activity
20232026
most citedDuMapNet: An End-to-End Vectorization System for City-Scale Lane-Level Map Generation

6 citations · 18 across the 37 of their papers we have counts for

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Showing 2026Show all

11 papers · 1 filter

cs.CV2026

Leveraging Visual and Geometric Priors for Metric-scale and Complete Vehicle Gaussian Reconstruction from Limited Views

Jinyu Miao, Jiusi Li, Yifei He +4

High-fidelity vehicle assets are essential for controllable traffic scene generation, particularly for synthesizing rare and safety-critical long-tail scenarios. However, reconstru…

cs.LG2026

daVinci-kernel: Co-Evolving Skill Selection, Summarization, and Utilization via RL for GPU Kernel Optimization

Dayuan Fu, Mohan Jiang, Tongyu Wang +5

GPU kernel optimization represents a paradigm where functional correctness is assumed and execution efficiency is the objective. We present daVinci-kernel, a reinforcement learning…

cs.RO2026

nuTruck: Benchmarking Autonomous Driving Planning for Distributed Electric-drive Trucks

Jinyu Miao, Pu Zhang, Yifei He +5

The dominance of traditional rule-based methods in autonomous driving has gradually been replaced by learning-based approaches. While learning-based planners have achieved consider…

cs.LG2026

Dual-Flow Reinforcement Learning with State-Aware Exploration

Qijun Li, Zheng Fu, Qi Song +4

In complex continuous-control reinforcement learning tasks, multimodal optimal actions often coincide with uncertain, multimodal return distributions, making reliable value estimat…

cs.AI2026

MapAgent: An Industrial-Grade Agentic Framework for City-scale Lane-level Map Generation

Deguo Xia, Zihan Li, Haochen Zhao +6

Lane-level maps are critical infrastructure for autonomous driving and lane-level navigation, yet constructing and maintaining standardized lane networks for hundreds of cities rem…

cs.CV2026

Envision4D: Envisioning Visual Futures via Feed-forward 4D Gaussian Splatting for Autonomous Driving

Qi Song, Yifei He, Chi Zhang +6

Forecasting the future evolution of dynamic scenes is crucial in autonomous driving. However, existing feed-forward paradigms are primarily designed for interpolation. When extende…

cs.RO2026

4DLidarOpen: An Open 4D FMCW Lidar Dataset for Motion-Aware Autonomous Driving

Kane Qian, Xin Zhao, Yining Shi +7

We present 4DLidarOpen, a large-scale open multi-modal dataset for autonomous driving, centered on 4D frequency-modulated continuous-wave (FMCW) Lidar sensing. Unlike conventional…

cs.CV2026

CAVE: A Structured Credit Assignment Approach for Fragmented Visual Evidence Reasoning

Tengda Guo, Jie Leng, Hanlei Li +6

Vision-Language Models (VLMs) have achieved strong performance on general multimodal reasoning, yet remain challenged in integrating nonlocal visual information to support semantic…

cs.CV2026

XEmbodied: A Foundation Model with Enhanced Geometric and Physical Cues for Large-Scale Embodied Environments

Kangan Qian, ChuChu Xie, Yang Zhong +13

Vision-Language-Action (VLA) models drive next-generation autonomous systems, but training them requires scalable, high-quality annotations from complex environments. Current cloud…

cs.RO2026

Physics-informed Deep Mixture-of-Koopmans Vehicle Dynamics Model with Dual-branch Encoder for Distributed Electric-drive Trucks

Jinyu Miao, Pu Zhang, Rujun Yan +8

Advanced autonomous driving systems require accurate vehicle dynamics modeling. However, identifying a precise dynamics model remains challenging due to strong nonlinearities and t…

cs.RO2026

LSRE: Latent Semantic Rule Encoding for Real-Time Semantic Risk Detection in Autonomous Driving

Qian Cheng, Weitao Zhou, Cheng Jing +5

Real-world autonomous driving must adhere to complex human social rules that extend beyond legally codified traffic regulations. Many of these semantic constraints, such as yieldin…