Skip to content

Latest commit

 

History

History
68 lines (54 loc) · 3.63 KB

README.md

File metadata and controls

68 lines (54 loc) · 3.63 KB
Raw

Paper

You can find my bachelor thesis related to this repository here.

Prerequisites

Download Tortoise SVN from here.

Usage of this project requires BeamNG.research to be installed. A copy can be downloaded from here. Use SVN to download BeamNG.research and make sure to download revision 35. Once downloaded and extracted, it is mandatory to set the environment variable BNG_HOME to the trunk folder.

Additionally, download the Python version 3.7.0 (not tested with other versions) from here and make sure that Powershell uses the correct version. You can check it with python --version .

Installation

Clone the repository: git clone https://github.com/masskro0/Urban-Test-Generator-Fuel-Scoring-Function

After extracting, right-click on setup.ps1 and choose Run with Powershell. This creates the virtual environment and installs the required packages automatically. You will be asked whether you want to install the additional packages which are needed for the traffic lights evaluation experiment (optional and not needed for test generation). You can either install them [Y] or not [N].

This script will also run setup.py which creates folders and moves files to the correct directories. This requires to set the BNG_HOME environment variable as well as using BeamNGpy 1.15.

Usage

Once installed, you can run main.py which will generate test cases and run them inside BeamNG.research. You can configure the test generator with variables provided in main.py. urban_test_generator.py is the test generator itself.

To run the fuel-inefficiency experiment, simply run main() in evaluation\fuel_inefficiency\main.py and wait until it completes.

For the traffic lights experiments, there are several options. The function create_tests() will generate new test cases and moves them to all experiment folders. collect_images_existing_tests() runs all test cases of all experiments and collects images, while collect_images() collects images of only one specific test case of one experiment. Since I already collected images, you can just run predict_all_images() which will use the pretrained traffic light detection system to make predictions and visualize the results. plot_confusion_matrix() will plot a confusion matrix; I included examples for this method.

Traffic Light Detection

If you want to run the evaluation for the traffic light detection, you need to setup a traffic light detection model. I used this one: https://github.com/affinis-lab/traffic-light-detection-module

Features

  • The test generator generates randomly an urban-like scenario as XML files. The scenarios contain

    • intersections,
    • multi-lane roads,
    • valid road configurations,
    • positions for traffic lights and signs,
    • traffic light sequences and states,
    • parked cars positions,
    • other traffic participants (waypoints and spawn points),
    • waypoints for ego-car with lane switches
    • choosing the daytime

  • A scoring function for assessing how fuel inefficient a driver drives. The function contains the following metrics:

    • RPM (Rounds per minute) infraction checking
    • Throttle infraction checking
    • Brake infraction checking
    • Accelerate-and-Stop behaviour checking
    • Engine idling while standing checking

  • A test oracle for checking test-specific infractions during runtime to consider a test case as succeeded or failed. The oracles are:

    • Timeout
    • Crashing
    • Violating traffic rules at intersections