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Road transportation is among the grand global challenges affecting human lives, health, society, and economy. Autonomous vehicles (AVs) are the latest among the radical solutions to address transportation challenges. AVs have become a reality although their penetration in real environments needs more time. The foremost challenge for AVs is to recognize objects in real driving environment with highest certainty. This paper is an extension of our earlier work where we developed a methodology to integrate supervised learning and decision fusion to enhance object classification accuracy in a driving environment, i.e., to enable an auto-pilot to take better driving decisions. This problem equates to pixel classification. Our study revealed that the C5.0 decision tree classifier performs similar to deep learning. This paper extends and investigates the topic further and provides an in-depth performance …