Real-Time Embedded Systems for Autonomous Vehicle Navigation: Scheduling Algorithms and Sensor Fusion Techniques

Abstract

Autonomous vehicles rely on real-time embedded systems to navigate dynamic environments, make decisions, and ensure safety. These systems are composed of hardware and software components that process inputs from sensors and execute tasks under strict timing constraints. A Real-Time Operating System (RTOS) is fundamental in ensuring deterministic performance, which is essential for critical applications such as obstacle avoidance, path planning, and emergency response. In parallel, sensor fusion techniques combine data from multiple modalities like LiDAR, cameras, and GPS to generate an accurate and reliable model of the environment. This paper delves into the development and optimization of real-time scheduling algorithms and sensor fusion frameworks in the context of autonomous vehicle navigation. By analyzing task scheduling strategies, data fusion approaches, system architecture, and path planning, the paper outlines the role of embedded systems in enhancing the safety, responsiveness, and intelligence of self-driving cars.

Keywords: RTOS, LiDAR integration, path planning, safety-critical systems