Abstract: A method and system for displaying indications for two-wheeled vehicles is disclosed herein. The system comprises a scooter processing unit. A rider profile database is stored at a memory coupled to the scooter processing unit. A route prediction unit is configured to predict a route of the two-wheeled vehicle based on the rider profile. A scooter motion tracking unit is configured to receive a set of sensing input parameters from at least one sensor mounted aboard the two-wheeled vehicle and a Control interface (CI), and for detecting a start instance and an end instance of a manoeuvre being performed by the two-wheeled vehicle. An indication control unit, communicatively coupled to the scooter motion tracking unit, is configured to receive a trigger signal corresponding to the start instance, and an end signal corresponding to the end instance for controlling the operation of an LED indicator.

Abstract: An image classification system and method is used to detect the parking status of lightweight vehicles, such as kick scooters. The system and method uses a deep learning model to analyze and classify ambiguous parking states that are likely to be encountered by lightweight vehicles, which are small and light enough to be parked in many different environments.

Abstract: A vehicle collision avoidance and driver notification system includes an object detection unit configured to detect environmental obstacles and a collision detection unit for assessing risk of collision. Depending on risk assessment, a collision avoidance unit gives feedback to the driver or directly interacts with the vehicle engine.