ZalaZONE Automotive Proving Ground Smart City

Smart city 3D environment

Since R2024b

Description

The ZalaZONE Automotive Proving Ground Smart City scene is a 3D environment of a smart city with these five urban testing environments:

Low-speed parking zone with a logistics yard for commercial vehicles
Downtown area
High-speed urban multi-lane area
Special T-intersections zone
Hill section with 10% and 20% gradients

The largest city block size is 25 by 60 meters, and the complete smart city area is 150,000 square meters. Lanes range from 2.75 to 3.5 meters wide at lengths of 25 to 50 meters. This scene is intended for simulation of numerous traffic, technical (NCAP/V2X), and dynamic test scenarios reaching a top speed of 70 km/h.

Setup

To simulate a driving algorithm in this scene:

To install the ZalaZONE Automotive Proving Ground Smart City scene, run this command.
```
sim3d.utils.installUnrealEngineScene('ZalaZONE Smart City')
```
To confirm your scene selection, click Next.
To install the scene, click Install. MATLAB^® shuts down, and a window displays the download progress. Once the download is complete, MATLAB restarts.
In Simulink^®, add a Simulation 3D Scene Configuration block to your Simulink model.
In this block, set the Scene source parameter to Default Scenes.
Set the Scene name parameter to ZalaZONE Smart City.

Layout

Overall	Active Area

Scene Dimensions

This table provides the scene area corner locations in the world coordinate system. Dimensions are in m.

Location	X (m)	Y (m)	Z (m)
Scene — Top left	`-140`	`260`	`201`
Scene — Bottom left	`-140`	`-260`	`201`
Scene — Top right	`180`	`260`	`201`
Scene — Bottom right	`180`	`-260`	`201`

Recommended Starting Locations

This table provides the recommended starting locations for the vehicle in the world coordinate system. Dimensions are in m and deg.

City Location Description	X (m)	Y (m)	Z (m)	Roll (deg)	Pitch (deg)	Yaw (deg)
1. Intersection in the downtown area heading toward the hills.	`859.5`	`1790`	`203`	`0`	`0`	`-90`
2. On the hill heading toward the downtown area.	`923`	`1520`	`203.2`	`0`	`5.73`	`107`
3. In the roundabout heading toward the downtown area.	`745.8`	`1861`	`202.5`	`0`	`0`	`-15.5`
4. Intersection in the downtown area heading away from the hills.	`872`	`1748`	`203`	`0`	`0`	`90`

Coordinate System

The road geometry is represented according to the WGS84 coordinate system. The starting point of the project is recorded in 46.8795303682 latitude, 16.8254638824 longitude coordinates. The position and location of the objects in the program are given relative to (X,Y,Z [m]; fi,omega,kappa[deg]). The point clouds used to build the model are available in the UTM projection from, BME Automated Drive.

Objects

Order Signs

Locations

Objects	X (m)	Y (m)	Z (m)	Rotation (deg)
Stop Signs	`856.641`	`1489.707`	`199.475`	`-90`
	`855.044`	`1563.649`	`200.129`	`-90`
	`886.942`	`1675.685`	`201.488`	`90`
	`810.573`	`1662.547`	`201.049`	`-90`
	`809.268`	`1813.582`	`202.304`	`-90`
	`867.734`	`1926.239`	`203.685`	`90`
	`925.663`	`1937.501`	`203.308`	`-80`
	`725.347`	`1951.642`	`202.738`	`30`
	`923.341`	`1640.772`	`202.184`	`-30`
	`920.677`	`1604.068`	`205.968`	`70`
	`956.681`	`1585.988`	`199.300`	`-90`
	`924.748`	`1501.875`	`200.594`	`-120`

Warning Signs

Locations

Objects	X (m)	Y (m)	Z (m)	Rotation (deg)
Yield Signs	`732.176`	`1489.404`	`198.414`	`90`
	`713.841`	`1505.846`	`198.528`	`-90`
	`725.809`	`1571.229`	`199.083`	`90`
	`709.583`	`1577.057`	`199.509`	`180`
	`753.844`	`1561.463`	`199.334`	`0`
	`752.664`	`1574.795`	`198.867`	`180`
	`767.519`	`1574.111`	`199.668`	`180`
	`813.594`	`1576.017`	`199.815`	`180`
	`826.267`	`1563.008`	`199.991`	`0`
	`755.661`	`1598.342`	`199.729`	`-90`
	`857.938`	`1532.234`	`199.905`	`-90`
	`851.777`	`1590.762`	`200.848`	`180`
	`851.410`	`1663.029`	`201.404`	`-90`
	`761.102`	`1673.006`	`200.569`	`180`
	`721.777`	`1668.427`	`200.042`	`90`
	`716.739`	`716.739`	`201.129`	`90`
	`727.905`	`1769.484`	`201.116`	`180`
	`761.679`	`177.579`	`201.339`	`180`
	`775.798`	`1749.993`	`201.415`	`0`
	`819.932`	`1757.026`	`202.005`	`0`
	`821.388`	`1758.037`	`202.137`	`-90`
	`844.108`	`1758.998`	`202.196`	`-90`
	`886.415`	`1777.855`	`202.311`	`90`
	`880.273`	`1813.517`	`202.944`	`0`
	`848.754`	`1814.376`	`202.764`	`180`
	`871.860`	`1849.023`	`202.998`	`0`
	`837.471`	`1849.023`	`203.147`	`90`
	`858.253`	`1874.384`	`203.481`	`180`
	`815.327`	`1888.301`	`203.087`	`0`
	`776.922`	`1855.946`	`202.826`	`90`
	`772.855`	`1873.668`	`202.722`	`-30`
	`749.937`	`1852.289`	`202.676`	`-120`
	`715.128`	`1856.289`	`202.091`	`90`
	`724.030`	`1868.838`	`202.107`	`180`
	`733.890`	`1869.838`	`202.099`	`0`
	`754.316`	`1858.010`	`202.508`	`150`
	`771.983`	`1878.507`	`202.525`	`0`
	`825.752`	`1919.427`	`203.555`	`-90`
	`823.984`	`1913.321`	`203.450`	`-140`
	`829.409`	`1920.794`	`203.565`	`-180`
	`840.625`	`1927.259`	`203.517`	`90`
	`837.096`	`1922.024`	`203.712`	`-90`
	`866.486`	`1954.685`	`204.104`	`0`
	`837.096`	`1961.208`	`203.796`	`0`
	`803.589`	`1970.059`	`204.033`	`90`
	`798.923`	`1961.335`	`203.615`	`0`
	`768.164`	`1957.751`	`203.503`	`0`
	`958.611`	`1930.751`	`202.652`	`90`
	`912.834`	`1881.448`	`203.111`	`180`
	`967.098`	`1869.290`	`202.293`	`0`
	`958.572`	`1791.372`	`201.690`	`-50`
	`929.784`	`1783.088`	`201.741`	`180`
	`937.503`	`1767.511`	`202.020`	`0`
	`910.9542`	`1781.930`	`201.787`	`180`
	`928.086`	`1678.696`	`200.644`	`180`
	`939.125`	`1664.692`	`200.856`	`0`
	`957.692`	`1663.001`	`200.206`	`-90`
	`899.989`	`1598.774`	`203.488`	`0`
	`896.768`	`1538.692`	`203.449`	`180`

Tips

If you have the Automated Driving Toolbox™ Interface for Unreal Engine^® Projects support package, then you can modify this scene. Enable the MathWorksAutomotiveContent plugin to use the scene in Unreal Engine projects. The scene name in the plugin is ZalaZONESmartCity.
For more details on customizing scenes, see Customize Unreal Engine Scenes for Automated Driving.

References

[1] Szalay, Zsolt. "Critical Scenario Identification Concept: The Role of the Scenario-in-the-Loop Approach in Future Automotive Testing." IEEE Access 11 (July 2023): 82464–76. https://doi.org/10.1109/ACCESS.2023.3298875.

[2] Duleba, Szabolcs, Tamás Tettamanti, Ádám Nyerges, and Zsolt Szalay. “Ranking the Key Areas for Autonomous Proving Ground Development Using Pareto Analytic Hierarchy Process." IEEE Access 9 (March 2021): 51214–30. https://doi.org/10.1109/ACCESS.2021.3064448.

[3] Somogyi, Árpád, Tamás Tettamanti, Pál Varga, Zsolt Szalay, Dániel Baranyai, and Tamás Lovas. "Digital Map Generation Workflow Demonstrated on ZalaZONE Automotive Proving Ground Elements." NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium, Miami, FL, May 8-12, 2023: 1–6. https://doi.org/10.1109/NOMS56928.2023.10154403.

[4] Gangel, Kálmán, Zoltán Hamar, András Háry, Áron Horváth, Gábor Jandó, Balázs Könyves, Dániel Panker et al. "Modelling the ZalaZONE Proving Ground: A Benchmark of State-of-the-Art Automotive Simulators PreScan, IPG CarMaker, and VTD Vires." Acta Technica Jaurinensis 14, no. 4 (2021): 488–50.. https://doi.org/10.14513/actatechjaur.00606.

Version History

Introduced in R2024b