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researcher

Qi Sun

9 papers hereh-index 131.4k citations28 works total

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

author position
  • middle author7

Across the 7 of 9 papers where every author was matched, so the position is known.

fields
  • eess.SY4
  • cs.LG3
  • cs.RO2
same name
  • Qi Sun — 36 papers, h 12
  • Qi Sun — 26 papers, h 19
  • Qi Sun — 8 papers, h 4
  • Qi Sun — 8 papers, h 7
  • Qi Sun — 7 papers, h 4
  • Qi Sun — 5 papers, h 8

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
20192024
most citedNumerically Stable Dynamic Bicycle Model for Discrete-time Control

8 citations · 12 across the 7 of their papers we have counts for

collaborators
Showing eess.SYShow all

4 papers · 1 filter

eess.SY2021

Recurrent Model Predictive Control

Zhengyu Liu, Jingliang Duan, Wenxuan Wang +5

This paper proposes an off-line algorithm, called Recurrent Model Predictive Control (RMPC), to solve general nonlinear finite-horizon optimal control problems. Unlike traditional…

eess.SY2020★ 8 cited

Numerically Stable Dynamic Bicycle Model for Discrete-time Control

Qiang Ge, Shengbo Eben Li, Qi Sun +1

Dynamic/kinematic model is of great significance in decision and control of intelligent vehicles. However, due to the singularity of dynamic models at low speed, kinematic models h…

eess.SY2020★ 2 cited

Centralized Coordination of Connected Vehicles at Intersections using Graphical Mixed Integer Optimization

Qiang Ge, Qi Sun, Zhen Wang +3

This paper proposes a centralized multi-vehicle coordination scheme serving unsignalized intersections. The whole process consists of three stages: a) target velocity optimization:…

eess.SY2020

Mixed Reinforcement Learning with Additive Stochastic Uncertainty

Yao Mu, Shengbo Eben Li, Chang Liu +4

Reinforcement learning (RL) methods often rely on massive exploration data to search optimal policies, and suffer from poor sampling efficiency. This paper presents a mixed reinfor…

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