# Matlab Occlusion Simulator
## Installation
install git lfs for large files
```
sudo apt-get -y install git-lfs
```
Note: if you did not install git-lfs before cloning the repo, you can use:
```
git lfs fetch
git lfs get
```
This will update the lfs tagged files
initialize git submodules
```
git submodule update --init
```
## Usage
### Getting Started
To get started you can chose to run "RunEvaluationManual_staticOcclusion.m" in Matlab. This will automatically start the simulation on a small dataset.
In the script you can adjust several parameters such as:
* Field of View of the automated vehicles
* AV percentage
* enable visualization
* enable further visualization for debugging
### Further Usage
The same applies for the dynamic simulation. If you want to run several simulation at once, you can use the "RunEvaluationParallel*.mat." It is configured to run the several evaluations on serveral CPUs. To check the current status of the evaluation, type "job" in the command window and check your cpu load to see if it is running.
## Methodology
An occlusion evaluation software using Matlab was developed to evaluate possible dynamic occlusions. The input for this software uses simulation data generated by Aimsun Next. The methodology comprises three main steps: Running the Aimsun simulation, Evaluating the occlusions, and Generating the resulting heat map.
1. **Evaluating Occlusion Based on Generated Data:**
The collected vehicle positions from an Aimsun simultion are used to evaluate occlusion generated by moving objects. The occlusion evaluation approach is illustrated in the figure below, showing a top-down view of an intersection with cars and buildings. The occlusion of one specific timestep is shown, based on all cars and buildings in the road network at that time.
<img src="images/sim_view.png" width="50%">
Small black boxes represent manually driven cars without environment-observing sensors, generating dynamic occlusion for connected automated vehicles (CAVs). Small red boxes highlight the CAVs currently in the road network. A circle around each CAV indicates the occlusion it is subjected to by non-CAVs, represented by green and red circles.
The next figure shows an illustration of the ray traycing approach.
<img src="images/occlusion_ray_traycing.png" width="50%">
Buildings in the image are downloaded from Open Street Maps
<a href="https://www.openstreetmap.org/">OpenStreetMap</a> - Published under referring to "Produced Work" <a href="https://opendatacommons.org/licenses/odbl/">ODbL</a>
3. **Generating an Occlusion Heat Map:**
A resulting heat map is generated based on an occupancy grid map with a one-meter resolution. Every time one or more CAVs observe a bin of this grid map during a timestep, the bin counter is increased by one. After the entire simulation run, a heat map is generated, showing which locations of the road network are dynamically occluded.
<img src="images/heat_map_view.png" width="60%">
From the heat map, this approach can reveal dynamic occlusions in a road network and adjust the penetration rate of connected automated vehicles (CAVs) in a mixed-traffic scenario. The red arrow indicates where a dynamically occluded spot in the road network was observed. In summary, this methodology enables the evaluation of static and dynamic occlusions in urban intersections by running an Aimsun simulation of a road network, evaluating occlusion based on generated data, and generating an occlusion heat map.
## Requirements
The software requires the following dependencies:
### Hard Requirements:
- MATLAB (tested using 2023a)
- Automated Driving Toolbox
- Mapping Toolbox
### Soft Requirements:
- Parallel Computing Toolbox
## Limitations
The algorithm runs quite slow. Do not expect realtime capabilities. The "ray-tracing" approach in this repository is utilized using basic mathematical equations wihtout advanced acceleration techniques. A small increase in performance was introdced using the Intersections algortihm from Douglas M. Schwarz.
## License
This software is released under the [MIT License](LICENSE).
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Matlab遮挡模拟器.zip
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1.版本:matlab2014/2019a/2021a 2.附赠案例数据可直接运行matlab程序。 3.代码特点:参数化编程、参数可方便更改、代码编程思路清晰、注释明细。 4.适用对象:计算机,电子信息工程、数学等专业的大学生课程设计、期末大作业和毕业设计。
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Matlab遮挡模拟器.zip (34个子文件)
Matlab遮挡模拟器
MatlabOcclusionSimulator-main
Results
.gitignore 79B
RunEvaluationParallel_dynamicOcclusion.m 2KB
RunEvaluationManual_staticOcclusion.m 2KB
RunEvaluationManual_dynamicOcclusion.m 2KB
submodules
intersections
intersections.m 12KB
license.txt 1KB
matlab-tools
readme.md 4KB
CITATION.cff 337B
EvaluateScenarioSingle.m 2KB
osmData
arcis_theresien_crossing.osm 131B
geotheplatz.osm 131B
RunEvaluationParallel_staticOcclusion.m 3KB
aimsunData
staticOcclusionScenario_ShortTest.xml 131B
staticOcclusionScenario.xml 133B
dynamicOcclusionScenario_ShortTest.xml 131B
dynamicOcclusionScenario.xml 133B
Scripts
analyseBinMapData.m 9KB
find_x_for_y.m 932B
analyseMultipleBinmaps.m 3KB
updateBoundingBox.m 2KB
EvaluateAVpenetration.m 3KB
getAngleFromXY.m 231B
getAdjacentBins.m 5KB
getAVpentrationRate.m 9KB
analyseData.m 25KB
readOSM.m 11KB
getBoundings.m 344B
analyseSingleBinmap.m 4KB
analyseSingleBinmapObservationRate.m 6KB
getPolyShape.m 543B
images
sim_view.png 130B
occlusion_ray_traycing.png 131B
heat_map_view.png 131B
EvaluateMultipleBinMaps.m 4KB
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