The doi 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: 10.4121/3ad2db22-ea82-4436-8df5-ebbbdb4aeec6

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 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/3ad2db22-ea82-4436-8df5-ebbbdb4aeec6.v2

Dataset

x choose version: version 1 - 2024-07-17

• Civil Engineering
• Other Engineering
• Cognitive Sciences
• Psychology
• Interdisciplinary Engineering
• Ground Transport
• Automotive Engineering
• Other Technology
• Engineering
• Transport
• Psychology and Cognitive Sciences
• Technology

Collision avoidance, Computational models, Looming, Perceived risk, Task difficulty

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Xiaolin He , R. (Riender) Happee , Meng Wang

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.

• 2024-07-17 first online

• 2024-07-31 published, posted

4TU.ResearchData

.m/.mat/mp4

A new computational perceived risk model for automated vehicles based on potential collision avoidance difficulty (PCAD)

• Horizon 2020 - SHAPE-IT (grant code 860410) European Union’s Horizon 2020
• Investigating Trust in Automation Toyota Motor Europe NV/SA

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