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On the Image Sensor Processing for Lane Detection and Control in Vehicle Lane Keeping Systems
1International Program on Energy Engineering, National Cheng Kung University, Tainan 70101, Taiwan. river85511@gmail.com.
Sensors (Basel, Switzerland)
|April 11, 2019
Summary
This study introduces a cost-effective lane detection and control system for autonomous vehicles. The developed image sensor and algorithm achieve high accuracy in real-time, enabling safer autonomous delivery systems.
Area of Science:
- Robotics and Autonomous Systems
- Computer Vision
- Control Engineering
Background:
- Lane keeping is crucial for advanced driving assistance systems and autonomous vehicles.
- Existing systems often face challenges in processing efficiency and cost-effectiveness.
Purpose of the Study:
- To present a cost-effective image sensor and efficient processing algorithm for lane detection and control.
- To enable reliable lane keeping for autonomous delivery systems.
Main Methods:
- Lane detection using inverse perspective mapping and RANSAC parabola fitting.
- Lane control employing a pure pursuit steering controller and a classical PI speed controller based on a nonholonomic kinematic model.
Main Results:
- Experimental validation on a 1/10 scale model car demonstrated superior processing performance in straight and curved sections.
- Achieved average lane detection error within 5% and maximum cross-track error within 9% in real-time.
Conclusions:
- The developed system offers a viable solution for real-time lane detection and control in autonomous vehicles.
- This advancement paves the way for more cost-effective autonomous delivery systems.
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