14 papers
JARVIS: From Visual Manuals to Autonomous Robotic Assembly
Fan Ding, Fucai Ke, Kevin Yuchen Ma +5
Visual manuals communicate procedural knowledge through stepwise diagrams, but translating these instructions into reliable long-horizon robotic execution remains challenging. We i…
Which Trajectory Is Safer? Combining Rule and Learning for Safety Evaluation
Xuewen Luo, Linlian Jiang, Bo Yu +3
Collision rate remains a dominant metric for evaluating autonomous driving trajectory safety, but its binary nature obscures substantial safety differences among collision-free tra…
KnowDiffuser: A Knowledge-Guided Diffusion Planner with LLM Reasoning
Fan Ding, Xuewen Luo, Fengze Yang +4
Recent advancements in Language Models (LMs) have demonstrated strong semantic reasoning capabilities, enabling their application in high-level decision-making for autonomous drivi…
GSMT: Graph Fusion and Spatiotemporal TaskCorrection for Multi-Bus Trajectory Prediction
Fan Ding, Hwa Hui Tew, Junn Yong Loo +6
Accurate trajectory prediction for buses is crucial in intelligent transportation systems, particularly within urban environments. In developing regions where access to multimodal…
Drive As You Like: Multi-Head Diffusion with Reinforcement Learning for Personalized Driving
Fan Ding, Ding Fan, Xuewen Luo +6
Personalized autonomous driving requires understanding the driving needs implicit in user requests. This is challenging when driving demonstrations lack style annotations and behav…
V2X-UniPool: Unifying Multimodal Perception and Knowledge Reasoning for Autonomous Driving
Xuewen Luo, Fengze Yang, Fan Ding +5
Autonomous driving (AD) has achieved significant progress, yet single-vehicle perception remains constrained by sensing range and occlusions. Vehicle-to-Everything (V2X) communicat…
"What's Happening"- A Human-centered Multimodal Interpreter Explaining the Actions of Autonomous Vehicles
Xuewen Luo, Fan Ding, Ruiqi Chen +3
Public distrust of self-driving cars is growing. Studies emphasize the need for interpreting the behavior of these vehicles to passengers to promote trust in autonomous systems. In…
SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving
Xuewen Luo, Fan Ding, Fengze Yang +4
This study addresses the critical need for enhanced situational awareness in autonomous driving (AD) by leveraging the contextual reasoning capabilities of large language models (L…
ST-HCSS: Deep Spatio-Temporal Hypergraph Convolutional Neural Network for Soft Sensing
Hwa Hui Tew, Fan Ding, Gaoxuan Li +4
Higher-order sensor networks are more accurate in characterizing the nonlinear dynamics of sensory time-series data in modern industrial settings by allowing multi-node connections…
KANS: Knowledge Discovery Graph Attention Network for Soft Sensing in Multivariate Industrial Processes
Hwa Hui Tew, Gaoxuan Li, Fan Ding +5
Soft sensing of hard-to-measure variables is often crucial in industrial processes. Current practices rely heavily on conventional modeling techniques that show success in improvin…
PKRD-CoT: A Unified Chain-of-thought Prompting for Multi-Modal Large Language Models in Autonomous Driving
Xuewen Luo, Fan Ding, Yinsheng Song +2
There is growing interest in leveraging the capabilities of robust Multi-Modal Large Language Models (MLLMs) directly within autonomous driving contexts. However, the high costs an…
Cross-Domain Transfer Learning using Attention Latent Features for Multi-Agent Trajectory Prediction
Jia Quan Loh, Xuewen Luo, Fan Ding +5
With the advancements of sensor hardware, traffic infrastructure and deep learning architectures, trajectory prediction of vehicles has established a solid foundation in intelligen…
Energy-efficient Hybrid Model Predictive Trajectory Planning for Autonomous Electric Vehicles
Fan Ding, Xuewen Luo, Gaoxuan Li +6
To tackle the twin challenges of limited battery life and lengthy charging durations in electric vehicles (EVs), this paper introduces an Energy-efficient Hybrid Model Predictive P…
FedBChain: A Blockchain-enabled Federated Learning Framework for Improving DeepConvLSTM with Comparative Strategy Insights
Gaoxuan Li, Chern Hong Lim, Qiyao Ma +4
Recent research in the field of Human Activity Recognition has shown that an improvement in prediction performance can be achieved by reducing the number of LSTM layers. However, t…