Object Identification for Autonomous Vehicles using Machine Learning

Abstract

Autonomous vehicles (AVs) hold immense promise in reshaping transportation by enhancing safety and efficiency. A critical challenge lies in accurately identifying objects at long ranges, particularly in adverse conditions. This study explores the application of machine learning algorithms for the long-range object identification in AVs. Methodologically, a diverse dataset encompassing real-world data from multiple sensors is curated and preprocessed. Various machine learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep reinforcement learning (DRL), are trained and evaluated using this dataset, with metrics such as accuracy, precision, recall, and F1 score employed for assessment. Results indicate promising performance, with sensor fusion techniques augmenting accuracy and reliability. Ethical considerations are addressed, emphasizing safety and bias mitigation. Limitations of current models in terms of robustness and generalization are analyzed, alongside proposals for enhancement. Findings underscore the significance of sensor fusion, model validation, and data diversity in ensuring AV safety and reliability. In conclusion, this research advances the field of AV perception systems, laying a foundation for safer and more efficient autonomous transportation.