activity
20232026
most citedLLM4Drive: A Survey of Large Language Models for Autonomous Driving

19 citations · 43 across the 5 of their papers we have counts for

collaborators

6 papers

cs.RO2026

GuidedVLA: Specifying Task-Relevant Factors via Plug-and-Play Action Attention Specialization

Xiaosong Jia, Bowen Yang, Zuhao Ge +17

Vision-Language-Action (VLA) models aim for general robot learning by aligning action as a modality within powerful Vision-Language Models (VLMs). Existing VLAs rely on end-to-end…

cs.RO2024

Reasoning Multi-Agent Behavioral Topology for Interactive Autonomous Driving

Haochen Liu, Li Chen, Yu Qiao +2

Autonomous driving system aims for safe and social-consistent driving through the behavioral integration among interactive agents. However, challenges remain due to multi-agent sce…

cs.CV2023★ 1 cited

Visual Point Cloud Forecasting enables Scalable Autonomous Driving

Zetong Yang, Li Chen, Yanan Sun +1

In contrast to extensive studies on general vision, pre-training for scalable visual autonomous driving remains seldom explored. Visual autonomous driving applications require feat…

cs.CV2023★ 5 cited

Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future

Hongyang Li, Yang Li, Huijie Wang +16

With the continuous maturation and application of autonomous driving technology, a systematic examination of open-source autonomous driving datasets becomes instrumental in fosteri…

cs.AI2023★ 19 cited

LLM4Drive: A Survey of Large Language Models for Autonomous Driving

Zhenjie Yang, Xiaosong Jia, Hongyang Li +1

Autonomous driving technology, a catalyst for revolutionizing transportation and urban mobility, has the tend to transition from rule-based systems to data-driven strategies. Tradi…

cs.CV2023★ 18 cited

OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping

Huijie Wang, Tianyu Li, Yang Li +13

Accurately depicting the complex traffic scene is a vital component for autonomous vehicles to execute correct judgments. However, existing benchmarks tend to oversimplify the scen…