https://github.com/mostafa-wael/environment-perception-for-self-driving-cars
Extracting useful scene information to allow self-driving cars to safely and reliably traverse their environment
https://github.com/mostafa-wael/environment-perception-for-self-driving-cars
object-detection opencv perception self-driving-car semantic-segmentation
Last synced: about 1 month ago
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Extracting useful scene information to allow self-driving cars to safely and reliably traverse their environment
- Host: GitHub
- URL: https://github.com/mostafa-wael/environment-perception-for-self-driving-cars
- Owner: Mostafa-wael
- License: mit
- Created: 2022-01-04T19:23:31.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-01-04T19:27:56.000Z (over 3 years ago)
- Last Synced: 2025-01-22T12:45:54.650Z (3 months ago)
- Topics: object-detection, opencv, perception, self-driving-car, semantic-segmentation
- Language: Jupyter Notebook
- Homepage:
- Size: 1.26 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Environment-Perception-For-Self-Driving-Cars
This is an assignment from "Visual Perception for Self-Driving Cars" course of the "Self-Driving Cars Specialization" on Coursera.org.
This assignmnet aims at extracting useful scene information to allow self-driving cars to safely and reliably traverse their environment, throught 4 main tasks as follows:
- Use the output of semantic segmentation neural networks to implement drivable space estimation in 3D.
- Use the output of semantic segmentation neural networks to implement lane estimation.
- Use the output of semantic segmentation to filter errors in the output of 2D object detectors.
- Use the filtered 2D object detection results to determine how far obstacles are from the self-driving car.
Course's link: https://www.coursera.org/learn/visual-perception-self-driving-cars/home/welcome
N.B. for any missing files, check the course's link