ESTIMATION OF VEHICLE SIGNALS FOR AUTONOMOUS DRIVING APPLICATIONS

2022-9-14
Temurtaş, Halil
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The aim of this study is to develop a state estimator for autonomous driving applications. The increasing demand and usage of self-driving vehicle technologies and Advanced Driver Assistance Systems (ADAS) play a vital role in vehicle safety. In order to ensure their operations, these autonomous driving applications require the observation of various vehicle signals. However, some of the necessary signals might not be directly observed by the sensors or the sensors to observe the necessary signal might be too expensive or big for mass production. Therefore, estimating necessary vehicle signals from inexpensive sensors for autonomous driving applications is a very demanding topic in the industry. This thesis aims to observe vehicle navigation solutions, yaw rate, tire slips, and tire forces using the IMU, GPS receiver, wheel encoders, and steering angle sensor found in many modern vehicles. In order to accomplish this, an Extended Kalman Filter (EKF) architecture is proposed to estimate the vehicle position, velocity, and attitude. Then a method to observe the necessary tire states using the vehicle navigation solution is proposed. A simulation environment using Gazebo Classics Simulator and ROS-2 middle-ware is built for the study to simulate vehicle dynamics and sensor data. Also, the proposed algorithms are implemented as ROS-2 nodes for real-time experiments. The experiments have been performed using a vehicle model in this environment. The results showed that the proposed algorithm could produce position estimates within a 5-meter error, velocity estimates within the 0.5 cm/s error, and attitude estimates within the 0.5-degree error. Moreover, the proposed algorithm can observe lateral and longitudinal tire force with up to 200-N error if there is sufficient change in heading to improve the quality of attitude estimates. Results showed that the proposed architecture could estimate sufficient vehicle navigation solutions and tire states.
autonomous vehicle, autonomous driving, estimation, sensor fusion, navigation, kalman filter, slip, contact force, otonom araç, otonom sürüş, kestirim, sensör füzyonu, seyrüsefer,, kalman filtre, kayma, temas kuvveti
https://hdl.handle.net/11511/99730
Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats
H. Temurtaş, “ESTIMATION OF VEHICLE SIGNALS FOR AUTONOMOUS DRIVING APPLICATIONS,” M.S. - Master of Science, Middle East Technical University, 2022.
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