Abstract
Autonomous driving is now near future reality which will transform our world due to its numerous benefits. The foremost challenge to this task is to correctly identify the objects in the driving environment. In this work, we propose an object recognition method known as Decision Tree and Decision Fusion based Recognition System (D2TFRS) for autonomous driving. We combined two separate feature sets, which are RGB pixel values and spatial points X,Y of each pixel to form our dataset. The D2TFRS is based on our intuition that reclassification of pre-identified misclassified objects in a driving environment can give better prediction accuracy. Results showed that D2TFRS outperformed AdaBoost classifier and performed better than C5.0 classifier in terms of the classification accuracy and Kappa. In terms of speed, C5.0 outperforms both AdaBoost and D2TFRS. However, D2TFRS outperformed AdaBoost with respect to speed. We strongly believe that D2TFRS will have better parallelization performance compared to the other two methods and it will be investigated in our future work.
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Acknowledgements
The authors acknowledge with thanks the technical and financial support from the Deanship of Scientific Research (DSR) at the King Abdulaziz University (KAU), Jeddah, Saudi Arabia, under the grant number G-661-611-38. The work carried out in this paper is supported by the HPC Center at KAU.
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Alam, F., Mehmood, R., Katib, I. (2018). D2TFRS: An Object Recognition Method for Autonomous Vehicles Based on RGB and Spatial Values of Pixels. In: Mehmood, R., Bhaduri, B., Katib, I., Chlamtac, I. (eds) Smart Societies, Infrastructure, Technologies and Applications. SCITA 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 224. Springer, Cham. https://doi.org/10.1007/978-3-319-94180-6_16
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