Clustering Traffic Scenarios Using Mental Models as Little as Possible-Reference-Cited by-同舟云学术

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Clustering Traffic Scenarios Using Mental Models as Little as Possible

Published:2020-10-19 Issue: Volume: Page:
ISSN:
Container-title:2020 IEEE Intelligent Vehicles Symposium (IV)
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Author:

Hauer Florian,Gerostathopoulos Ilias,Schmidt Tabea,Pretschner Alexander

Funder

Intel

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9304518/9304528/09304636.pdf?arnumber=9304636

Cited by 21 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Traffic Scene Similarity: a Graph-based Contrastive Learning Approach;2023 IEEE Symposium Series on Computational Intelligence (SSCI);2023-12-05

2. scenoRITA: Generating Diverse, Fully Mutable, Test Scenarios for Autonomous Vehicle Planning;IEEE Transactions on Software Engineering;2023-10-01

3. On the Comparability and Optimal Aggressiveness of the Adversarial Scenario-Based Safety Testing of Robots;IEEE Transactions on Robotics;2023-08

4. How to Rationalise the Sampling of Test-Scenarios in Automated Driving Based on Criticality Metrics?;2023 8th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS);2023-06-14

5. ZEBRA: Z-order Curve-based Event Retrieval Approach to Efficiently Explore Automotive Data;2023 IEEE Intelligent Vehicles Symposium (IV);2023-06-04

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