Week 25: Study of difference images, Results, Controlnet

Results table (regression, cropped image)

Driving results (regression networks)
	Manual		Pilotnet v + w		TinyPilotnet v + w		Stacked v+w		Stacked (diff) v+w		LSTM-Tinypilotnet v + w		DeepestLSTM-Tinypilot.
Circuits	Percentage	Time	Percentage	Time	Percentage	Time	Percentage	Time	Percentage	Time	Percentage	Time	Percentage	Time
Simple (clockwise)	100%	1min 35s	100%	1min 37s	100%	1min 41s	100%	1min 41s	100%	1min 39s	100%	1min 40s	100%	1min 37s
Simple (anti-clockwise)	100%	1min 32s	100%	1min 38s	100%	1min 41s	10%		100%	1min 38s	100%	1min 38s	100%	1min 38s
Monaco (clockwise)	100%	1min 15s	100%	1min 20s	100%	1min 19s	85%		45%		50%		55%
Monaco (anti-clockwise)	100%	1min 15s	100%	1min 19s	100%	1min 18s	15%		5%		35%		55%
Nurburgrin (clockwise)	100%	1min 02s	100%	1min 04s	100%	1min 04s	8%		8%		40%		100%	1min 04s
Nurburgrin (anti-clockwise)	100%	1min 02s	100%	1min 06s	100%	1min 05s	80%		50%		50%		80%

Results table (regression, whole image)

Driving results (regression networks)
	Manual		Pilotnet v + w		TinyPilotnet v + w		Stacked v+w		Stacked (diff) v+w
Circuits	Percentage	Time	Percentage	Time	Percentage	Time	Percentage	Time	Percentage	Time
Simple (clockwise)	100%	1min 35s	100%	1min 41s	100%	1min 39s	100%	1min 40s	100%	1min 43s
Simple (anti-clockwise)	100%	1min 32s	100%	1min 39s	100%	1min 38s	100%	1min 46s	10%
Monaco (clockwise)	100%	1min 15s	100%	1min 21s	100%	1min 19s	50%		5%
Monaco (anti-clockwise)	100%	1min 15s	100%	1min 23s	100%	1min 20s	7%		5%
Nurburgrin (clockwise)	100%	1min 02s	100%	1min 03s	100%	1min 05s	50%		8%
Nurburgrin (anti-clockwise)	100%	1min 02s	100%	1min 06s	100%	1min 06s	80%		50%

Driving results (regression networks, continuation)
	LSTM-Tinypilotnet v + w		DeepestLSTM-Tinypilot.		Controlnet
Circuits	Percentage	Time	Percentage	Time	Percentage	Time
Simple (clockwise)	100%	1min 39s	100%	1min 39s	100%	1min 46s
Simple (anti-clockwise)	10%		100%	1min 41s	100%	1min 37s
Monaco (clockwise)	100%	1min 27s	50%		5%
Monaco (anti-clockwise)	50%		100%	1min 21s	5%
Nurburgrin (clockwise)	100%	1min 08s	100%	1min 05s	8%
Nurburgrin (anti-clockwise)	50%		100%	1min 07s	8%

Study of temporal images

I’ve tried to create a difference image with only two channels: HV. First, I made the absolute difference of the two images (separated 5 frames) for each channel. Then I normalized the difference between 0 and 255. It isn’t a good solution for driving.

Straight line:

Curve:

I’ve create a sum image using numpy.add(x1, x2). The image result is:

Controlnet

Driving results (Controlnet network, whole image)
	Manual		Controlnet
Circuits	Percentage	Time	Percentage	Time
Simple (clockwise)	100%	1min 35s	100%	1min 46s
Simple (anti-clockwise)	100%	1min 33s	100%	1min 37s
Monaco (clockwise)	100%	1min 15s	5%
Monaco (anti-clockwise)	100%	1min 15s	5%
Nurburgrin (clockwise)	100%	1min 02s	8%
Nurburgrin (anti-clockwise)	100%	1min 02s	8%