TY - GEN
T1 - Learning Autonomous Driving in Tangible Practice
T2 - 2021 IEEE Frontiers in Education Conference, FIE 2021
AU - Paulino, Laura
AU - Zhu, Michelle
AU - Wang, Weitian
N1 - Funding Information:
ACKNOWLEDGMENT This research is partially supported through a STEM +Computing grant from the Division of Research on Learning of the National Science Foundation (# 1742125). The authors would thank the great support from the Department of Computer Science at Montclair State University.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Autonomous vehicle
KW - PID control
KW - STEM education
KW - computer vision
KW - hands-on engagement
UR - http://www.scopus.com/inward/record.url?scp=85123827479&partnerID=8YFLogxK
U2 - 10.1109/FIE49875.2021.9637402
DO - 10.1109/FIE49875.2021.9637402
M3 - Conference contribution
AN - SCOPUS:85123827479
T3 - Proceedings - Frontiers in Education Conference, FIE
BT - Proceedings - 2021 IEEE Frontiers in Education Conference, FIE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 October 2021 through 16 October 2021
ER -