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

Aashik Chandramohan; Mannes Poel; Bernd Meijerink; Geert Heijenk

doi:10.1109/WD.2019.8734192

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

Aashik Chandramohan, Mannes Poel, Bernd Meijerink, Geert Heijenk

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

10 Citations (Scopus)

27 Downloads (Pure)

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 language	English
Title of host publication	2019 Wireless Days, WD 2019
Publisher	IEEE
ISBN (Electronic)	9781728101170
DOIs	https://doi.org/10.1109/WD.2019.8734192
Publication status	Published - 13 Jun 2019
Event	11th Wireless Days Conference, WD 2019 - Manchester Metropolitan University Business School, Manchester, United Kingdom Duration: 24 Apr 2019 → 26 Apr 2019 Conference number: 11 https://wirelessdays2019.net/

Conference

Conference	11th Wireless Days Conference, WD 2019
Abbreviated title	WD 2019
Country/Territory	United Kingdom
City	Manchester
Period	24/04/19 → 26/04/19
Internet address	https://wirelessdays2019.net/

Keywords

Autonomous driving
Cooperative driving
Highway environment
Q learning
Reinforcement Learning
SUMO

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/WD.2019.8734192

Cite this

@inproceedings{5b9884808603474aa54f05945112a70d,

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 = jun,

day = "13",

doi = "10.1109/WD.2019.8734192",

language = "English",

booktitle = "2019 Wireless Days, WD 2019",

publisher = "IEEE",

address = "United States",

note = "11th Wireless Days Conference, WD 2019, WD 2019 ; Conference date: 24-04-2019 Through 26-04-2019",

url = "https://wirelessdays2019.net/",

}

TY - GEN

T1 - Machine Learning for Cooperative Driving in a Multi-Lane Highway Environment

AU - Chandramohan, Aashik

AU - Poel, Mannes

AU - Meijerink, Bernd

AU - Heijenk, Geert

N1 - Conference code: 11

PY - 2019/6/13

Y1 - 2019/6/13

N2 - 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.

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.

KW - Autonomous driving

KW - Cooperative driving

KW - Highway environment

KW - Q learning

KW - Reinforcement Learning

KW - SUMO

UR - http://www.scopus.com/inward/record.url?scp=85068583711&partnerID=8YFLogxK

U2 - 10.1109/WD.2019.8734192

DO - 10.1109/WD.2019.8734192

M3 - Conference contribution

AN - SCOPUS:85068583711

BT - 2019 Wireless Days, WD 2019

PB - IEEE

T2 - 11th Wireless Days Conference, WD 2019

Y2 - 24 April 2019 through 26 April 2019

ER -

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

Abstract

Conference

Keywords

UN SDGs

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