AI-Based Autonomous Vehicle Perception Systems

Abstract

An autonomous vehicle perception system plays a fundamental role in the process of scene understanding, which is identified as a primary means to interpret the surrounding world for intelligent systems with the potential to communicate effectively with social intelligence. The system of a particular vehicle supervises, captures, and processes environmental data in real time using input data from multiple sensor devices. It works as a centralized processor and integrates relevant and diversified data from sensor inputs by employing data fusion models to acquire an accurate environment model with reduced uncertainties. The autonomous vehicle perception system comprises several essential components such as scene perception, object detection and localization, representation of the environment, decision-making, and path planning algorithms. The output information of the perception system provides essential and effective support for vehicle decision-making in terms of speed and direction by assisting the vehicle velocity regulator and transferring signals to the brakes, steering, and accelerator. Furthermore, object detection, identification, and tracking systems are imperative for the safe navigation of an autonomous vehicle that interacts with its dynamic environment. The latest advancements in disruptive technologies such as deep learning and AI demonstrate improved performance compared with prior traditional solutions, particularly under complex conditions. Hence, there has been substantial work in the area of AI-driven perception systems for autonomous vehicles in recent years. With the arrival of the AI era, deep learning-based perception models are substituting classical machine learning and multi-objective decision-making models, whereby robust, precise, and computationally efficient state-of-the-art designs from object tracking to scene depiction are developed, promising future real-world applications.