Browsing by Author "Zhang, Ce"

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    Autonomous Vehicle Perception Quality Assessment
    Zhang, Ce (Virginia Tech, 2023-06-29)
    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.
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    A simulation-based comparative study on lateral characteristics of trucks with double and triple trailers
    Chen, Yang; Peterson, Andrew W.; Zhang, Ce; Ahmadian, Mehdi (Inderscience Publishers, 2019-01-01)
    This paper investigates the lateral stability and manoeuvrability in long combination vehicles (LCVs), namely semi-trucks with 28-ft doubles, 28- ft triples, and 33-ft doubles, using TruckSim. In particular, the likelihood of rollovers, rearward amplification, and off-tracking are analysed among those LCVs using the multi-domain dynamic models developed in TruckSim. The efforts to validate the truck dynamic model against test results are also included. The simulation results show that trucks with triple trailers exhibit a larger rearward amplification, higher likelihood of rollovers, and larger offtracking than trucks with double trailers. Additionally, the results indicate that increasing the trailer length from 28 to 33 feet does not increase the likelihood of rollovers or the rearward amplification. In fact, the longer trailers provide a slight amount of additional roll stability due to their longer wheelbase.
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    A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis
    Zhang, Ce; Eskandarian, Azim (IEEE, 2021-07-01)
    The driver's cognitive and physiological states affect his/her ability to control the vehicle. Thus, these driver states are essential to the safety of automobiles. The design of advanced driver assistance systems (ADAS) or autonomous vehicles will depend on their ability to interact effectively with the driver. A deeper understanding of the driver state is, therefore, paramount. Electroencephalography (EEG) is proven to be one of the most effective methods for driver state monitoring and human error detection. This paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three decades. First, the commonly used EEG system setup for driver state studies is introduced. Then, the EEG signal preprocessing, feature extraction, and classification algorithms for driver state detection are reviewed. Finally, EEG-based driver state monitoring research is reviewed in-depth, and its future development is discussed. It is concluded that the current EEG-based driver state monitoring algorithms are promising for safety applications. However, many improvements are still required in EEG artifact reduction, real-time processing, and between-subject classification accuracy.