activity
20242026
most citedA Survey on Vision-Language-Action Models for Autonomous Driving

2 citations · 2 across the 14 of their papers we have counts for

collaborators
Showing 2025Show all

6 papers · 1 filter

cs.RO2025

MTRDrive: Memory-Tool Synergistic Reasoning for Robust Autonomous Driving in Corner Cases

Ziang Luo, Kangan Qian, Jiahua Wang +13

Vision-Language Models(VLMs) have demonstrated significant potential for end-to-end autonomous driving, yet a substantial gap remains between their current capabilities and the rel…

cs.CV2025

EditReward: A Human-Aligned Reward Model for Instruction-Guided Image Editing

Keming Wu, Sicong Jiang, Max Ku +3

Recently, we have witnessed great progress in image editing with natural language instructions. Several closed-source models like GPT-Image-1, Seedream, and Google-Nano-Banana have…

cs.LG2025

EvaDrive: Evolutionary Adversarial Policy Optimization for End-to-End Autonomous Driving

Siwen Jiao, Kangan Qian, Hao Ye +12

Autonomous driving faces significant challenges in achieving human-like iterative decision-making, which continuously generates, evaluates, and refines trajectory proposals. Curren…

cs.CV2025★ 2 cited

A Survey on Vision-Language-Action Models for Autonomous Driving

Sicong Jiang, Zilin Huang, Kangan Qian +17

The rapid progress of multimodal large language models (MLLM) has paved the way for Vision-Language-Action (VLA) paradigms, which integrate visual perception, natural language unde…

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…