Code and data underlying the publication: Social-aware Planning and Control for Automated Vehicles Based on Driving Risk Field and Model Predictive Contouring Control: Driving through Roundabouts as a Case Study

DOI:10.4121/70e29cf5-8502-4e8d-bf32-2953431a83ff.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: 10.4121/70e29cf5-8502-4e8d-bf32-2953431a83ff
Datacite citation style:
Dong, Yongqi; Zhang, Li; Haneen Farah; van Arem, Bart (2025): Code and data underlying the publication: Social-aware Planning and Control for Automated Vehicles Based on Driving Risk Field and Model Predictive Contouring Control: Driving through Roundabouts as a Case Study. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/70e29cf5-8502-4e8d-bf32-2953431a83ff.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

This is the code and data related to the publication:

L. Zhang, Y. Dong, H. Farah and B. van Arem, "Social-Aware Planning and Control for Automated Vehicles Based on Driving Risk Field and Model Predictive Contouring Control: Driving Through Roundabouts as a Case Study," 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Honolulu, Oahu, HI, USA, 2023, pp. 3297-3304, doi: 10.1109/SMC53992.2023.10394462.


https://doi.org/10.1109/SMC53992.2023.10394462


keywords: {Trajectory planning;Predictive models;Prediction algorithms;Robustness;Planning;Predictive control;Vehicles;Automated vehicles;Planning and control;Social-aware driving;Roundabouts;Driving Risk Field;Model Predictive Contouring Control},


The implementation is based on Python and Highway_env simulation environment https://github.com/Farama-Foundation/HighwayEnv

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The gradual deployment of automated vehicles (AVs) results in mixed traffic where AVs will interact with human-driven vehicles (HDVs). Thus, social-aware motion planning and control while considering interactions with HDVs on the road is critical for AVs’ deployment and safe driving under various manoeuvres. Previous research mostly focuses on the trajectory planning of AVs using Model Predictive Control or other relevant methods, while seldom considering the integrated planning and control of AVs altogether to simplify the whole pipeline architecture. Furthermore, there are very limited studies on social-aware driving that makes AVs understandable and expected by human drivers, and none when it comes to the challenging manoeuvre of driving through roundabouts. To fill these research gaps, this paper develops an integrated social-aware planning and control algorithm for AVs’ driving through roundabouts based on Driving Risk Field (DRF), Social Value Orientation (SVO), and Model Predictive Contouring Control (MPCC), i.e., DRF-SVO-MPCC. The proposed method is tested and verified with simulation on the open-sourced highway-env platform. Compared with the baseline method using purely Nonlinear Model Predictive Control, the DRF-SVO-MPCC can achieve better performance under various maneuvers of driving through roundabouts with and without surrounding HDVs.



History

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

Publisher

4TU.ResearchData

Format

py; txt; csv; md

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

DATA

Files (3)