Machine Learning for Cooperative Driving in a Multi-Lane Highway Environment

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Most of the research in automated driving currently involves using the on-board sensors on the vehicle to collect information regarding surrounding vehicles to maneuver around them. In this paper we discuss how information communicated through vehicular networking can be used for controlling an autonomous vehicle in a multi-lane highway environment. A driving algorithm is designed using deep Q learning, a type of reinforcement learning. In order to train and test driving algorithms, we deploy a simulated traffic system, using SUMO (Simulation of Urban Mobility). The performance of the driving algorithm is tested for perfect knowledge regarding surrounding vehicles. Furthermore, the impact of limited communication range and random packet loss is investigated. Currently the performance of the driving algorithm is far from ideal with the collision ratios being quite high. We propose directions for additional research to improve the performance of the algorithm.

Original languageEnglish
Title of host publication2019 Wireless Days, WD 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728101170
DOIs
Publication statusPublished - 13 Jun 2019
Event2019 Wireless Days, WD 2019 - Manchester Metropolitan University Business School, Manchester, United Kingdom
Duration: 24 Apr 201926 Apr 2019
https://wirelessdays2019.net/

Conference

Conference2019 Wireless Days, WD 2019
Abbreviated titleWD 2019
CountryUnited Kingdom
CityManchester
Period24/04/1926/04/19
Internet address

Fingerprint

Learning systems
Reinforcement learning
Packet loss
Communication
Sensors

Keywords

  • Autonomous driving
  • Cooperative driving
  • Highway environment
  • Q learning
  • Reinforcement Learning
  • SUMO

Cite this

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title = "Machine Learning for Cooperative Driving in a Multi-Lane Highway Environment",
abstract = "Most of the research in automated driving currently involves using the on-board sensors on the vehicle to collect information regarding surrounding vehicles to maneuver around them. In this paper we discuss how information communicated through vehicular networking can be used for controlling an autonomous vehicle in a multi-lane highway environment. A driving algorithm is designed using deep Q learning, a type of reinforcement learning. In order to train and test driving algorithms, we deploy a simulated traffic system, using SUMO (Simulation of Urban Mobility). The performance of the driving algorithm is tested for perfect knowledge regarding surrounding vehicles. Furthermore, the impact of limited communication range and random packet loss is investigated. Currently the performance of the driving algorithm is far from ideal with the collision ratios being quite high. We propose directions for additional research to improve the performance of the algorithm.",
keywords = "Autonomous driving, Cooperative driving, Highway environment, Q learning, Reinforcement Learning, SUMO",
author = "Aashik Chandramohan and Mannes Poel and Bernd Meijerink and Geert Heijenk",
year = "2019",
month = "6",
day = "13",
doi = "10.1109/WD.2019.8734192",
language = "English",
booktitle = "2019 Wireless Days, WD 2019",
publisher = "IEEE Computer Society",
address = "United States",

}

Chandramohan, A, Poel, M, Meijerink, B & Heijenk, G 2019, Machine Learning for Cooperative Driving in a Multi-Lane Highway Environment. in 2019 Wireless Days, WD 2019., 8734192, IEEE Computer Society, 2019 Wireless Days, WD 2019, Manchester, United Kingdom, 24/04/19. https://doi.org/10.1109/WD.2019.8734192

Machine Learning for Cooperative Driving in a Multi-Lane Highway Environment. / Chandramohan, Aashik; Poel, Mannes; Meijerink, Bernd; Heijenk, Geert.

2019 Wireless Days, WD 2019. IEEE Computer Society, 2019. 8734192.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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AB - Most of the research in automated driving currently involves using the on-board sensors on the vehicle to collect information regarding surrounding vehicles to maneuver around them. In this paper we discuss how information communicated through vehicular networking can be used for controlling an autonomous vehicle in a multi-lane highway environment. A driving algorithm is designed using deep Q learning, a type of reinforcement learning. In order to train and test driving algorithms, we deploy a simulated traffic system, using SUMO (Simulation of Urban Mobility). The performance of the driving algorithm is tested for perfect knowledge regarding surrounding vehicles. Furthermore, the impact of limited communication range and random packet loss is investigated. Currently the performance of the driving algorithm is far from ideal with the collision ratios being quite high. We propose directions for additional research to improve the performance of the algorithm.

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