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20232026
most citedGPT-Driver: Learning to Drive with GPT

39 citations · 87 across the 19 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

DriveReferee: Geometric Safety Verdicts Need Not Be Learned for Driving World-Action Models

Fengcheng Yu, Dhruv Parikh, Junjie Ye +5

Generative world-action models (WAMs) jointly generate future video and vehicle actions, while their action branches remain primarily optimized by expert imitation. Yet imitation p…

cs.CV2025

ROVER: Benchmarking Reciprocal Cross-Modal Reasoning for Omnimodal Generation

Yongyuan Liang, Wei Chow, Feng Li +7

Unified multimodal models (UMMs) have emerged as a powerful paradigm for seamlessly unifying text and image understanding and generation. However, prevailing evaluations treat thes…

cs.CV2025★ 17 cited

UrbanVideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spaces

Baining Zhao, Jianjie Fang, Zichao Dai +8

Large multimodal models exhibit remarkable intelligence, yet their embodied cognitive abilities during motion in open-ended urban 3D space remain to be explored. We introduce a ben…

cs.CV2025★ 3 cited

PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding

Wei Chow, Jiageng Mao, Boyi Li +3

Understanding the physical world is a fundamental challenge in embodied AI, critical for enabling agents to perform complex tasks and operate safely in real-world environments. Whi…

cs.CV2023★ 15 cited

A Language Agent for Autonomous Driving

Jiageng Mao, Junjie Ye, Yuxi Qian +2

Human-level driving is an ultimate goal of autonomous driving. Conventional approaches formulate autonomous driving as a perception-prediction-planning framework, yet their systems…

cs.CV2023★ 39 cited

GPT-Driver: Learning to Drive with GPT

Jiageng Mao, Yuxi Qian, Junjie Ye +2

We present a simple yet effective approach that can transform the OpenAI GPT-3.5 model into a reliable motion planner for autonomous vehicles. Motion planning is a core challenge i…

cs.CV2023★ 9 cited

SSCBench: A Large-Scale 3D Semantic Scene Completion Benchmark for Autonomous Driving

Yiming Li, Sihang Li, Xinhao Liu +11

Monocular scene understanding is a foundational component of autonomous systems. Within the spectrum of monocular perception topics, one crucial and useful task for holistic 3D sce…