Urban Autonomous Driving of Emergency Vehicles with Reinforcement Learning

Zherui Tong; Angel Ayala; Eduardo Benitez Sandoval; Francisco Cruz

doi:10.1109/LA-CCI58595.2023.10409469

Urban Autonomous Driving of Emergency Vehicles with Reinforcement Learning

Zherui Tong
, Angel Ayala
, Eduardo Benitez Sandoval
, Francisco Cruz

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

Abstract

The development of autonomous driving technologies has primarily focused on passenger vehicles, leaving behind the potential benefits for emergency vehicles like fire trucks and ambulances. This paper investigates reinforcement learning techniques’ application in driving emergency vehicles in complex urban scenarios. We demonstrate our approach’s adaptability and efficacy in various driving challenges, vehicle types, and changing surroundings by employing advanced learning algorithms, such as Soft Actor-Critic, in the CARLA simulator. Our preliminary research highlights the possibilities of using reinforcement learning methods to improve self-driving features in emergency vehicles, emphasizing the importance of further research to tackle the unique problems of these emergency vehicles.

Original language	English
Title of host publication	2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350348071
DOIs	https://doi.org/10.1109/LA-CCI58595.2023.10409469
State	Published - 2023
Event	2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023 - Recife-Pe, Brazil Duration: 29 Oct 2023 → 1 Nov 2023

Publication series

Name	2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023

Conference

Conference	2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023
Country/Territory	Brazil
City	Recife-Pe
Period	29/10/23 → 1/11/23

UN SDGs

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

SDG 11 Sustainable Cities and Communities

Keywords

autonomous driving
CARLA simulator
emergency vehicle
reinforcement learning

Access to Document

10.1109/LA-CCI58595.2023.10409469

Cite this

Tong, Z., Ayala, A., Sandoval, E. B., & Cruz, F. (2023). Urban Autonomous Driving of Emergency Vehicles with Reinforcement Learning. In 2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023 (2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/LA-CCI58595.2023.10409469

@inproceedings{b9db780de7384e50902fc9795b529766,

title = "Urban Autonomous Driving of Emergency Vehicles with Reinforcement Learning",

abstract = "The development of autonomous driving technologies has primarily focused on passenger vehicles, leaving behind the potential benefits for emergency vehicles like fire trucks and ambulances. This paper investigates reinforcement learning techniques{\textquoteright} application in driving emergency vehicles in complex urban scenarios. We demonstrate our approach{\textquoteright}s adaptability and efficacy in various driving challenges, vehicle types, and changing surroundings by employing advanced learning algorithms, such as Soft Actor-Critic, in the CARLA simulator. Our preliminary research highlights the possibilities of using reinforcement learning methods to improve self-driving features in emergency vehicles, emphasizing the importance of further research to tackle the unique problems of these emergency vehicles.",

keywords = "autonomous driving, CARLA simulator, emergency vehicle, reinforcement learning",

author = "Zherui Tong and Angel Ayala and Sandoval, \{Eduardo Benitez\} and Francisco Cruz",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023 ; Conference date: 29-10-2023 Through 01-11-2023",

year = "2023",

doi = "10.1109/LA-CCI58595.2023.10409469",

language = "English",

series = "2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023",

}

Tong, Z, Ayala, A, Sandoval, EB & Cruz, F 2023, Urban Autonomous Driving of Emergency Vehicles with Reinforcement Learning. in 2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023. 2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023, Recife-Pe, Brazil, 29/10/23. https://doi.org/10.1109/LA-CCI58595.2023.10409469

Urban Autonomous Driving of Emergency Vehicles with Reinforcement Learning. / Tong, Zherui; Ayala, Angel; Sandoval, Eduardo Benitez et al.
2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023).

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

TY - GEN

T1 - Urban Autonomous Driving of Emergency Vehicles with Reinforcement Learning

AU - Tong, Zherui

AU - Ayala, Angel

AU - Sandoval, Eduardo Benitez

AU - Cruz, Francisco

PY - 2023

Y1 - 2023

N2 - The development of autonomous driving technologies has primarily focused on passenger vehicles, leaving behind the potential benefits for emergency vehicles like fire trucks and ambulances. This paper investigates reinforcement learning techniques’ application in driving emergency vehicles in complex urban scenarios. We demonstrate our approach’s adaptability and efficacy in various driving challenges, vehicle types, and changing surroundings by employing advanced learning algorithms, such as Soft Actor-Critic, in the CARLA simulator. Our preliminary research highlights the possibilities of using reinforcement learning methods to improve self-driving features in emergency vehicles, emphasizing the importance of further research to tackle the unique problems of these emergency vehicles.

AB - The development of autonomous driving technologies has primarily focused on passenger vehicles, leaving behind the potential benefits for emergency vehicles like fire trucks and ambulances. This paper investigates reinforcement learning techniques’ application in driving emergency vehicles in complex urban scenarios. We demonstrate our approach’s adaptability and efficacy in various driving challenges, vehicle types, and changing surroundings by employing advanced learning algorithms, such as Soft Actor-Critic, in the CARLA simulator. Our preliminary research highlights the possibilities of using reinforcement learning methods to improve self-driving features in emergency vehicles, emphasizing the importance of further research to tackle the unique problems of these emergency vehicles.

KW - autonomous driving

KW - CARLA simulator

KW - emergency vehicle

KW - reinforcement learning

UR - https://www.scopus.com/pages/publications/85185226601

U2 - 10.1109/LA-CCI58595.2023.10409469

DO - 10.1109/LA-CCI58595.2023.10409469

M3 - Conference contribution

AN - SCOPUS:85185226601

T3 - 2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023

BT - 2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023

Y2 - 29 October 2023 through 1 November 2023

ER -

Tong Z, Ayala A, Sandoval EB, Cruz F. Urban Autonomous Driving of Emergency Vehicles with Reinforcement Learning. In 2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2023). doi: 10.1109/LA-CCI58595.2023.10409469

Urban Autonomous Driving of Emergency Vehicles with Reinforcement Learning

Abstract

Publication series

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UN SDGs

Keywords

Access to Document

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