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Ruyang Li

4 accepted papers

2026

EE-RL: Vision Language Guided Reinforcement Learning with Explorer and Expert model for End-to-End Autonomous Driving

CVPR 2026

End-to-end driving frameworks, which directly map raw sensor data to vehicle control commands, have shown remarkable potential. However, their performance often deteriorates in sparse-critical scenarios, where rare but safety-sensitive events occur. To address this problem, we propose Explorer-Exper

Cited by 0SourcecodeScholar
2025

Improving Height Prediction for Vision-Based Roadside 3D Object Detection

ICASSP 2025accepted

Roadside vision-based 3D object detection is vital in many applications, such as autonomous driving. The mainstream methods enhance the accuracy of distance estimation by converting predicted height distribution into depth distribution. However, predicting object’s height in roadside perception is c…

Cited by 0SourceScholar
2025

MMEditor: Multimodal Prompt-Driven 3D Gaussian Splatting Editing

ICASSP 2025accepted

We propose a multimodal 3D scene editing framework MMEditor to create or modify objects within an extant 3D Gaussian Splatting (3DGS) according to text and image prompts. MMEditor employs a multimodal image editing module to iteratively optimize 3D Gaussians in editing regions for delicate and multi…

Cited by 0SourceScholar
2023

Enhancing Network by Reinforcement Learning and Neural Confined Local Search

IJCAI 2023poster

It has been found that many real networks, such as power grids and the Internet, are non-robust, i.e., attacking a small set of nodes would cause the paralysis of the entire network. Thus, the Network Enhancement Problem~(NEP), i.e., improving the robustness of a given network by modifying its struc…

Cited by 2SourcePDFScholar