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Copilot : Driving assistance on mobile devices
Lane and obstacle detection for active assistance during driving.
Vehicle Position + collision time superposed in the top view
Accompanying article https://towardsdatascience.com/copilot-driving-assistance-635e1a50f14
Global annual road accidents fatalities total about 1.5 million which is just about the population of Mauritius. 90% of these occur in low and middle income countries which have less than half of the total vehicles in the world. Advanced driver-assistance systems (ADAS) Lane detection, collision warning are present in less than 0.1% of the vehicles. They are almost non existent in developing countries. Median Smartphone ownership in emerging economies is about 10 times as high as that of four wheeler. While we already have semi autonomous vehicles running about in parts of the world. This repository checks how close we might come to using a mobile computing platform as an ADAS copilot.
DOWNLOAD WEIGHTS AND CODE
! git clone https://github.com/visualbuffer/copilot.git
! mv copilot/* ./
! wget -P ./model_data/ https://s3-ap-southeast-1.amazonaws.com/deeplearning-mat/backend.h5
Robustness for different illumination conditionsz
USAGE EXAMPLE
from frame import FRAME
file_path = "videos/highway.mp4"# <== Upload appropriate file
video_out = "videos/output11.mov"
frame = FRAME(
ego_vehicle_offset = .15, # SELF VEHICLE OFFSET
yellow_lower = np.uint8([ 20, 50, 100]), # LOWER YELLOW HLS THRESHOLD
yellow_upper = np.uint8([35, 255, 255]), # UPER YELLOW HLS THRESHOLD
white_lower = np.uint8([ 0, 200, 0]), # LOWER WHITE THRESHOLD
white_upper = np.uint8([180, 255, 100]), # UPPER WHITE THRESHOLD
lum_factor = 118, # NORMALIZING LUM FACTOR
max_gap_th = 0.45, # MAX GAP THRESHOLD
YOLO_PERIOD = .25, # YOLO PERIOD
lane_start=[0.35,0.75] , # LANE INITIATION
verbose = 3) # VERBOSITY
frame.process_video(file_path, 1,\
video_out = video_out,pers_frame_time =144,\
t0 =144 , t1 =150)#None)| PARAMETER | Description |
|---|---|
| SELF VEHICLE OFFSET | Trim off from bottom edge video if ego vehicle covers part of the frame % of front view |
| LOWER YELLOW HLS THRESHOLD | Lower yellow HLS threshold used to prepare the mask. Tune down if yellow lane is not detected, up if all the foilage is |
| UPPER YELLOW HLS THRESHOLD | Upper threshold for identifying yellow lanes |
| LOWER WHITE THRESHOLD | Lower yellow HLS threshold used to prepare the mask. Tune up saturation if foilage lights up the entire scene |
| UPPER WHITE THRESHOLD | |
| NORMALIZING LUM FACTOR | Factor used to normalize luminosity against, reducing increses lower Lum threshold |
| MAX GAP THRESHOLD | Max continous gap tollerated in the lane detection % of top-view height |
| YOLO PERIOD | Period [s] after which YOLO is detected, typ 2s reducing decreases processing fps increases detection |
| LANE INITIATION | intial guess for lane start % of top-view width |
| VERBOSITY | 1 Show lesser,2 Show less,3 Show everything |
Detecting lane change automatically
Notebooks
| DIRECTORY | COLAB |
|---|---|
| ./notebooks/coPilot.ipynb | https://colab.research.google.com/drive/1CdqDXZqssDgSC35W4A-4Gp8kfqzyPKug |