LongiControl: A Reinforcement Learning Environment for Longitudinal Vehicle Control

被引：1

作者：

Dohmen, Jan ^{[1
]}

Liessner, Roman ^{[1
]}

Friebel, Christoph ^{[1
]}

Baeker, Bernard ^{[1
]}

机构：

[1] Tech Univ Dresden, Dresden Inst Automobile Engn, George Bahr Str 1c, D-01069 Dresden, Germany

来源：

ICAART: PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 2 | 2021年

关键词：

Reinforcement Learning; Artificial Intelligence; Deep Learning; Machine Learning; Autonomous Driving; Longitudinal Control; OpenAI Gym;

D O I：

10.5220/0010305210301037

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Reinforcement Learning (RL) might be very promising for solving a variety of challenges in the field of autonomous driving due to its ability to find long-term oriented solutions in complex decision scenarios. For training and validation of a RL algorithm, a simulative environment is advantageous due to risk reduction and saving of resources. This contribution presents an RL environment designed for the optimization of longitudinal control. The focus is on providing an illustrative and comprehensible example for a continuous real-world problem. The environment will be published following the OpenAI Gym interface, allowing for easy testing and comparing of novel RL algorithms. In addition to details on implementation reference is also made to areas where research is required.

引用

页码：1030 / 1037

页数：8

共 50 条

[1] Reinforcement Learning of Adaptive Longitudinal Vehicle Control for Dynamic Collaborative Driving
Ng, Luke
Clark, Christopher M.
Huissoon, Jan P.
[J]. 2008 IEEE INTELLIGENT VEHICLES SYMPOSIUM, VOLS 1-3, 2008, : 583 - +
[2] Explainable Reinforcement Learning for Longitudinal Control
Liessner, Roman
Dohmen, Jan
Wiering, Marco
[J]. ICAART: PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 2, 2021, : 874 - 881
[3] Actor-Critic Reinforcement Learning for Linear Longitudinal Output Control of a Road Vehicle
Puccetti, Luca
Rathgeber, Christian
Hohmann, Soeren
[J]. 2019 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC), 2019, : 2907 - 2913
[4] Connected and automated vehicle control at unsignalized intersection based on deep reinforcement learning in vehicle-to-infrastructure environment
Chen, Juan
Sugumaran, Vijayan
Qu, Peiyan
[J]. INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, 2022, 18 (07)
[5] Lateral Control of a Vehicle using Reinforcement Learning
Brasch, Moritz
Heinz, Ipek Sarac
Bayer, Alexander
[J]. 2022 8TH INTERNATIONAL CONFERENCE ON CONTROL, DECISION AND INFORMATION TECHNOLOGIES (CODIT'22), 2022, : 451 - 456
[6] Connected automated vehicle cooperative control with a deep reinforcement learning approach in a mixed traffic environment
Shi, Haotian
Zhou, Yang
Wu, Keshu
Wang, Xin
Lin, Yangxin
Ran, Bin
[J]. Transportation Research Part C: Emerging Technologies, 2021, 133
[7] Connected automated vehicle cooperative control with a deep reinforcement learning approach in a mixed traffic environment
Shi, Haotian
Zhou, Yang
Wu, Keshu
Wang, Xin
Lin, Yangxin
Ran, Bin
[J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2021, 133
[8] A Reinforcement Learning Method for Complicated Control Environment
赵珑
[J]. High Technology Letters, 1996, (02) : 64 - 67
[9] Deep reinforcement learning control of electric vehicle charging in the of
Dorokhova, Marina
Martinson, Yann
Ballif, Christophe
Wyrsch, Nicolas
[J]. APPLIED ENERGY, 2021, 301
[10] Neural reinforcement learning for the control of an autonomous mobile vehicle
Cicirelli, G
D'Orazio, T
Ancona, N
Distante, A
[J]. IASTED: PROCEEDINGS OF THE IASTED INTERNATIONAL CONFERENCE ON ROBOTICS AND APPLICATIONS, 2003, : 18 - 23

← 1 2 3 4 5 →