arXiv Computer Vision
5d ago

Refine Connections, Close the Gap: A Reliable Enhancement Framework for Driving Scene Topology

The paper introduces TopoEnhance, a framework that improves the reliability of scene topology graphs for autonomous driving by treating topology enhancement as a denoising reconstruction task. It learns to recover structural consistency from corrupted ground‑truth graphs, thereby correcting logical inconsistencies and unreliable connections. Experiments show consistent gains in both continuous topology metrics (TOP score) and discrete connectivity (Topology Jaccard Similarity) across multiple baselines without retraining.

By Xiaoqi Wang, Dingyi Zhaung, David Paz, Wenbin He, Yucai Bai, Peng Zhou, Rui Zhang, Jinhua Zhao, Liu Ren
arXiv AI
Jun 16

OmniTraffic: A Controllable Generation Pipeline and Benchmark for Spatio-Temporal Traffic Reasoning

arXiv:2606. 15749v1 Announce Type: cross Abstract: Traffic scene understanding requires models to reason beyond object recognition, including lane topology, multi-view geometry, temporal evolution, and signal-phase semantics.

By Maonan Wang, Zhengyan Huang, Kemou Jiang, Yuhang Fu, Jiayue Zhu, Yuxin Cai, Xingchen Zou, Qiaosheng Zhang, Yi Yu, Ding Wang, Xi Chen, Ben M. Chen, Yuxuan Liang, Zhiyong Cui, Man On Pun, Yirong Chen
Hugging Face Trending Papers
Jun 25

LAMP: Lane-Aligned Motion Primitives for Feasible Trajectory Prediction

Motion forecasting is essential for autonomous driving systems to enable safe decision-making and planning in complex driving scenarios. While existing predictors excel at minimizing standard displacement errors, they often overlook the adherence to lane topology of multimodal predictions, particularly for lower-probability modes.