Search-based task and motion planning for hybrid systems: Agile autonomous vehicles-Reference-Cited by-同舟云学术

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Search-based task and motion planning for hybrid systems: Agile autonomous vehicles

Published:2023-05 Issue: Volume:121 Page:105893
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Ajanović Zlatan^ORCID,Regolin Enrico,Shyrokau Barys^ORCID,Ćatić Hana^ORCID,Horn Martin,Ferrara Antonella

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference49 articles.

1. On highly-skilled autonomous competition vehicles: An FSM for autonomous rallycross;Acosta,2019

2. A multi-heuristic search-based motion planning for autonomous parking;Adabala,2020

3. A novel approach to model exploration for value function learning;Ajanović,2019

4. Validating SuperHuman automated driving performance;Ajanovic,2020

5. Search-based optimal motion planning for automated driving;Ajanovic,2018

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1. A computationally efficient and robust looming perception model based on dynamic neural field;Neural Networks;2024-11

2. Incremental Learning-Based Real-Time Trajectory Prediction for Autonomous Driving via Sparse Gaussian Process Regression;2024 IEEE Intelligent Vehicles Symposium (IV);2024-06-02

3. Asymptotically Optimal A* for Kinodynamic Planning;IEEE Robotics and Automation Letters;2024-05

4. Advances in Vehicle Dynamics and Road Safety: Technologies, Simulations, and Applications;Applied Sciences;2024-04-27

5. CL-FDAPF trajectory planner and FO-LADRC motion controller for autonomous sweeper vehicle;Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering;2024-04-15

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