Search Results for author:
Found 7 papers, 3 papers with code
CLRKDNet: Speeding up Lane Detection with Knowledge Distillation
Road lanes are integral components of the visual perception systems in intelligent vehicles, playing a pivotal role in safe navigation.
Dragtraffic: A Non-Expert Interactive and Point-Based Controllable Traffic Scene Generation Framework
However, most existing scene generation methods lack controllability, accuracy, and versatility, resulting in unsatisfactory generation results.
Monocular 3D lane detection for Autonomous Driving: Recent Achievements, Challenges, and Outlooks
This review looks back and analyzes the current state of achievements in the field of 3D lane detection research.
CurbNet: Curb Detection Framework Based on LiDAR Point Cloud Segmentation
Curb detection is a crucial function in intelligent driving, essential for determining drivable areas on the road.
Every Dataset Counts: Scaling up Monocular 3D Object Detection with Joint Datasets Training
Monocular 3D object detection plays a crucial role in autonomous driving.
The Role of the Hercules Autonomous Vehicle During the COVID-19 Pandemic: An Autonomous Logistic Vehicle for Contactless Goods Transportation
Moreover, many countries have imposed tough lockdown measures to reduce the virus transmission (e. g., retail, catering) during the pandemic, which causes inconveniences for human daily life.
Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding
In this paper, we propose a novel road crack detection algorithm based on deep learning and adaptive image segmentation.
