Autonomous Vehicle Control

This application area focuses on end-to-end control and operation of self-driving vehicles in real-world environments. It spans sensing the surroundings, understanding road context, predicting other agents’ behavior, making driving decisions, and executing precise vehicle control. The use cases highlight both full-scale intelligent vehicle systems and small-scale test platforms that allow rapid, low-risk experimentation with algorithms before deployment on public roads. It matters because safe, reliable autonomous vehicle control can dramatically reduce accidents, improve traffic flow, and lower operating costs in logistics, ride-hailing, and public transport. AI models fuse data from cameras, lidar, radar, and maps to perceive the environment, plan routes and maneuvers, and control steering, acceleration, and braking. Supporting technologies such as HD mapping, simulation and testing frameworks, and vehicle-to-everything communication are critical to validate performance and close key safety and reliability gaps before large-scale deployment.