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Robust lane-following algorithms are one of the main challenges in developing effective automated vehicles. In this work, a team of four undergraduate students designed and evaluated several automated lane-following algorithms using computer vision as part of a Research Experience for Undergraduates program funded by the National Science Foundation. The developed algorithms use the Robot Operating System (ROS) and the OpenCV library in Python to detect lanes and implement the lane-following logic on the road. The algorithms were tested on a real-world test course using a street-legal vehicle with a high-definition camera as input and a drive-by-wire system for output. Driving data were recorded to compare the performance of human driving to that of the self-driving algorithms on the basis of three criteria: lap completion time, lane positioning infractions, and speed limit infractions. The evaluation of the data showed that the human drivers successfully completed every lap with zero infractions at a 100% success rate in varied weather conditions, whereas, our most reliable algorithms had a success rate of at least 70% with some lane positioning infractions and at lower speeds.
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Developing, Analyzing, and Evaluating Self-Drive Algorithms Using Electric Vehicles on a Test Course
Reliable lane-following is one of the most important tasks for an automated vehicle or ADAS. The intent of this project was to design and evaluate multiple lane-following algorithms for an automated vehicle using computer vision. The implemented algorithms' performance was then evaluated on a testing course and compared with a human driver. ROS and OpenCV were used to detect and follow lanes on the road. A street-legal vehicle with a high-definition camera and drive-by-wire system was used to implement and evaluate driving data. Each algorithm was evaluated based on time for completion, speed limit infractions, and lane positioning infractions. The recorded evaluation data determined the most reliable lane-following algorithm. All of our algorithms had a success rate of at least 60% on certain lanes of the testing course.
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- PAR ID:
- 10386931
- Date Published:
- Journal Name:
- 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS)
- Page Range / eLocation ID:
- 687 to 692
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/MASS56207.2022.00101
