Predicting Road Crossing Behaviour using Pose Detection and Sequence Modelling

The world is rapidly advancing toward a future where artificial intelligence (AI) takes a central role in many everyday activities. In business, for example, robots have become indispensable in manufacturing processes and warehouse management. These robots efficiently handle tasks such as stacking and removing items, o ptimizing various business operations. In aviation, autopilot systems have been a standard feature in airplanes for many years, enhancing flight safety and efficiency. Similarly, in many developed countries, vehicles equipped with autopilot capabilities ar e becoming increasingly common. These self - driving vehicles are designed with an array of sensors and high - resolution cameras to monitor their surroundings, detect objects, and take necessary actions to prevent collisions or accidents. While these autonomous vehicles perform admirably on highways where the primary concern is other vehicles, they face significant challenges in busy urban environments. In such settings, it is often advisable for drivers to switch from autopilot to manual c ontrol. This is particularly crucial in bustling market areas where pedestrian behaviour can be unpredictable.