1 citations · 2 across the 12 of their papers we have counts for
13 papers
FleetAgent: Teleoperation Assistant for Autonomous Fleets via Vectorized V2N Messages
Juntong Peng, Qi Chen, Deyuan Qu +3
Large-scale autonomous fleets rely on teleoperation to resolve rare failures, yet streaming raw sensor data from many vehicles is costly, and remote operators can only monitor a li…
OmniV2X: A Generative Foundation Planner for Efficient End-to-End Cooperative Driving
Juntong Peng, Juanwu Lu, Yupeng Zhou +3
We present OmniV2X, a generative foundation model for vehicle-to-everything (V2X) cooperative driving. The model directly interprets independent context sequences comprising multi-…
Efficient and Explainable End-to-End Autonomous Driving via Masked Vision-Language-Action Diffusion
Jiaru Zhang, Manav Gagvani, Can Cui +3
Large Language Models (LLMs) and Vision-Language Models (VLMs) have emerged as promising candidates for end-to-end autonomous driving. However, these models typically face challeng…
QuantV2X: A Fully Quantized Multi-Agent System for Cooperative Perception
Seth Z. Zhao, Huizhi Zhang, Zhaowei Li +11
Cooperative perception through Vehicle-to-Everything (V2X) communication offers significant potential for enhancing vehicle perception by mitigating occlusions and expanding the fi…
ViLaD: A Large Vision Language Diffusion Framework for End-to-End Autonomous Driving
Can Cui, Yupeng Zhou, Juntong Peng +6
End-to-end autonomous driving systems built on Vision Language Models (VLMs) have shown significant promise, yet their reliance on autoregressive architectures introduces some limi…
Communication-Efficient Multi-Agent 3D Detection via Hybrid Collaboration
Yue Hu, Juntong Peng, Yunqiao Yang +1
Collaborative 3D detection can substantially boost detection performance by allowing agents to exchange complementary information. It inherently results in a fundamental trade-off…
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…
Generative AI for Autonomous Driving: Frontiers and Opportunities
Yuping Wang, Shuo Xing, Cui Can +46
Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation,…
On-Board Vision-Language Models for Personalized Autonomous Vehicle Motion Control: System Design and Real-World Validation
Can Cui, Zichong Yang, Yupeng Zhou +11
Personalized driving refers to an autonomous vehicle's ability to adapt its driving behavior or control strategies to match individual users' preferences and driving styles while m…
LLM4AD: Large Language Models for Autonomous Driving -- Concept, Review, Benchmark, Experiments, and Future Trends
Can Cui, Yunsheng Ma, Sung-Yeon Park +14
With the broader adoption and highly successful development of Large Language Models (LLMs), there has been growing interest and demand for applying LLMs to autonomous driving tech…
Communication-Efficient Collaborative Perception via Information Filling with Codebook
Yue Hu, Juntong Peng, Sifei Liu +3
Collaborative perception empowers each agent to improve its perceptual ability through the exchange of perceptual messages with other agents. It inherently results in a fundamental…
Compatible Transformer for Irregularly Sampled Multivariate Time Series
Yuxi Wei, Juntong Peng, Tong He +4
To analyze multivariate time series, most previous methods assume regular subsampling of time series, where the interval between adjacent measurements and the number of samples rem…
Graph-based Decentralized Task Allocation for Multi-Robot Target Localization
Juntong Peng, Hrishikesh Viswanath, Aniket Bera
We introduce a new graph neural operator-based approach for task allocation in a system of heterogeneous robots composed of Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehi…