no code implementations • 18 Apr 2024 • Xiao Wang, Ke Tang, Xingyuan Dai, Jintao Xu, Quancheng Du, Rui Ai, Yuxiao Wang, Weihao Gu
To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S4TP) framework.
1 code implementation • 27 Mar 2024 • Weidong Xie, Lun Luo, Nanfei Ye, Yi Ren, Shaoyi Du, Minhang Wang, Jintao Xu, Rui Ai, Weihao Gu, Xieyuanli Chen
Experimental results on the KITTI dataset show that our proposed methods achieve state-of-the-art performance while running in real time.
no code implementations • 3 Jan 2024 • Haowen Zheng, Dong Cao, Jintao Xu, Rui Ai, Weihao Gu, Yang Yang, Yanyan Liang
Ultimately, we utilize this reconstruction target to reconstruct the student features.
1 code implementation • 29 Nov 2023 • Junyi Ma, Xieyuanli Chen, Jiawei Huang, Jingyi Xu, Zhen Luo, Jintao Xu, Weihao Gu, Rui Ai, Hesheng Wang
Furthermore, the standardized evaluation protocol for preset multiple tasks is also provided to compare the performance of all the proposed baselines on present and future occupancy estimation with respect to objects of interest in autonomous driving scenarios.
no code implementations • 23 Oct 2023 • Jintao Xu, Yifei Li, Wenxun Xing
Convergence of the proximal point version is proven based on a Kurdyka-Lojasiewicz (KL) property analysis framework, and we can ensure a locally R-linear or sublinear convergence rate depending on the different ranges of the Kurdyka-Lojasiewicz (KL) exponent, in which a necessary auxiliary function is constructed to realize our goal.
no code implementations • 2 Mar 2023 • Shuhang Zheng, Yixuan Li, Zhu Yu, Beinan Yu, Si-Yuan Cao, Minhang Wang, Jintao Xu, Rui Ai, Weihao Gu, Lun Luo, Hui-Liang Shen
The experimental results evaluated on the KITTI dataset show that, with only a small set of training data, I2P-Rec achieves recall rates at Top-1\% over 80\% and 90\%, when localizing monocular and stereo images on point cloud maps, respectively.
no code implementations • CVPR 2023 • Ruihao Wang, Jian Qin, Kaiying Li, Yaochen Li, Dong Cao, Jintao Xu
Experimental results demonstrate that our work outperforms the state-of-the-art approaches in terms of F-Score, being 10. 6% higher on the OpenLane dataset and 4. 0% higher on the Apollo 3D synthetic dataset, with a speed of 185 FPS.
1 code implementation • 28 Nov 2022 • Hao Dong, Xianjing Zhang, Jintao Xu, Rui Ai, Weihao Gu, Huimin Lu, Juho Kannala, Xieyuanli Chen
However, current works are based on raw data or network feature-level fusion and only consider short-range HD map generation, limiting their deployment to realistic autonomous driving applications.
no code implementations • 12 Oct 2022 • Ruihao Wang, Jian Qin, Kaiying Li, Yaochen Li, Dong Cao, Jintao Xu
Experimental results demonstrate that our work outperforms the state-of-the-art approaches in terms of F-Score, being 10. 6% higher on the OpenLane dataset and 5. 9% higher on the Apollo 3D synthetic dataset, with a speed of 185 FPS.
Ranked #1 on 3D Lane Detection on Apollo Synthetic 3D Lane
no code implementations • 30 Aug 2022 • Jintao Xu, Chenglong Bao, Wenxun Xing
Training deep neural networks (DNNs) is an important and challenging optimization problem in machine learning due to its non-convexity and non-separable structure.
1 code implementation • 5 Jul 2022 • Jiadai Sun, Yuchao Dai, Xianjing Zhang, Jintao Xu, Rui Ai, Weihao Gu, Xieyuanli Chen
We also use a point refinement module via 3D sparse convolution to fuse the information from both LiDAR range image and point cloud representations and reduce the artifacts on the borders of the objects.