Week 24: Reading information, Temporal difference network, Results

Results(cropped image)

Driving results (classification networks)
	Manual		5v+7w biased		5v+7w balanced		5v+7w imbalanced
Circuits	Percentage	Time	Percentage	Time	Percentage	Time	Percentage	Time
Simple (clockwise)	100%	1min 35s	100%	1min 41s	75%		100%	1min 42s
Simple (anti-clockwise)	100%	1min 32s	100%	1min 39s	100%	1min 39s	100%	1min 43s
Monaco (clockwise)	100%	1min 15s	100%	1min 20s	70%		85%
Monaco (anti-clockwise)	100%	1min 15s	100%	1min 18s	8%		100%	1min 20s
Nurburgrin (clockwise)	100%	1min 02s	100%	1min 03s	100%	1min 03s	100%	1min 05s
Nurburgrin (anti-clockwise)	100%	1min 02s	100%	1min 05s	80%		80%

Results(whole image)

Driving results (classification networks)
	Manual		5v+7w biased		5v+7w balanced		5v+7w imbalanced
Circuits	Percentage	Time	Percentage	Time	Percentage	Time	Percentage	Time
Simple (clockwise)	100%	1min 35s	35%		10%		90%
Simple (anti-clockwise)	100%	1min 32s	100%	1min 49s	100%	1min 46s	90%
Monaco (clockwise)	100%	1min 15s	100%	1min 24s	5%		100%	1min 23s
Monaco (anti-clockwise)	100%	1min 15s	100%	1min 29s	8%		100%	1min 24s
Nurburgrin (clockwise)	100%	1min 02s	100%	1min 10s	8%		90%
Nurburgrin (anti-clockwise)	100%	1min 02s	100%	1min 07s	8%		100%	1min 09s

Reading information

End to End Learning for Self-Driving Cars

In this paper (https://github.com/Kejie-Wang/End-to-End-Learning-for-Self-Driving-Cars), a convolutional neural network (CNN) is trained to map raw pixels from a single front-facing camera directly to steering commands. The system automatically learns internal representations of the necessary processing steps such as detecting useful road features with only the human steering angle as the training signal.

Images are fed into a CNN which then computes a proposed steering command. The proposed command is compared to the desired command for that image and the weights of the CNN are adjusted to bring the CNN output closer to the desired output. The weight adjustment is accomplished using back propagation. Once trained, the network can generate steering from the video images of a single center camera.

Training data was collected by driving on a wide variety of roads and in a diverse set of lighting and weather conditions. Most road data was collected in central New Jersey, although highway data was also collected from Illinois, Michigan, Pennsylvania, and New York. Other road types include two-lane roads (with and without lane markings), residential roads with parked cars, tunnels, and unpaved roads. Data was collected in clear, cloudy, foggy, snowy, and rainy weather, both day and night. 72 hours of driving data was collected.

They train the weights of their network to minimize the mean squared error between the steering command output by the network and the command of either the human driver, or the adjusted steering command for off-center and rotated images. The network consists of 9 layers, including a normalization layer, 5 convolutional layers and 3 fully connected layers. The input image is split into YUV planes and passed to the network.

The first layer of the network performs image normalization. The convolutional layers were designed to perform feature extraction and were chosen empirically through a series of experiments that varied layer configurations. Theye use strided convolutions in the first three convolutional layers with a 2×2 stride and a 5×5 kernel and a non-strided convolution with a 3×3 kernel size in the last two convolutional layers. They follow the five convolutional layers with three fully connected layers leading to an output control value which is the inverse turning radius. The fully connected layers are designed to function as a controller for steering, but it is not possible to make a clean break between which parts of the network function primarily as feature extractor and which serve as controller.

To train a CNN to do lane following they only select data where the driver was staying in a lane and discard the rest. They then sample that video at 10 FPS. A higher sampling rate would result in including images that are highly similar and thus not provide much useful information. After selecting the final set of frames they augment the data by adding artificial shifts and rotations to teach the network how to recover from a poor position or orientation.

Before road-testing a trained CNN, they first evaluate the networks performance in simulation.

VisualBackProp: efficient visualization of CNNs

Paper

Target-driven Visual Navigation in Indoor Scenesusing Deep Reinforcement Learning

Paper, 2

Temporal difference network

In this method I test a new network with the difference image of it and it-5. The results are:

Driving results (regression networks)
	Temporal_dif const v whole image		Temporal_dif whole image		Temporl_dif const v cropped image		Temporal_dif cropped image
Circuits	Percentage	Time	Percentage	Time	Percentage	Time	Percentage	Time
Simple (clockwise)	100%	3min 37s	100%	1min 43s	100%	3min 37s	100%	1min 39s
Simple (anti-clockwise)	100%	3min 38s	100%	1min 44s	100%	3min 38s	100%	1min 42s
Monaco (clockwise)	45%		5%		45%		5%
Monaco (anti-clockwise)	45%		5%		8%		5%
Nurburgrin (clockwise)	8%		8%		8%		8%
Nurburgrin (anti-clockwise)	90%		8%		90%		8%

Difference image: