ADAS Coding Activities: Lane Detection, Traffic Signs, YOLOv3

Optional Coding Activities

Coding Activity 1 –Development of Lane Detection system forADAS application
➢Description:
➢Lane detection system uses front camera of the ego vehicle. It detect the lanes and also determine the lane
in which the Ego vehicle is moving. Lane detection is important algorithm for ADAS systems like Lane
Departure Warning, Lane Change Assist, Lane Keep Assist, etc.
➢Problem Statement:
➢Use Python (or any other programming language) for development.
➢Use openCV for computer vision and then required machine Learning / Deep Learning algorithm of your
choice.
➢Down a video from public dataset like KITTI, nuscenes or other, which contains recording of road from
front camera where lanes are clearly visible.
➢Implement the Lane detection system and validate it.
➢Test the algorithm on the video by contiuously detecting road lanes.
➢Some references:
➢https://www.analyticsvidhya.com/blog/2020/05/tutorial-real-time-lane-detection-opencv/
➢https://medium.com/@mrhwick/simple-lane-detection-with-opencv-bfeb6ae54ec0
➢https://paperswithcode.com/task/lane-detection/codeless
➢https://arxiv.org/pdf/1903.02193.pdf

Coding Activity 2 –Development of Traffic Sign Recognition system forADAS
➢Description:
➢Traffic Sign Recognition system is important ADAS function because when one is driving on road, there
are very high chances for driver to miss some or many traffic signs which are vital to drive properly.
Hence ADAS system by using mainly the front camera enabled with AI based algorithm detects and
display the traffic signs detected on road to the driver on the instrumnet cluster of the vehicle.
➢Problem Statement:
➢Use Python (or any other programming language) for development.
➢Use openCV for computer vision and then required machine Learning / Deep Learning algorithm of your
choice.
➢Download the publicly available dataset of traffic sign images.
➢Develop Deep Learning CNN model and train it using the dataset. Keep aside some part of the data for
validation and testing.
➢After training and validation with high accuracy, test the model on test dataset and save it for later use.

Coding Activity 2 –Development of Traffic Sign Recognition system forADAS
➢References:
➢https://data-flair.training/blogs/python-project-traffic-signs-recognition/
➢https://medium.com/dataflair/class-data-science-project-for-2020-traffic-signs-recognition-12b09c131742
➢https://towardsdatascience.com/recognizing-traffic-signs-with-over-98-accuracy-using-deep-learning-
86737aedc2ab
➢https://www.pyimagesearch.com/2019/11/04/traffic-sign-classification-with-keras-and-deep-learning/

Coding Activity 3 –Development of Speed Limit Recognition system forADAS
➢Description:
➢Speed limit recognition is kind a sub part of traffic sign recognition system. In many countries, as per the
road and area, the maximum speed limit varies. If the vehicle is found to be driving higher than the
allowed maximum speed limit, it can lead to fine and also danger situation on road.
➢Hence, such ADAS systems using the front camera enabled with AI, detects the traffic signs which are for
speed limits and then display the speed on the instrument panel of the vehicle.
➢Problem Statement:
➢Use Python (or any other programming language) for development.
➢Use openCV for computer vision and then required machine Learning / Deep Learning algorithm of your
choice.
➢Download the publicly available dataset of speed limit traffic sign images
➢Develop Deep Learning CNN model and train it using the dataset. Keep aside some part of the data for
validation and testing.
➢After training and validation with high accuracy, test the model on test dataset and save it for later use.
➢Test the system by using some other speed limit images. You can also test it using some video feed.