Abstract
In recent years, there has been intense progress in the autonomous car field. In large part of the publication, the problem of controlling the movement of an autonomous vehicle is solved based on machine learning algorithms that require training data, significant computing power, and memory resources. The work aimed to find an alternative methodology that would allow adaptive car control in various road scenarios. The following tasks were analyzed: driving in a column of cars with a safe distance, overtaking maneuver and returning to the correct lane, keeping the vehicle in the middle of the lane while moving along a winding track with automatic speed adjustment. During the simulation, data from sensors (cameras, LIDAR) were used by adaptive controllers. A state machine was used to switch between controllers. The visualization of the system's operation was realized thanks to the newly introduced possibility of integrating the graphics engine “Unreal Engine” and “Matlab 2021a”. Based on the algorithm's operation, it was found that adaptive controllers can effectively cope with the control of autonomous vehicles in various situations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Litman, T.: Autonomous Vehicle Implementation Predictions. Victoria Transport Policy (2021)
Zanchin, B.C., Adamshuk, R., Santos, M.M.: On the instrumentation and classification of autonomous cars. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2017)
Zhang, J.L., Loannou, P.A.: Longitudinal control of heavy trucks in mixed traffic: environmental and fuel economy considerations. IEEE Trans. Intell. Transp. Syst. (2006)
Li, S.E., Deng, K., Li, K.Q., Ahn, C.: Terminal sliding mode control of automated car-following system without reliance on longitudinal acceleration information. Mechatronics (2015)
Zhang, G.X., Wang, Z.C., Fan, B.W., Zhao, L., Qi, Y.Z.: Adaptive cruise control system with traffic jam tracking function based on multi-sensors and the driving behavior of skilled drivers. Adv. Mech. Eng. (2018)
Feng, X., Hu, J., Huo, Y., Zhang, Y.: Autonomous lane change decision making using different deep reinforcement learning methods. In: 19th COTA International Conference of Transportation Professionals (2019)
Haavaldsen, H., Aasbø, M., Huo, Y., Lindseth, F.: Autonomous Vehicle Control: end-to-end Learning in Simulated Urban Environments. Norwegian University of Science and Technology, Trondheim, Norway (2019)
Witryna internetowa MathWorks.: (2021). https://www.mathworks.com/products/automated-driving.html
Bokare, P.S., Maurya, A.K.: Acceleration-deceleration behaviour of various vehicle types. In: World Conference on Transport Research—WCTR 2016 Shanghai (2016)
Maitra, B., Cheranchery, M.F., Prasad, P.: Rules For Safe Driving
Houenou, A., Bonnifait, P., Cherfaoui, V., Wen, Y.: Vehicle trajectory prediction based on motion model and maneuver recognition. In: International Conference on Intelligent Robots and Systems (IROS 2013) (2013)
MathWorks web page.: https://www.mathworks.com/help/mpc/ug/lane-keeping-assist-with-lanedetection.html. Accessed 02 Sep 2021
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Oskroba, J., Mendrok, K. (2022). Adaptive Control of the Autonomous Car. In: Powałka, B., Parus, A., Chodźko, M., Szewczyk, R. (eds) Mechatronics—Trending Future Industries. MECHATRONICS 2020. Lecture Notes in Networks and Systems, vol 377. Springer, Cham. https://doi.org/10.1007/978-3-030-93377-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-93377-7_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-93376-0
Online ISBN: 978-3-030-93377-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)