6 accepted papers
Modular design of planning-oriented autonomous driving has markedly advanced end-to-end systems. However, existing architectures remain constrained by an over-reliance on ego status, hindering generalization and robust scene understanding. We identify the root cause as an inherent design within thes
Imitation learning is increasingly utilized to improve driving performance using real-world data, yet ensuring the safety of its outputs remains a fundamental challenge. While differentiable optimization-based methods are widely employed to enhance safety of imitation planner, their joint training o
"In the field of autonomous driving, online High-definition (HD) map construction is crucial for planning tasks. Recent studies have developed several high-performance HD map construction models to meet the demand. However, the point sequences generated by recent HD map construction models are jitte…
3D object detection is one of the most important tasks in autonomous driving and robotics. Our research focuses on tackling low efficiency issue of point-based methods, and we propose a novel Instance-Centroid Faster Point Sampling (IC-FPS) framework. We design a Neighboring Feature Diffusion Module
In the point cloud analysis task, the existing local feature aggregation descriptors (LFAD) do not fully utilize the neighborhood information of center points. Previous methods only use the distance information to constrain the local aggregation process, which is easy to be affected by abnormal poin…
Siamese neural network has been well investigated by tracking frameworks due to its fast speed and high accuracy. However, very few efforts were spent on background-extraction by those approaches. In this paper, a Pixel to Global Matching Network (PG-Net) is proposed to suppress the influence of bac…