Computer Vision Machine Learning

Machine Learning

Alfred Hofmann1, Ursula Barth1, Ingrid Beyer1, Christine Günther1,
Frank Holzwarth1, Anna Kramer1, and Erika Siebert-Cole1

1
Abstract. Autonomous Driving requires object recognition for vehicles to automatically generate a path according to their recognised environment. These environmental conditions have different dims of light, from daylight to night. High-resolution images require high amounts of storage, which is expensive. As automated driving moves from urban to rural areas, in which driving at night and recognizing traffic signs and lights are necessary for all light conditions. A reliable source of input, allowing for the intended performance of an autonomous driving system, such as the country road pilot or rural road pilot, is necessary for adequate deployment of its functionality in its target environment. For quality criteria such as intended performance, functional reliability, safety, and correct driving behaviour to be ensured, accuracy metrics can be a substantial contribution to the product quality criteria. Autonomous technology has the challenge of high price, any new innovative methods for saving costs, without comprising quality, would help develop and enhance the chance of this coming technology being installed, into more advanced automated or autonomous driving vehicles, once the product safety as quality criteria is able to be validated on target roads. Part of this work’s limitation was that only a simulation environment was used for testing the image processing and autonomous driving accuracy models. It was researched and certain algorithms found that may be used in storage size minimisation taken a high-resolution image, its size be reduced could be used without compromising accuracy in the classification process. Further research in their validation may be necessary.

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1 Introduction
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References
1. Baldonado, M., Chang, C.-C.K., Gravano, L., Paepcke, A.: The Stanford Digital Library Metadata Architecture. Int. J. Digit. Libr. 1 (1997) 108–121
2. Bruce, K.B., Cardelli, L., Pierce, B.C.: Comparing Object Encodings. In: Abadi, M., Ito, T. (eds.): Theoretical Aspects of Computer Software. Lecture Notes in Computer Science, Vol. 1281. Springer-Verlag, Berlin Heidelberg New York (1997) 415–438
3. van Leeuwen, J. (ed.): Computer Science Today. Recent Trends and Developments. Lecture Notes in Computer Science, Vol. 1000. Springer-Verlag, Berlin Heidelberg New York (1995)
4. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. 3rd edn. Springer-Verlag, Berlin Heidelberg New York (1996)
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