5 papers
LiteViLNet: Lightweight Vision-LiDAR Fusion Network for Efficient Road Segmentation
Daojie Peng, Bingtao Wang, Fulong Ma +2
Road segmentation is a fundamental perception task for autonomous driving and intelligent robotic systems, requiring both high accuracy and real-time inference, especially for depl…
Closed Loop Dynamic Driving Data Mixture for Real-Synthetic Co-Training
Hongzhi Ruan, Pei Liu, Weiliang Ma +5
Data scaling is fundamental to modern deep learning, and grows increasingly critical as autonomous driving shifts to end-to-end learning. Real-world driving data is expensive to an…
Multi-Scale Generative Modeling with Heat Dissipation Flow Matching
Jun Ma, Hanquan Zhang, Yanjun Qin +2
Diffusion models are widely used in image generation, with most relying on noise-based corruption and denoising. A distinct branch instead uses blur as the main corruption, preserv…
VGGT-Occ: Geometry-Grounded and Density-Aware Gated Fusion for 3D Occupancy Prediction
Xun Chen, Tianchen Deng, Rui Wang +5
3D semantic occupancy prediction requires accurate 2D-to-3D feature lifting, yet current methods restrict camera geometry to initial projections. Subsequent operations like offset…
DECAMP: Towards Scene-Consistent Multi-Agent Motion Prediction with Disentangled Context-Aware Pre-Training
Jianxin Shi, Zengqi Peng, Xiaolong Chen +2
Trajectory prediction is a critical component of autonomous driving, essential for ensuring both safety and efficiency on the road. However, traditional approaches often struggle w…