works on

From the 2 of 22 linked papers with an AI index.

activity
20242026
collaborators
Showing cs.ROShow all

6 papers · 1 filter

cs.RO2025

TrafficMCTS: A Closed-Loop Traffic Flow Generation Framework with Group-Based Monte Carlo Tree Search

Ze Fu, Licheng Wen, Pinlong Cai +3

Traffic flow simulation within the domain of intelligent transportation systems is garnering significant attention, and generating realistic, diverse, and human-like traffic patter…

cs.RO2025

LimSim Series: An Autonomous Driving Simulation Platform for Validation and Enhancement

Daocheng Fu, Naiting Zhong, Xu Han +5

Closed-loop simulation environments play a crucial role in the validation and enhancement of autonomous driving systems (ADS). However, certain challenges warrant significant atten…

cs.RO2024

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…

cs.RO2024

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…

cs.RO2024

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…

cs.RO2024

DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models

Licheng Wen, Daocheng Fu, Xin Li +7

Recent advancements in autonomous driving have relied on data-driven approaches, which are widely adopted but face challenges including dataset bias, overfitting, and uninterpretab…