Data and code underlying the publication: A new computational perceived risk model for automated vehicles based on potential collision avoidance difficulty (PCAD)
Datacite citation style:
He, Xiaolin; R. (Riender) Happee; Wang, Meng (2024): Data and code underlying the publication: A new computational perceived risk model for automated vehicles based on potential collision avoidance difficulty (PCAD). Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/3ad2db22-ea82-4436-8df5-ebbbdb4aeec6.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
choose version:
version 2 - 2024-07-31 (latest)
version 1 - 2024-07-17
This package contains the MATLAB implementation code of four computational perceived risk models, the two datasets Dataset Merging and Dataset Obstacle Avoidance, a README file for the code and data, and a video representing the dynamics of the proposed model PCAD in a traffic event.
history
- 2024-07-17 first online, published, posted
publisher
4TU.ResearchData
format
.m/.mat/mp4
associated peer-reviewed publication
A new computational perceived risk model for automated vehicles based on potential collision avoidance difficulty (PCAD)
funding
- Horizon 2020 - SHAPE-IT (grant code 860410) European Union’s Horizon 2020
- Investigating Trust in Automation Toyota Motor Europe NV/SA
organizations
TU Delft, Faculty of Mechanical Engineering, Department of Cognitive Robotics; Technische Universität Dresden, "Friedrich List" Faculty of Transport and Traffic Sciences, Chair of Traffic Process Automation
DATA
files (1)
- 36,336,163 bytesMD5:
301cc4e1752a50d7c67709d52a85b0a2
PCAD.zip -
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