Advancing Object Detection in Autonomous Controllers: Integrating SIL and HIL Testing for Safety and Efficacy in ADAS Systems

Abstract

This research delves into the critical domain of object detection for autonomous controllers, an indispensable aspect of modern self-driving cars and advanced driver assistance systems (ADAS). The effective detection of principal objects such as passenger cars and road signs is fundamental to ensuring the safety and functionality of autonomous vehicles. Utilizing a multifaceted approach, this study integrates cutting-edge technologies, including Software in the Loop (SIL) and Hardware in the Loop (HIL) testing, to enhance object detection capabilities. SIL and HIL testing are pivotal in refining and validating the performance of object detection systems under varying conditions. The research begins by elucidating the sensory infrastructure of autonomous controllers, comprising cameras, LiDAR, radar, and ultrasonic sensors. These sensors act as the eyes and ears of the autonomous system, continuously gathering data from the environment. Object detection is then addressed through state-of-the-art machine learning algorithms, primarily Convolutional Neural Networks (CNNs). These algorithms meticulously analyze sensor data to classify objects into categories, encompassing passenger cars, pedestrians, bicycles, and diverse road signs. Furthermore, the study emphasizes the significance of traffic sign detection, a crucial component for ensuring road safety. To ensure real-world applicability, object tracking is examined, enabling the prediction of object movements, and facilitating informed decision-making for the autonomous controller. The research underscores the importance of dynamic control actions, where decisions are transformed into precise steering, braking, and acceleration maneuvers. The research concludes by recognizing the ongoing challenges posed by real-world driving conditions, which necessitate the continuous adaptation and improvement of object detection systems. The amalgamation of SIL and HIL testing emerges as an innovative approach to validate and enhance object detection in autonomous controllers, ensuring their safety and efficacy in an ever-evolving landscape of transportation technology.