Abstract
By using computer vision and machine learning methods, driving lane detection and tracking, the position of the vehicles in the vicinity, their speed and direction will be determined through real-time processing of images taken from the traffic camera. Processing of the collected data using artificial intelligence and fuzzy logic and to calculate the data within the scope of “game theory” and to implement the dynamic control of the vehicle in the light of calculated data is planned. In addition to that, the designed system can also function as a driver assistant for non-autonomous vehicles with an appropriate user interface. First, the positions of the vehicles and driving lanes will be detected and monitored using computer vision and machine learning methods. Then, the vehicle speeds will be calculated by taking advantage of the historical data of the vehicle positions in the surrounding area from the previous observations, and the location estimation will be made by creating probability distributions of where each vehicle will be in the future. With the position estimation and the obtained speed information, it will be ensured that the vehicle is in the safest position in the transportation process to the destination and that it travels again at the safest speed.
Highlights
In last years, we see that switching to autonomous vehicles in vehicle technology is accelerating
When we look into history of autonomous vehicles, we see that first autonomous projects appear in 1980s
Support vector machines (SVM) was trained with various color spaces in order to select the color space that is most compatible with the Histogram Over Gradient (HOG) algorithm and data set. 90 % of the data group for training and 10 % for the lead test were allocated
Summary
We see that switching to autonomous vehicles in vehicle technology is accelerating. Autonomous vehicles are vehicles that can sense surrounding environment and travel without any human intervention. Autonomous vehicles can sense the environment with technologies like radar, LIDAR, odometer and computer vision. When we look into history of autonomous vehicles, we see that first autonomous projects appear in 1980s. First prototype came to life with navlab and ALV projects that ran by Carnegie Mellon University. Followed by project Eureka Prometheus by Mercedes-Benz and Bundeswehr partnership at 1987. Lots of company manufactured many more and some of these vehicles could find a place in active traffic in some countries
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