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Fan Ding

14 papers hereh-index 576 citations15 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author3
  • middle author9

Across the 12 of 14 papers where every author was matched, so the position is known.

fields
  • cs.RO6
  • cs.LG4
  • cs.AI3
  • cs.HC1
same name
  • Fan Ding — 28 papers, h 12
  • Fan Ding — 4 papers, h 7
  • Fan Ding — 2 papers, h 5
  • Fan Ding — 2 papers, h 1
  • Fan Ding — 1 paper, h 14
  • Fan Ding — 1 paper, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
collaborators
Showing 2024Show all

4 papers · 1 filter

cs.RO2024

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…

cs.AI2024

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…

cs.RO2024

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…

cs.LG2024

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…

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