Code underlying the publication: Comprehensive Training and Evaluation on Deep Reinforcement Learning for Automated Driving in Various Simulated Driving Maneuvers

DOI:10.4121/26e8f131-53f8-44b9-8ecf-249bfedb0154.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/26e8f131-53f8-44b9-8ecf-249bfedb0154
Datacite citation style:
Dong, Yongqi; Datema, Tobias; Wassenaar, Vincent; Van de Weg, Joris; Tolga Kopar, Cahit et. al. (2025): Code underlying the publication: Comprehensive Training and Evaluation on Deep Reinforcement Learning for Automated Driving in Various Simulated Driving Maneuvers. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/26e8f131-53f8-44b9-8ecf-249bfedb0154.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

This is the code and data related to the publication:

Y. Dong, T. Datema, V. Wassenaar, J. Van de Weg, C. T. Kopar and H. Suleman, "Comprehensive Training and Evaluation on Deep Reinforcement Learning for Automated Driving in Various Simulated Driving Maneuvers," 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain, 2023, pp. 6165-6170, doi: 10.1109/ITSC57777.2023.10422159.

 

 keywords: {Training;Deep learning;Roads;Reinforcement learning;Automobiles;Task analysis;Optimization}


The implementation is based on Python, Stable-Baselines3 (https://stable-baselines3.readthedocs.io/en/master/) and Highway_env simulation environment https://github.com/Farama-Foundation/HighwayEnv


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Developing and testing automated driving models in the real world might be challenging and even dangerous, while simulation can help with this, especially for challenging manoeuvres. Deep reinforcement learning (DRL) has the potential to tackle complex decision-making and controlling tasks through learning and interacting with the environment, thus it is suitable for developing automated driving while not being explored in detail yet. This study carried out a comprehensive study by implementing, evaluating, and comparing the two DRL algorithms, Deep Q-networks (DQN) and Trust Region Policy Optimization (TRPO), for training automated driving on the highway-env simulation platform. Effective and customized reward functions were developed and the implemented algorithms were evaluated in terms of onlane accuracy (how well the car drives on the road within the lane), efficiency (how fast the car drives), safety (how likely the car is to crash into obstacles), and comfort (how much the car makes jerks, e.g., suddenly accelerates or brakes). Results show that the TRPO-based models with modified reward functions delivered the best performance in most cases. Furthermore, to train a uniform driving model that can tackle various driving manoeuvres besides the specific ones, this study expanded the highway-env and developed an extra customized training environment, namely, ComplexRoads, integrating various driving manoeuvres and multiple road scenarios together. Models trained on the designed ComplexRoads environment can adapt well to other driving manoeuvres with promising overall performance. Lastly, several functionalities were added to the highway-env to implement this work. The codes are open on GitHub at https://github.com/alaineman/drlcarsim-paper.


History

  • 2025-02-20 first online, published, posted

Publisher

4TU.ResearchData

Format

py; txt; csv; avi;

Funding

  • Safe and efficient operation of AutoMated and human drivEN vehicles in mixed traffic (grant code 17187) [more info...] Applied and Technical Sciences (TTW), a subdomain of the Dutch Institute for Scientific Research (NWO)

Organizations

TU Delft, Faculty of Civil Engineering and Geosciences, Department of Transport and Planning
TU Delft, Faculty of Electrical Engineering, Mathematics & Computer Science

DATA

Files (2)