In recent years, the rapid development of autonomous vehicles (AVs) has necessitated the need for high-quality perception systems. Perception is a fundamental requirement for AVs, with cameras and LiDARs being commonly used sensors for environmental understanding and localization. However, there is a research gap in assessing the quality of AVs perception systems. To address this gap, this dissertation proposes a novel paradigm for evaluating AVs perception quality by studying the perception quality of cameras and LiDARs sensors. Our proposed paradigm aims to provide a comprehensive assessment of the quality of perception systems used in AVs.To achieve our research goals, we first validate the concept of surrounding environmental complexity through subjective experiments that rate complexity scores. In this study, we propose a neural network to classify complexity. Subsequently, we study image-based perception quality assessment by using image saliency and 2D object detection algorithms to create an image-based quality index. We then develop a neural network model to regress the proposed quality index score. Furthermore, we extend our research to LiDAR-based point cloud quality assessment by using the image-based saliency map as guidance to generate a point cloud quality index score. We then develop a neural network model to regress the score. Finally, we validate the proposed perception quality index with a novel designed AVs perception algorithm. In conclusion, this dissertation makes a significant contribution to the field of AVs perception by proposing a new paradigm for assessing perception quality. Our research findings can be used to improve the overall performance and safety of AVs, which has significant implications for the transportation industry and society as a whole. / Doctor of Philosophy / This dissertation delves into the fundamentals of autonomous vehicles (AVs), which is perception, with the aim of developing a new paradigm for evaluating the quality of perception algorithms. AVs are the dream of humanity, and perception is the fundamental requirement for achieving their full potential. Our research proposes a new approach to assessing the quality of perception algorithms, which can have significant implications for the performance and safety of AVs. By studying the perception algorithm quality, we aim to identify areas for improvement, leading to better AV performance and enhancing user trust. Our findings highlight the importance of perception in the development of AVs and demonstrate the need for continuous evaluation and improvement of the perception algorithms used in AVs.

<p dir="ltr">Accurate 3D point clouds are crucial in various fields, and the advancement of software algorithms has facilitated the reconstruction of 3D models from high-quality images. Notably, both single-camera and multi-camera systems have gained popularity in obtaining these images. While single-camera setups offer simplicity and cost-effectiveness, multi-camera systems provide a broader field of view and improved coverage. However, a crucial gap persists, a lack of direct comparison and comprehensive analysis regarding the quality of point clouds acquired from each system. This thesis aims to bridge this gap by evaluating the point cloud quality obtained from both single-camera and multi-camera systems, considering various factors such as lighting conditions, camera settings, and the stability of multi-camera setup in the 3D reconstruction process. Our research also aims to provide insights into how these factors influence the quality and performance of the reconstructed point clouds. By understanding the strengths and limitations of each system, researchers and professionals can make informed decisions when selecting the most suitable 3D imaging approach for their specific applications. To achieve these objectives, we designed and utilized a custom rig with three vertically stacked cameras, each equipped with a fixed camera lens, and maintained uniform lighting conditions. Additionally, we employed a single-camera system with a zoom lens and non uniform lighting conditions. Through noise analysis, our results revealed several crucial findings. The single-camera system exhibited relatively higher noise levels, likely due to non-uniform lighting and the use of a zoom lens. In contrast, the multi-camera system demonstrated lower noise levels, which can be attributed to well-lit conditions and the use of fixed lenses. However, within the multi-camera system, instances of significant instability led to a substantial increase in noise levels in the reconstructed point cloud compared to more stable conditions. Our noise analysis showed the multi-camera system preformed better compared to the single-camera system in terms of noise quality. However, it is crucial to recognize that noise detection also revealed the influence of factors like lighting conditions, camera calibration and camera stability of multi-camera systems on the reconstruction process.</p>