Learning to Drive (Efficiently) : A Feasibility Study for the Application of Reinforce Learning for Automated Powertrain Control Software Engineering-Reference-Cited by-同舟云学术

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Learning to Drive (Efficiently) : A Feasibility Study for the Application of Reinforce Learning for Automated Powertrain Control Software Engineering

Published:2024-05-30 Issue: Volume:22 Page:111-116
ISSN:
Container-title:2024 IEEE International Conference on Electro Information Technology (eIT)
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Author:

Dominka Sven¹,Doppler Jörg¹,Smith Henrik¹,Litschauer Teresa¹,Laflamme Catherine²

Affiliation:

1. Bosch Engineering GmbH Robert Bosch AG,Vienna,Austria

2. Fraunhofer Austria Research GmbH,Wattens,Austria

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10609838/10609857/10609861.pdf?arnumber=10609861

Reference25 articles.

1. Automotive Feature Coordination based on a Machine-Learning Approach

2. Taming and optimizing feature interaction in software-intensive automotive systems

3. An Edge Computing Framework for Powertrain Control System Optimization of Intelligent and Connected Vehicles Based on Curiosity-Driven Deep Reinforcement Learning

4. A Decision-Making Method for Connected Autonomous Driving Based on Reinforcement Learning

5. A Real-Time Intelligent Speed Optimization Planner Using Reinforcement Learning

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