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Yining Shi

Tsinghua University

13 papers hereh-index 11505 citations29 works total

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

author position
  • first author4
  • middle author4

Across the 8 of 13 papers where every author was matched, so the position is known.

fields
  • cs.CV9
  • cs.RO4
affiliations
  • Tsinghua University
ORCID 0000-0003-2926-925X
same name
  • Yining Shi — 4 papers, h 2
  • Yining Shi — 2 papers, h 5

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 cs.ROShow all

4 papers · 1 filter

cs.RO2026

4DLidarOpen: An Open 4D FMCW Lidar Dataset for Motion-Aware Autonomous Driving

Kane Qian, Xin Zhao, Yining Shi +7

We present 4DLidarOpen, a large-scale open multi-modal dataset for autonomous driving, centered on 4D frequency-modulated continuous-wave (FMCW) Lidar sensing. Unlike conventional…

cs.RO2025

AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving

Kangan Qian, Sicong Jiang, Yang Zhong +18

Vision-Language Models (VLMs) show promise for autonomous driving, yet their struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate…

cs.RO2025

FASIONAD++ : Integrating High-Level Instruction and Information Bottleneck in FAt-Slow fusION Systems for Enhanced Safety in Autonomous Driving with Adaptive Feedback

Kangan Qian, Ziang Luo, Sicong Jiang +16

Ensuring safe, comfortable, and efficient planning is crucial for autonomous driving systems. While end-to-end models trained on large datasets perform well in standard driving sce…

cs.RO2024

FASIONAD : FAst and Slow FusION Thinking Systems for Human-Like Autonomous Driving with Adaptive Feedback

Kangan Qian, Zhikun Ma, Yangfan He +13

Ensuring safe, comfortable, and efficient navigation is a critical goal for autonomous driving systems. While end-to-end models trained on large-scale datasets excel in common driv…

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