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619 results on '"Li, Shengyi"'

4. CILF:Causality Inspired Learning Framework for Out-of-Distribution Vehicle Trajectory Prediction

Author

Li, Shengyi, Xue, Qifan, Zhang, Yezhuo, and Li, Xuanpeng

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Trajectory prediction is critical for autonomous driving vehicles. Most existing methods tend to model the correlation between history trajectory (input) and future trajectory (output). Since correlation is just a superficial description of reality, these methods rely heavily on the i.i.d. assumption and evince a heightened susceptibility to out-of-distribution data. To address this problem, we propose an Out-of- Distribution Causal Graph (OOD-CG), which explicitly defines the underlying causal structure of the data with three entangled latent features: 1) domain-invariant causal feature (IC), 2) domain-variant causal feature (VC), and 3) domain-variant non-causal feature (VN ). While these features are confounded by confounder (C) and domain selector (D). To leverage causal features for prediction, we propose a Causal Inspired Learning Framework (CILF), which includes three steps: 1) extracting domain-invariant causal feature by means of an invariance loss, 2) extracting domain variant feature by domain contrastive learning, and 3) separating domain-variant causal and non-causal feature by encouraging causal sufficiency. We evaluate the performance of CILF in different vehicle trajectory prediction models on the mainstream datasets NGSIM and INTERACTION. Experiments show promising improvements in CILF on domain generalization.

Published
2023

5. Hierarchical Motion Encoder-Decoder Network for Trajectory Forecasting

Author

Xue, Qifan, Li, Shengyi, Li, Xuanpeng, Zhao, Jingwen, and Zhang, Weigong

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Trajectory forecasting plays a pivotal role in the field of intelligent vehicles or social robots. Recent works focus on modeling spatial social impacts or temporal motion attentions, but neglect inherent properties of motions, i.e. moving trends and driving intentions. This paper proposes a context-free Hierarchical Motion Encoder-Decoder Network (HMNet) for vehicle trajectory prediction. HMNet first infers the hierarchical difference on motions to encode physically compliant patterns with high expressivity of moving trends and driving intentions. Then, a goal (endpoint)-embedded decoder hierarchically constructs multimodal predictions depending on the location-velocity-acceleration-related patterns. Besides, we present a modified social pooling module which considers certain motion properties to represent social interactions. HMNet enables to make the accurate, unimodal/multimodal and physically-socially-compliant prediction. Experiments on three public trajectory prediction datasets, i.e. NGSIM, HighD and Interaction show that our model achieves the state-of-the-art performance both quantitatively and qualitatively. We will release our code here: https://github.com/xuedashuai/HMNet.

Published
2021

7. CILF: Causality Inspired Learning Framework for Out-of-Distribution Vehicle Trajectory Prediction

Author

Li, Shengyi, Xue, Qifan, Zhang, Yezhuo, Li, Xuanpeng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Lu, Huimin, editor, Blumenstein, Michael, editor, Cho, Sung-Bae, editor, Liu, Cheng-Lin, editor, Yagi, Yasushi, editor, and Kamiya, Tohru, editor

Published
2023
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12. Prospects for the database development in electrical engineering materials.

Author

Sheng, Peng, Li, Shengyi, Xu, Li, Wang, Bo, Bai, Huitao, Li, Hui, and Xue, Qing

Subjects
*ELECTRICAL engineering materials, *GENOME editing, *DATABASE design, *DATABASES, *ARTIFICIAL intelligence
Abstract

With the proposal and development of the Material Genome Engineering program, artificial intelligence has played a significant role in accelerating the research and development of new materials. In the field of electrical engineering materials, high-throughput experimental and computational methods provide a huge amount of data. It also poses new challenges to how to manage material data scientifically and efficiently. Database technology has become a hot topic for material scientists and engineers. This paper makes a comprehensive overview of the development, demand analysis and application of database technology in the electrical engineering materials, and discusses the existing problems and the future development trend of the database. Compared with many materials, such as energy materials, catalytic materials, biomedical materials, etc., the electrical material database still has a long way to go in the process of database platform construction, management and operation, and practical application. However, driven by governmental support and market demand, the construction of electrical material database will gradually improve and play an important role in the data-driven new materials researches. [ABSTRACT FROM AUTHOR]

Published
2024
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