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Self-driving cars can revolutionize transportation systems, o!ering the potential to significantly enhance efficiency while also addressing the critical issue of human fatalities on roadways. Hence, there is a need to investigate methods to enhance self-driving technologies through end-to-end learning techniques. In this paper, we investigate methodologies that integrate Convolutional Neural Networks (CNNs) to enhance self-driving consistency through real-time velocity and steering estimation. We extend an end-to-end state-of-the-art learning solution with real-time speed data as additional model input to refine reliability. Specifically, our work integrates an optical encoder sensor system to record car speed during training data collection, ensuring the throttle can be regulated during model inference. An end-to-end experimental testbed is deployed on the Chameleon cloud using CHI@Edge infrastructure to manage a 1:18 scaled car, equipped with a Raspberry Pi as its onboard computer. Finally, we provide guidance that facilitates reproducibility and highlight the challenges and limitations of supporting such experiments.