This Cyber-Physical Systems (CPS) grant will study smart tracking systems on scooters for ensuring safe and smooth interaction with other vehicles and pedestrians on the road. The smart system consists of inexpensive sensors, active sensing based estimation algorithms, and deep learning based robust image processing to enable trajectory tracking of all nearby vehicles on the road. If the danger of a scooter-vehicle collision is detected, an audio-visual alert is automatically provided to the car driver to make them aware of the presence of the scooter. The system also monitors the scooter rider's behavior, provides real-time feedback to improve rider compliance with traffic signals and sidewalk rules, and documents the information as a part of the rider's safety record. The key attractive features of the system are that it is inexpensive (< $500), is immediately useful on today's roads without requiring the vehicles on the road to be equipped with additional technology, and is potentially commercializable. The project contributes to the society by improving safety of micro-transportation systems, and broadens participation in computing via undergraduate research activities and promoting significant cross-disciplinary collaboration between faculty in engineering, computer science and human factors.