Application of Edge Detection inAutomated Driving SystemsECE 533 Final Project ReportTom KratzkeIntroductionPerhaps one of the more interesting applications of image processing is its use in the field of automated driving. As you may suspect, automated driving is the idea that human input is not required to drive a car. Instead, a computer would drive for you basedon sensor data, camera data, and position information. This information is all processed by a computer and it makes all the decisions. If a system could be made that would decide how fast to go, what direction to go, and how to avoid obstacles it would be conceivable to just relax and have the computer drive for you. There are a few systems currently available, but nothing to the extent of a full automated system. Some vehicles today come equipped with a collision detection system, where if the vehicle decides that a collision is imminent; it will automatically break for you. There is still a lot of work to be done before fully automated systems are available.One of the methods currently being research in the area of automated driving is computer vision systems. Based on sensor and camera data collected from the vehicle, a vehicle can be directed on where to go without human input. There are a couple sub-fields in automated driving where this could be very beneficial: automated steering and bumper to bumper driving.Automated steering is exactly what it sounds like; a computer will decide what angle to adjust the steering wheel based on external data like pictures and distance sensors. Most systems currently being researched are learning systems, where a human driver drives the vehicle for a period of time and the computer studies what situations the driver has the steering wheel in certain positions. Then the driver enables the automatedsteering system and the computer uses what it learned from the driver and steers the vehicle by itself.Bumper to bumper driving is different in the fact that there is no learning required. In a gridlock situation, the driver can simply switch on the system and it would start to function. This system is based on the idea that bumper to bumper driving is very simple and tedious; something a computer should have no problem with. In one system, the vehicle takes pictures of the vehicle in front of it and processes the picture to extract the centerline and width of the vehicle image. From this data, it stays directly behind the other car and calculates the distance behind that car from the extracted width.ApproachThe idea investigated in this project is if edge detection methods would aid in either of these two subfields of automated driving.Automated steering as described above would require an extremely large set of learning data if no image processing was done. Edge detection could greatly reduce the amount of learning data required by simplifying the image considerably. If the outline of a road can be extracted from the image, that test data could be applied to all cases where the road looks similar instead of taking into account non-essential information such as surrounding terrain. I will implement the edge detection algorithm for all kinds of road conditions starting with very simple cases and progressing to more complicated systems. Empty straight roads, empty curving roads, roads in bad repair, crowded roads, and combinations thereof will all be tested.Bumper to bumper driving could also potentially benefit from edge detection. If the outline of a vehicle can be extracted, its width and centerline would be easy to calculate. I will test all types of different vehicles from a rear perspective to see if a single operator can be used in all situations.For all my tests, I will implement all of the following operators:Sobel: Finds edges using the Sobel approximation of the derivative.[-1 -2 -1 [-1 0 1 0 0 0 -2 0 2 1 2 1] -1 0 1]Prewitt: Finds edges using the Prewitt derivative approximation.[-1 -1 -1 [-1 0 1 0 0 0 -1 0 1 1 1 1] -1 0 1]Laplacian of Gaussian: Finds edges by looking at the zero crossing after filtering image with the Laplacian of Gaussian filterCanny: Finds edges by looking for local maxima of the gradient of the image.Work DoneAutomated SteeringTo test my method on the usefulness of edge detection on automated steering, I performed the four different edge detection techniques to an array of images. I started with the simplest situations, where there are no turns and no obstacles on the road and worked my way up to more complicated situations. I started adding turns to the road, additional noise outside the road, (trees, bushes, etc.) as well as obstacles to avoid on the road. Finally, I took all of these cases to the extreme by using pictures of roads in bad repair, crowded highways, and roads with a high amount of tree cover. I am looking for an outline of the road that can be reasonable extracted by a computer program. This outline would represent the boundary between drivable road and surrounding terrain and obstacles in the road. The following pictures are a sample set of my data.Note: All images of edge detection algorithm used for automated steering are in the following order.Top: PrewittSobelLoGBottom: CannyIn simple cases with no noise or obstacles, Sobel and Prewitt perform very wellWith introduction of noise/obstacles, Canny and LoG still extract a reasonable roadWith obstacles but with no surrounding noise, Prewitt and Sobel operators function correctlyCanny operator still extracts a reasonable road from a high amount of noiseWith a lot of road obstacles, Canny still extracts road edgesBumper to Bumper DrivingTo test the usefulness of edge detection, I performed the four edge detection algorithms on a series of rear view pictures of vehicles. First, commercial vehicles were used, including cars, trucks, vans, and SUVs. Next, commercial vehicles were tested to see if the same edge detection algorithm would work for all vehicles. Finally, I attemptedto find situations where background noise would disrupt the algorithms effectiveness to see how useful this method is in real world scenarios. I am looking for the outline of the car with little surrounding noise. This outline would be used by a computer program to extract the centerline and width of the vehicle. The following is a sample set of my results.Note: All images of edge detection algorithm used for bumper to bumper driving are in
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