9 papers
LARAD: Layout-Aware Road Anomaly Detection via Spatial-Logic Reasoning
Shiyi Mu, Xujie Chen, Shugong Xu
Accurate open-world obstacle detection is critical for autonomous driving. Current anomaly segmentation methods suffer from a fundamental blind spot: they over-rely on texture nove…
DDStereo: Efficient Dual Decoder Transformers for Stereo 3D Road Anomaly Detection
Shiyi Mu, Zichong Gu, Zhiqi Ai +2
Stereo-based 3D obstacle perception for autonomous driving is currently constrained by an imbalanced triplet: deployment cost, detection accuracy, and open-set adaptability. While…
StereoDETR: Stereo-based Transformer for 3D Object Detection
Shiyi Mu, Zichong Gu, Zhiqi Ai +3
Compared to monocular 3D object detection, stereo-based 3D methods offer significantly higher accuracy but still suffer from high computational overhead and latency. The state-of-t…
Visual Bridge: Universal Visual Perception Representations Generating
Yilin Gao, Shuguang Dou, Junzhou Li +4
Recent advances in diffusion models have achieved remarkable success in isolated computer vision tasks such as text-to-image generation, depth estimation, and optical flow. However…
AnchDrive: Bootstrapping Diffusion Policies with Hybrid Trajectory Anchors for End-to-End Driving
Jinhao Chai, Anqing Jiang, Hao Jiang +4
End-to-end multi-modal planning has become a transformative paradigm in autonomous driving, effectively addressing behavioral multi-modality and the generalization challenge in lon…
Knowledge Transfer from Interaction Learning
Yilin Gao, Kangyi Chen, Zhongxing Peng +2
Current visual foundation models (VFMs) face a fundamental limitation in transferring knowledge from vision language models (VLMs), while VLMs excel at modeling cross-modal interac…
SparseMeXT Unlocking the Potential of Sparse Representations for HD Map Construction
Anqing Jiang, Jinhao Chai, Yu Gao +10
Recent advancements in high-definition \emph{HD} map construction have demonstrated the effectiveness of dense representations, which heavily rely on computationally intensive bird…
TLD: A Vehicle Tail Light signal Dataset and Benchmark
Jinhao Chai, Shiyi Mu, Shugong Xu
Understanding other drivers' intentions is crucial for safe driving. The role of taillights in conveying these intentions is underemphasized in current autonomous driving systems.…
A Learnable Color Correction Matrix for RAW Reconstruction
Anqi Liu, Shiyi Mu, Shugong Xu
Autonomous driving algorithms usually employ sRGB images as model input due to their compatibility with the human visual system. However, visually pleasing sRGB images are possibly…