonomous cars [23,24]. According to this model, the SAF that defines the steering angle δ is assumed to be the sum of the following two scaled parameters: the linear e and angular θ deviations of the car from its desired trajectory (e.g., the center of the lane) as illustrated in Figure 3.…”

Section: Servo Control As a Pd Controllermentioning

confidence: 99%

Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions

Алексеева¹,

Tanev²,

Shimohara³

2018

Algorithms

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Abstract:The most important characteristics of autonomous vehicles are their safety and their ability to adapt to various traffic situations and road conditions. In our research, we focused on the development of controllers for automated steering of a realistically simulated car in slippery road conditions. We comparatively investigated three implementations of such controllers: a proportional-derivative (PD) controller built in accordance with the canonical servo-control model of steering, a PID controller as an extension of the servo-control, and a controller designed heuristically via the most versatile evolutionary computing paradigm: genetic programming (GP). The experimental results suggest that the controller evolved via GP offers the best quality of control of the car in all of the tested slippery (rainy, snowy, and icy) road conditions.

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Section: Servo Control As a Pd Controllermentioning

confidence: 99%