2 citations · 3 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Computing Transition Pathways for the Study of Rare Events Using Deep Reinforcement Learning
Bo Lin, Yangzheng Zhong, Weiqing Ren
Understanding the transition events between metastable states in complex systems is an important subject in the fields of computational physics, chemistry and biology. The transiti…
Double A3C: Deep Reinforcement Learning on OpenAI Gym Games
Yangxin Zhong, Jiajie He, Lingjie Kong
Reinforcement Learning (RL) is an area of machine learning figuring out how agents take actions in an unknown environment to maximize its rewards. Unlike classical Markov Decision…