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10 results on '"Chan, Chingyao"'

1. A Novel Graph based Trajectory Predictor with Pseudo Oracle

Author

Yang, Biao, Yan, Guocheng, Wang, Pin, Chan, Chingyao, Song, Xiang, and Chen, Yang

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Pedestrian trajectory prediction in dynamic scenes remains a challenging and critical problem in numerous applications, such as self-driving cars and socially aware robots. Challenges concentrate on capturing pedestrians' motion patterns and social interactions, as well as handling the future uncertainties. Recent studies focus on modeling pedestrians' motion patterns with recurrent neural networks, capturing social interactions with pooling-based or graph-based methods, and handling future uncertainties by using random Gaussian noise as the latent variable. However, they do not integrate specific obstacle avoidance experience (OAE) that may improve prediction performance. For example, pedestrians' future trajectories are always influenced by others in front. Here we propose GTPPO (Graph-based Trajectory Predictor with Pseudo Oracle), an encoder-decoder-based method conditioned on pedestrians' future behaviors. Pedestrians' motion patterns are encoded with a long short-term memory unit, which introduces the temporal attention to highlight specific time steps. Their interactions are captured by a graph-based attention mechanism, which draws OAE into the data-driven learning process of graph attention. Future uncertainties are handled by generating multi-modal outputs with an informative latent variable. Such a variable is generated by a novel pseudo oracle predictor, which minimizes the knowledge gap between historical and ground-truth trajectories. Finally, the GTPPO is evaluated on ETH, UCY and Stanford Drone datasets, and the results demonstrate state-of-the-art performance. Besides, the qualitative evaluations show successful cases of handling sudden motion changes in the future. Such findings indicate that GTPPO can peek into the future., Comment: 17 apges, 8 figures

Published
2020
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7. Support vector machines for the identification of real-time driving distraction using in-vehicle information systems.

Author

Ma, Yanli, Gu, Gaofeng, Yin, Biqing, Qi, Shouming, Chen, Ke, and Chan, Chingyao

Subjects
SUPPORT vector machines, DISTRACTION, INFORMATION storage & retrieval systems, RADIAL basis functions, INFORMATION resources management, KERNEL functions, DISTRACTED driving
Abstract

IVIS (In-vehicle Information System) is an important factor causing driver distraction. To study the driver distraction detection method when operating IVIS, the effectiveness of driving performance indicators in the identification of driving distraction was verified by the method of variance analysis. Forty participants were selected to conduct the driver distraction experiment when operating IVIS, and the data of driving performance indicators such as eye movement, speed, et al. were obtained. According to the driving performance data of IVIS, a real-time detection of distraction based on driving performance was built by using support vector machine, and three kernel functions of the model were conducted comparative analysis and validation. The results show that SVM models can effectively evaluate the degree of drivers' distraction. At the same time, when the Radial Basis Function is used as a kernel function, the accuracy for recognizing driver distraction is 89.9%, which is higher than when the sigmoid polynomial kernel function and SAVE-IT model are used. The research could be applied in the design of adaptive in-vehicle systems and the evaluation of driving distraction, providing theoretical support and reference for the development of vehicle-mounted information systems and the management of driver distraction prevention measures. We verify the effectiveness of driving performance indicators as decision variables for driving distraction. We use driving performance indicators as inputs to a distraction detection algorithm. We examine changes in the level of information about driving distraction. SVMs method can be used to detect driving distraction during IVIS operations in real time. The accuracy for using Radial Basis Function to recognizing the driver's level of distraction is higher than the Radial Basis Function. [ABSTRACT FROM AUTHOR]

Published
2022
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10. Dynamic Model of a Fluctuating Rocker-Arm Ratio Cam System

Author

Chan, Chingyao and Pisano, Albert P.

Abstract

This study is focused upon one example from a class of cam systems with a fluctuating rocker-arm ratio, known as “finger follower” cam systems. This class of cam systems is typically built in practice with a hydraulic tappet as the pivot-end support for the finger-follower, and the rocker-arm ratio varies as much as 34 percent from the baseline value during the cam cycle. A six-degree-of-freedom dynamic model is formulated to predict the forces as well as the motions of the cam system components. Successful dynamic modeling of such a system requires an accurate model for the hydraulic tappet as well as for the cam system, and so, six separate dynamic phenomena are identified in the tappet and the resulting nonlinear dynamics included in the cam system model. Lateral and rotational motions, as well as the customary longitudinal displacement, are admitted for the valve, and it is found that although the extra degrees of freedom change the cam contact force but little, they strongly influence where in the cam system toss and impact occur.

Published
1987
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