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most citedA Survey on Multimodal Large Language Models for Autonomous Driving

27 citations · 62 across the 21 of their papers we have counts for

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5 papers · 1 filter

astro-ph.IM2025★ 1 cited

AstroMMBench: A Benchmark for Evaluating Multimodal Large Language Models Capabilities in Astronomy

Jinghang Shi, Xiaoyu Tang, Yang Huang +4

Astronomical image interpretation presents a significant challenge for applying multimodal large language models (MLLMs) to specialized scientific tasks. Existing benchmarks focus…

cs.RO2025

SIMSplat: Language-Aligned 4D Gaussian Splatting for Driving Scenario Generation

Sung-Yeon Park, Adam Lee, Juanwu Lu +6

Driving scene manipulation using real-world sensor data has emerged as a promising alternative to traditional driving simulators. Despite advances in language control and neural sc…

cs.RO2025

On Learning Closed-Loop Probabilistic Multi-Agent Simulator

Juanwu Lu, Rohit Gupta, Ahmadreza Moradipari +3

The rapid iteration of autonomous vehicle (AV) deployments leads to increasing needs for building realistic and scalable multi-agent traffic simulators for efficient evaluation. Re…

cs.RO2025

A Hierarchical Test Platform for Vision Language Model (VLM)-Integrated Real-World Autonomous Driving

Yupeng Zhou, Can Cui, Juntong Peng +5

Vision-Language Models (VLMs) have demonstrated notable promise in autonomous driving by offering the potential for multimodal reasoning through pretraining on extensive image-text…

cs.LG2025

Accelerating Inference of Discrete Autoregressive Normalizing Flows by Selective Jacobi Decoding

Jiaru Zhang, Juanwu Lu, Xiaoyu Wu +2

Discrete normalizing flows are promising generative models with advantages such as analytical log-likelihood computation and end-to-end training. However, the architectural constra…