Search Results for author:
Found 11 papers, 4 papers with code
S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles
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.
ModaLink: Unifying Modalities for Efficient Image-to-PointCloud Place Recognition
Experimental results on the KITTI dataset show that our proposed methods achieve state-of-the-art performance while running in real time.
Distilling Temporal Knowledge with Masked Feature Reconstruction for 3D Object Detection
Ultimately, we utilize this reconstruction target to reconstruct the student features.
Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in Autonomous Driving Applications
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.
ADMM Training Algorithms for Residual Networks: Convergence, Complexity and Parallel Training
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.
I2P-Rec: Recognizing Images on Large-scale Point Cloud Maps through Bird's Eye View Projections
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.
BEV-LaneDet: An Efficient 3D Lane Detection Based on Virtual Camera via Key-Points
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.
SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Generation
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.
BEV-LaneDet: a Simple and Effective 3D Lane Detection Baseline
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
Convergence Rates of Training Deep Neural Networks via Alternating Minimization Methods
Training deep neural networks (DNNs) is an important and challenging optimization problem in machine learning due to its non-convexity and non-separable structure.
Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation
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.
