Abstract: An image classification system and method are used to determine the status of equipment completeness of the rental two-wheeled vehicles, such as electric scooters. The system and method use deep learning models to analyze and classify ambiguous states of the rental vehicle when the user finishes the ride. These states are likely to be encountered by rental vehicles, to protect helmets, trunks or other equipment from loss or damage.

Abstract: A smart system for detecting the presence of a helmet in a vehicle is disclosed herein. The system comprises a first transceiver configured within a trunk of the vehicle. The first transceiver is configured to transmit a first detection signal on detecting the opening of the trunk of the vehicle. At least one second transceiver corresponds to at least one helmet provided on the body of the helmet. The second transceiver is configured to respond to the first detection signal with a response signal, wherein the response signal includes a unique identity marker corresponding to the helmet. A transmission device is configured to detect opening and closing of trunk to activate the first transceiver, subsequent to which the first detection signal is sent, and the transmission device is further configured to transmit information associated with the response signal to a base controller.

Abstract: A system and method for detecting rider impairment based on image or audio input is implemented in a rental fleet of lightweight vehicles. The system comprises a mobile device, a backend server, and one or more lightweight vehicles. Access to fleet vehicles is controlled by the mobile application based on results of data collected about the prospective driver.

Abstract: A system and method for rider profiling for lightweight vehicles is disclosed herein. The system comprises a lightweight vehicle actuating unit. Load-cells are configured on a deck of the lightweight vehicle for sensing and measuring load acting thereupon for generating a measurement signal. A pressure-pattern collection unit receives the measurement signal to identify and process a load pattern acting on the deck to generate a pressure-pattern signal. A learning model receives the pressure-pattern signal for processing to obtain information associated with a pose of the rider. A riding control is coupled to a riding control database and configured to unit receive the information associated with the pose from the learning model for determining at least one instance of rule violation and computing a decision based on the at least one instance of rule violation.

Abstract: The present disclosure relates to a system and method for automatic vehicle recognition, based on a smart device. The system mainly includes an image capturing device integrated in the smart device, a data storage to store the images captured by the image capturing device and known identity aspects related to a vehicle allotted to a user, and a License plate processing and matching (LPPM) component to perform recognition process. LPPM component includes an identity aspect detector to detect a portion representing the identity aspect, an image processor to enhance the portion and perform Optical Characters Recognition (OCR) to extract character strings from the portion. The character string is compared against a character string of the known identity aspect to verify the vehicle.