Human driver risk perception model: Fundamental threat parameters and what makes a situation risky

The level of automation in vehicles is growing. But until all vehicles are completely automated, there will be a transition period where automated vehicles and human drivers coexist. Because these road users coexist, it is necessary that automated vehicles understand human drivers and vice versa, to resolve potential conflicts. This study aims to create a model that predicts human risk perception in different driving scenarios, to provide an understanding of the fundamental features of human threat perception while driving.

The model created is a multi-criteria decision-making process that uses KITTI Vision Benchmark data as an input. This model is tested against the data gathered by an online survey, where 1918 participants answered the question: "How high is the risk on a scale from 0-100?" for 100 situations. Furthermore, a multivariate regression is performed on the survey data, which is compared to the model.

Structure of code

The first step in the pipeline is the parsing of KITTI data from json to txt files. The next step is the selection of images to be used for the survey. With this data, the online survey is started. When the survey finishes, the result have to be parsed again from json and csv files to python dataframes and csv files. These result are then analysyed and compared with the model.

The code is split up into three sections based on the pipeline described above:

1. Parsing of KITTI data and selection of images
2. Parsing of survey result
3. Model creation, optimistation, and comparison

For a model based on ros, which can give realtime predictions, see the corresponding thread

If you want to use the code, you need to change the paths in the relevant files. For example change the data path to: '/resources/test_data'

File contents

kitti

• image_selector.py This file selects images from a given dataset. Needed to select images are a absolute data path to relevant folder, a relative path to the images/labels/oxts data and the different road types used.

• parseTrackletXML.py This file, created by Christian Herdtweck (Max Planck Institute for Biological Cybernetics) parses xml object files from the KITTI dataset to python objects.

• tracklet_parser.py This file parses xml files to .txt per image with help form parseTrackletXML.py

online

• appenFunctions.py This file parses a raw csv file with survey responses from Appen to a cleaned up csv file.

• dataCombiner.py This file combines the csv files from appenFUnctions.py and herokuFunctions.py whilest merging them on participant and filtering cheaters.

• herokuFunctions.py This file parses json files with results from the online survey to a csv file.

model

• compare.py This file analyses the survey results and compares them with the model predicitons. Most of the results come from this file.

• optimisation.py This file uses optimisation methods to calculate optimal model parameters. Multiple optimisation methods can be used.

• services.py This file's main function is the creation of csv files which contain all feature values per image. This is also done for the model feature, where it is a prediction.

Dependencies

• python3
• numpy
• pandas
• seaborn
• opencv-python
• sklearn
• matplotlib