Real-Time, Secure, and Computationally Efficient Navigation: A Prediction-Planning Framework Using Occupancy Grid Map-Reference-Cited by-同舟云学术

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Real-Time, Secure, and Computationally Efficient Navigation: A Prediction-Planning Framework Using Occupancy Grid Map

Published:2023-09-24 Issue: Volume: Page:4352-4359
ISSN:
Container-title:2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)
language:
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Author:

Zhang Xiaotong¹,Fu Jiawei¹,Chen Shitao¹,Zheng Nanning¹

Affiliation:

1. Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University,National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications,Xi'an,Shaanxi,P.R. China,710049

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10420842/10420843/10422627.pdf?arnumber=10422627

Reference22 articles.

1. Environment Perception Framework Fusing Multi-Object Tracking, Dynamic Occupancy Grid Maps and Digital Maps

2. Morphing and Sampling Network for Dense Point Cloud Completion

3. Self-supervised point cloud prediction using 3d spatio-temporal convolutional net-works;Mersch,2022

4. Predicting the Perceptual Quality of Point Cloud: A 3D-to-2D Projection-Based Exploration

5. Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling

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