2 citations · 2 across the 10 of their papers we have counts for
11 papers
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…
AdaThinkDrive: Adaptive Thinking via Reinforcement Learning for Autonomous Driving
Yuechen Luo, Fang Li, Shaoqing Xu +11
While reasoning technology like Chain of Thought (CoT) has been widely adopted in Vision Language Action (VLA) models, it demonstrates promising capabilities in end to end autonomo…
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…
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…
LEGO-Motion: Learning-Enhanced Grids with Occupancy Instance Modeling for Class-Agnostic Motion Prediction
Kangan Qian, Jinyu Miao, Ziang Luo +7
Accurate and reliable spatial and motion information plays a pivotal role in autonomous driving systems. However, object-level perception models struggle with handling open scenari…
How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic
Kangan Qian, Jinyu Miao, Xinyu Jiao +6
Understanding the spatial dynamics of cars within urban systems is essential for optimizing infrastructure management and resource allocation. Recent empirical approaches for analy…
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…
Enhancing Lane Segment Perception and Topology Reasoning with Crowdsourcing Trajectory Priors
Peijin Jia, Ziang Luo, Tuopu Wen +4
In autonomous driving, recent advances in lane segment perception provide autonomous vehicles with a comprehensive understanding of driving scenarios. Moreover, incorporating prior…
DiffMap: Enhancing Map Segmentation with Map Prior Using Diffusion Model
Peijin Jia, Tuopu Wen, Ziang Luo +9
Constructing high-definition (HD) maps is a crucial requirement for enabling autonomous driving. In recent years, several map segmentation algorithms have been developed to address…