A5102008511- Advanced Neural Network Applications
- Advanced Bandit Algorithms Research
- Autonomous Vehicle Technology and Safety
- Time Series Analysis and Forecasting
- Robotic Mechanisms and Dynamics
- Video Surveillance and Tracking Methods
- Iterative Learning Control Systems
- Human Pose and Action Recognition
- Multimodal Machine Learning Applications
- Data Stream Mining Techniques
- Image Processing and 3D Reconstruction
- Recommender Systems and Techniques
- Intelligent Tutoring Systems and Adaptive Learning
- Anomaly Detection Techniques and Applications
- Traffic and Road Safety
- Piezoelectric Actuators and Control
- Advanced Vision and Imaging
- Advanced Graph Neural Networks
Tongji University
2023
Chinese University of Hong Kong
2008
The multi-modality and stochastic characteristics of human behavior make motion prediction a highly challenging task, which is critical for autonomous driving. While deep learning approaches have demonstrated their great potential in this area, it still remains unsolved to establish connection between multiple driving scenes (e.g., merging, roundabout, intersection) the design models. Current learning-based methods typically use one unified model predict trajectories different scenarios, may...
Retrieval Augmented Generation (RAG) has proven to be highly effective in boosting the generative performance of language model knowledge-intensive tasks. However, existing RAG framework either indiscriminately perform retrieval or rely on rigid single-class classifiers select methods, leading inefficiencies and suboptimal across queries varying complexity. To address these challenges, we propose a reinforcement learning-based that dynamically selects most suitable strategy based query % our...
Driving scene understanding is to obtain compre-hensive information through the sensor data and provide a basis for downstream tasks, which indispensable safety of self-driving vehicles. Specific perception such as object detection graph generation, are commonly used. However, results these tasks only equivalent characterization sampling from high-dimensional features, not sufficient represent scenario. In addition, goal inconsistent with human driving that just focuses on what may affect...
Despite the superior performance of Large language models on many NLP tasks, they still face significant limitations in memorizing extensive world knowledge. Recent studies have demonstrated that leveraging Retrieval-Augmented Generation (RAG) framework, combined with Knowledge Graphs encapsulate factual data a structured format, robustly enhances reasoning capabilities LLMs. However, deploying such systems real-world scenarios presents challenges: continuous evolution non-stationary...
One essential task for autonomous driving is to accurately predict the future motions of surrounding traffic agents. Recently, Graph Neural Network (GNN) approaches have shown potential in motion prediction due fact that information scenario can be inherently formed into a graph structure. However, existing are limited using static representing states current timestamp, ignoring scenario's derivation. In this work, we propose Future (FGNet) two-stage GNN-based model accurate and real-time...
Driving scene understanding is to obtain comprehensive information through the sensor data and provide a basis for downstream tasks, which indispensable safety of self-driving vehicles. Specific perception such as object detection graph generation, are commonly used. However, results these tasks only equivalent characterization sampling from high-dimensional features, not sufficient represent scenario. In addition, goal inconsistent with human driving that just focuses on what may affect...