39 citations · 113 across the 28 of their papers we have counts for
6 papers · 1 filter
DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving
Xuemeng Yang, Licheng Wen, Yukai Ma +11
This paper presented DriveArena, the first high-fidelity closed-loop simulation system designed for driving agents navigating in real scenarios. DriveArena features a flexible, mod…
KoMA: Knowledge-driven Multi-agent Framework for Autonomous Driving with Large Language Models
Kemou Jiang, Xuan Cai, Zhiyong Cui +7
Large language models (LLMs) as autonomous agents offer a novel avenue for tackling real-world challenges through a knowledge-driven manner. These LLM-enhanced methodologies excel…
DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Language Models
Renqiu Xia, Song Mao, Xiangchao Yan +19
Scientific documents record research findings and valuable human knowledge, comprising a vast corpus of high-quality data. Leveraging multi-modality data extracted from these docum…
Continuously Learning, Adapting, and Improving: A Dual-Process Approach to Autonomous Driving
Jianbiao Mei, Yukai Ma, Xuemeng Yang +11
Autonomous driving has advanced significantly due to sensors, machine learning, and artificial intelligence improvements. However, prevailing methods struggle with intricate scenar…
OASim: an Open and Adaptive Simulator based on Neural Rendering for Autonomous Driving
Guohang Yan, Jiahao Pi, Jianfei Guo +14
With deep learning and computer vision technology development, autonomous driving provides new solutions to improve traffic safety and efficiency. The importance of building high-q…
LimSim++: A Closed-Loop Platform for Deploying Multimodal LLMs in Autonomous Driving
Daocheng Fu, Wenjie Lei, Licheng Wen +5
The emergence of Multimodal Large Language Models ((M)LLMs) has ushered in new avenues in artificial intelligence, particularly for autonomous driving by offering enhanced understa…