This Innovative Practice Work-In-Progress Paper presents a case of learning autonomous driving in tangible practice. As technology sustainably enhances the quality of life, intelligent systems continue to contribute solutions to some of the biggest challenges faced by humans. Autonomous vehicles offer humans the opportunity to increase transportation safety by reducing human errors on the road, preventing accidents, improving human productivity by reducing commuting time, and possibly mitigating air pollution. There is a critical shortage of educational and training programs in autonomous vehicles due to the high cost of full-size vehicles, computing and sensor equipment, and big lab space needed. To address this problem, we develop a 1/10-scale autonomous vehicle powered by pre-collision detection, lane tracking, and road sign recognition systems. The pre-collision system is built using ultrasonic sensors, and the Proportional-Integral-Derivative (PID) control is implemented to manipulate the vehicle's safety response. The Open-Source Computer Vision Library (OpenCV) is exploited to detect and process real-time on-road streaming video to enable lane-tracking and road sign recognition. AI techniques are utilized for the model training. Preliminary results of this work are presented and analyzed. We also discuss the future directions of this study.