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This paper develops a cost-effective vehicle detection and tracking system based on fusion of a 2-D LIDAR and a monocular camera to protect electric micromobility devices, especially e-scooters, by predicting the real- time danger of a car- scooter collision. The cost and size disadvantages of 3-D LIDAR sensors make them an unsuitable choice for micromobility devices. Therefore, a 2-D RPLIDAR Mapper sensor is used. Although low-cost, this sensor comes with major shortcomings such as the narrow vertical field of view and its low density of data points. Due to these factors, the sensor does not have a robust output in outdoor applications, and the measurements keep jumping and sliding on the vehicle surface. To improve the performance of the LIDAR, a single monocular camera is fused with the LIDAR data not only to detect vehicles, but also to separately detect the front and side of a target vehicle and to find its corner. It is shown that this corner detection method is more accurate than strategies that are only based on the LIDAR data. The corner measurements are used in a high-gain observer to estimate the location, velocity, and orientation of the target vehicle. The developed system is implemented on a Ninebot e-scooter platform, and multiple experiments are performed to evaluate the performance of the algorithm.