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Ozone
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Next-generation Benchmark by Digital Twin
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# Ozone: A Unified Platform for Transportation Research Intelligent Transportation Systems increasingly depend on heterogeneous data from roadside cameras, UAV imagery, LiDAR, and in-vehicle sensors, yet the lack of unified data standards, model interfaces, and evaluation protocols across these sources hampers reproducibility, cross-dataset benchmarking, and cross-region transferability of research findings. Existing trajectory datasets follow incompatible conventions for coordinate systems, object representations, and metadata fields, forcing researchers to build custom preprocessing pipelines for each dataset and simulator combination. To address these challenges, we propose Ozone, a unified platform for transportation research organized around five interconnected layers---Hardware, Data, Model, Evaluation, and Prototype---each with standardized schemas, automated conversion pipelines, and interoperable interfaces. ## Standard Interface ### Trajactory Data The trajectory data standard definition is available in the [TrajectoryDataFormat Wiki](https://github.com/ZhilingResearch/Ozone/wiki/TrajectoryDataFormat). Use the Standardized Data Toolkit data loader to convert raw datasets into [this format](./standardized_data_toolkit/dataloader/README.md). Current dataset converters in [`dataloader`](./standardized_data_toolkit/dataloader/README.md#supported-datasets): | Dataset | Converter Support | |:--------|:------------------| | [highD](https://levelxdata.com/highd-dataset/) | [Yes](./standardized_data_toolkit/dataloader/README.md#supported-datasets) | | [inD](https://levelxdata.com/ind-dataset/) | [Yes](./standardized_data_toolkit/dataloader/README.md#supported-datasets) | | [NGSIM](https://data.transportation.gov/stories/s/Next-Generation-Simulation-NGSIM-Open-Data/i5zb-xe34/) | [Yes](./standardized_data_toolkit/dataloader/README.md#supported-datasets) | | [CitySim](https://github.com/UCF-SST-Lab/UCF-SST-CitySim1-Dataset) | [Yes](./standardized_data_toolkit/dataloader/README.md#supported-datasets) | | [Waymo](https://waymo.com/open/) | [Yes](./standardized_data_toolkit/dataloader/README.md#supported-datasets) | | [OpenACC](https://data.jrc.ec.europa.eu/dataset/9702c950-c80f-4d2f-982f-44d06ea0009f) | [Yes](./standardized_data_toolkit/dataloader/README.md#supported-datasets) | | [ADAS Single-Vehicle (Central Ohio)](https://data.transportation.gov/) | [Yes](./standardized_data_toolkit/dataloader/README.md#supported-datasets) | | [ADAS Two-Vehicle (Central Ohio)](https://data.transportation.gov/) | [Yes](./standardized_data_toolkit/dataloader/README.md#supported-datasets) | For dataset-specific [metadata](./standardized_data_toolkit/dataset_metadata.md), field mapping, coordinate notes, and intermediate variables. Other useful [toolkit](./standardized_data_toolkit/README.md) modules are summarized, including: - `ssm_calculator/`: safety surrogate metric (SSM) computation and analysis - `utils/`: shared utilities for data loading, JSON export, and visualization support ### Crash Data #### Human driven vehicle(HDV) #### Autonomous Vehicle(AV) #### Advanced Driver Assistance System (ADAS) ### Carla API ### Hardware #### Driver Simualtor | Type | Manufacturer | model | force feedback |:-------------------------------------------------------------:|:------------------------------------------------:|:----------------:|:----------------:| | Wheel | Logitech | G923 | yes | Wheel | Thrustmaster | / | yes | Wheel | Moza | / | yes #### Driver State Monitoring(DSM) | Type | Manufacturer | model | |:---------------------------------------------------------------------------------------:|:------------------------------------------------:|:----------------:| | Eyeteacker | Tobbi Eye Tracking | Pro 3 | Eyeteacker | Smarteye | / | EGG | / | / ## Traffic Flow Simulation Calibration ## Benchmarking ### Huamn Factor ### Autonmous Vehicle ### Road Site Unit ## Digital Twin Basemap For Trajactory Dataset ## Ubiquitous Traffic Eyes Dataset | Location Name | Real World Map | Digital Twin Map | |:---------------------------------------------------------------------------------------:|:------------------------------------------------:|:----------------:| | [ Expressway 1(SQM1, SQM2) <br/> Expressway<br />China <br/>](http://seutraffic.com/) |  | ![]() | | [ Expressway 2(YTAvenue3) <br/> Expressway<br />China <br/>](http://seutraffic.com/) |  | ![]() | | [ Expressway 3(CKQ4) <br />Expressway<br />China <br/>](http://seutraffic.com/) |  | ![]() | | [Expressway 4(RML7) <br />Expressway<br />China <br/>](http://seutraffic.com/) |  | ![]() | | [Urban Road 1(XAM-N5, XAM-N6)<br />Expressway<br />China <br/>](http://seutraffic.com/) |  | ![]() | | [Urban Road 2(PKDD8) <br />Expressway<br />China <br/>](http://seutraffic.com/) |  | ![]() | | [Urban Road 3(XAM-S9) <br />Expressway<br />China <br/>](http://seutraffic.com/) |  | ![]() | ## CitySim Dataset Released by the University of Central Florida (UCF), CitySim provides vehicle trajectories extracted from drone aerial videos on urban roads, with a focus on safety and high-risk events. It supports traffic safety research and digital twin applications. | Location Name | Real World Map | Digital Twin Map | |:------------------------------------------------------------------------------------------------------------:|:-------------------------------------:|:----------------:| | [ Intersection A <br />Intersection<br />USA <br/>](https://github.com/UCF-SST-Lab/UCF-SST-CitySim1-Dataset) |  | ![]() | | [ Intersection B <br />Intersection<br />USA <br/>](https://github.com/UCF-SST-Lab/UCF-SST-CitySim1-Dataset) |  | ![]() | | [ Intersection C <br />Intersection<br />USA <br/>](https://github.com/UCF-SST-Lab/UCF-SST-CitySim1-Dataset) |  | ![]() | | [ Intersection D <br />Intersection<br />USA <br/>](https://github.com/UCF-SST-Lab/UCF-SST-CitySim1-Dataset) |  | ![]() | | [ Intersection E <br />Intersection<br />USA <br/>](https://github.com/UCF-SST-Lab/UCF-SST-CitySim1-Dataset) |  | ![]() | | [ Intersection F <br />Intersection<br />USA <br/>](https://github.com/UCF-SST-Lab/UCF-SST-CitySim1-Dataset) |  | ![]() | | [ Roundabout A <br />Roundabout<br />USA <br/>](https://github.com/UCF-SST-Lab/UCF-SST-CitySim1-Dataset) |  | ![]() | | [ Roundabout B <br />Roundabout<br />USA <br/>](https://github.com/UCF-SST-Lab/UCF-SST-CitySim1-Dataset) |  | ![]() | | [ Expressway A <br />Expressway<br />China <br/>](https://github.com/UCF-SST-Lab/UCF-SST-CitySim1-Dataset) |  | ![]() | | [ Expressway B <br />Expressway<br />China <br/>](https://github.com/UCF-SST-Lab/UCF-SST-CitySim1-Dataset) |  | ![]() | | [ Freeway B <br />Freeway<br />China <br/>](https://github.com/UCF-SST-Lab/UCF-SST-CitySim1-Dataset) |  | ![]() | | [ Freeway C <br />Freeway<br />China <br/>](https://github.com/UCF-SST-Lab/UCF-SST-CitySim1-Dataset) |  | ![]() | | [ Freeway D <br />Freeway<br />China <br/>](https://github.com/UCF-SST-Lab/UCF-SST-CitySim1-Dataset) |  | ![]() | ## NGSIM Dataset A classic public dataset released by the U.S. Department of Transportation. It contains high-precision vehicle trajectories from multiple road types and is widely used in traffic flow modeling and driver behavior research. | Location Name | Real World Map | Digital Twin Map | |:---------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------:|:----------------:| | [ I-80<br/>](https://data.transportation.gov/stories/s/Next-Generation-Simulation-NGSIM-Open-Data/i5zb-xe34/) | **<img src="main/NGSIM/I-80/1.png" width="450" />** | ![]() | | [ US-101<br/>](https://data.transportation.gov/stories/s/Next-Generation-Simulation-NGSIM-Open-Data/i5zb-xe34/) | **<img src="main/NGSIM/US-101/1.png" width="450" />** | ![]() | | [ Lankershim<br/>Boulevard<br/>](https://data.transportation.gov/stories/s/Next-Generation-Simulation-NGSIM-Open-Data/i5zb-xe34/) | **<img src="main/NGSIM/Lankershim-Boulevard/1.png" width="450" />** | ![]() | | [ Peachtree<br/>Street<br/>](https://data.transportation.gov/stories/s/Next-Generation-Simulation-NGSIM-Open-Data/i5zb-xe34/) | **<img src="main/NGSIM/Peachtree-Street/1.png" width="450" />** | ![]() | ## pNEUMA Dataset A large-scale urban trajectory dataset collected in downtown Athens by a drone swarm during peak hours. Released by EPFL and collaborators, it emphasizes natural driving trajectories under congested and high-density traffic conditions. | Location Name | Real World Map | Digital Twin Map | |:-----------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:----------------:| | [ platia <br />Omonias<br/>](https://open-traffic.epfl.ch/) | <img src="main/pNEUMA/platia-Omonias/1.png" width="450" height="300" alt="Omonia Square"/> | ![]() | ## INTERACTION Dataset A dataset focusing on interactive driving scenarios such as ramps, roundabouts, intersections, and signalized intersections. It provides high-quality maps and scene segmentation, and is widely used for interactive trajectory prediction research. | location Name | Real World Map | Digital Twin Map | |:-------------:|:--------------:|:----------------:| ## Zen Traffic Data Provided by Hanshin Expressway in Japan, this dataset offers long-term, wide-area vehicle trajectories along with related influencing factors. It is often used for expressway operation monitoring and incident analysis. | Location Name | Real World Map | Digital Twin Map | |:-----------------------------------------------------------------------:|:--------------------------------------------------------------------:|:----------------:| | [ Hanshin <br />Expressway<br/>](https://zen-traffic-data.net/english/) | <img src="main/INTERACTION/Hanshin-Expressway/1.png" width="450" height="300" alt="Hanshin Expressway"/> | ![]() | ## ROCO Dataset A roundabout-specific dataset constructed for traffic conflict and incident studies. It uses roadside multi-camera recordings, trajectory extraction, and manual annotations of conflicts/collisions with severity labels. | Location Name | Real World Map | Digital Twin Map | |:--------------------------------------------------------:|:--------------------------------------------------------------------:|:----------------:| | [ Michigan<br />Traffic lab<br/>](https://github.com/michigan-traffic-lab/ROCO) | **<img src="main/ROCO/Ann-Arbor/1.png" width="450" height="300" />** | ![]() | ## SIND A Drone Dataset at Signalized Intersection in China ## Dragon Lake Parking (DLP) Dataset The Dragon Lake Parking (DLP) Dataset contains annotated video and data of vehicles, cyclists, and pedestrians inside a parking lot. We collected it by flying a drone above a huge parking lot. ## I-24 MOTION A large-scale traffic sensing platform deployed on a 4-mile section of I-24 in Tennessee, USA. It provides vehicle trajectories, multi-camera video, and sensor data for traffic management and autonomous driving research. | Location Name | Real World Map | Digital Twin Map | |:---------------------------------------------------------:|:--------------------------------------------------------------------------------------:|:----------------:| | [ Nashville<br />](https://i24motion.org/) | <img src="main/I-24-MOTION/Nashville/1.png" width="450" height="300" alt="Nashville"/> | ![]() | ## 100-Car Naturalistic Driving Study (NDS) A landmark naturalistic driving study conducted by VTTI, NHTSA, and VDOT. It collected multi-channel in-vehicle video and vehicle dynamics sensor data, and compiled an “event database” (crash/near-crash/baseline). It remains a key resource for analyzing naturalistic driving behavior. | Location Name | Real World Map | Digital Twin Map | |:-----------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|:----------------:| | [ Washington, D.C. <br />](https://zen-traffic-data.net/english/) | <img src="main/100-car/Washington/1.png" width="450" height="300" alt="District of Columbia"/> | ![]() | ## High-D Dataset A highway vehicle trajectory dataset collected by RWTH Aachen (ika team) using drones on German highways. The trajectories are automatically extracted and designed for autonomous driving and microscopic traffic flow studies. | Location Name | Real World Map | Digital Twin Map | |:-----------------------------------------------------------------------:|:-------------------------------------------------------------------------:|:----------------:| | [ weisweiler<br />](https://levelxdata.com/highd-dataset/) | <img src="main/highD/weisweiler/1.png" width="450" height="300"/> | ![]() | | [ garzweiler<br />](https://levelxdata.com/highd-dataset/) | <img src="main/highD/garzweiler/1.png" width="450" height="300"/> | ![]() | | [ grevenbroich<br />](https://levelxdata.com/highd-dataset/) | <img src="main/highD/grevenbroich/1.png" width="450" height="300"/> | ![]() | | [ bergheim-sud<br />](https://levelxdata.com/highd-dataset/) | <img src="main/highD/bergheim-sud/1.png" width="450" height="300"/> | ![]() | | [ serways<br />raststatte<br />](https://levelxdata.com/highd-dataset/) | <img src="main/highD/serways-raststatte/1.png" width="450" height="300"/> | ![]() | | [ koln<br />west<br />](https://levelxdata.com/highd-dataset/) | <img src="main/highD/koln-west/1.png" width="450" height="300"/> | ![]() | ## in-D Dataset | Dataset Name | Real World Map | Digital Twin Map | |:--------------------------------------------------------------------:|:--------------------------------------------:|:----------------:| | [ Bendplatz<br />](https://levelxdata.com/ind-dataset/) | <img src="main/in-D/Bendplatz/1.png" width="450" height="300"/> | ![]() | | [ Frankenburg<br />](https://levelxdata.com/ind-dataset/) | <img src="main/in-D/Frankenburg/1.png" width="450" height="300"/> | ![]() | | [ Heckstrasse<br />](https://levelxdata.com/ind-dataset/) | <img src="main/in-D/Heckstrasse/1.png" width="450" height="300"/> | ![]() | | [ Neukollner<br />Strasse<br/>](https://levelxdata.com/ind-dataset/) | <img src="main/in-D/Neukollner_Strasse/1.png" width="450" height="300"/> | ![]() | ## round-D Dataset | Dataset Name | Real World Map | Digital Twin Map | |:-------------------------------------------------------------:|:--------------------------------------------------------------------------:|:----------------:| | [ Neuweiler<br />](https://levelxdata.com/round-dataset/) | **<img src="main/round-D/Neuweiler/1.png" width="450" height="300"/>** | ![]() | | [ KackertstraBe<br />](https://levelxdata.com/round-dataset/) | **<img src="main/round-D/KackertstraBe/1.png" width="450" height="300"/>** | ![]() | | [ Thiergarten<br />](https://levelxdata.com/round-dataset/) | **<img src="main/round-D/Thiergarten/1.png" width="450" height="300"/>** | ![]() | ## Digital Twin Basemap For Autonomous Vehicle Test Field | Dataset Name | Real World Map | Digital Twin Map | |:------------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------:|:------------------------------------------------------------------------------:| | [ Bejing YiZhuang <br />Region<br />China<br/>](https://raw.githubusercontent.com/ZhilingResearch/NBDT/main/asset/MainPage/bjyz.png) | **<img src="main/beijing/yizhuang/1.png" width="450" height="300" />** | **<img src="main/beijing/yizhuang/digital/1.png" width="450" height="300" />** | ## Special Digital Twin Basemap ## Realworld Test Field Digital Twin Basemap with RSU/Sensor data avaliviable. | Chengdu Underground Parking Garage | Intersection of Signal Light Road, ASU | |:----------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------:| | <img src="main/smaller_digital/Underground%20Parking%20Garage/1.png" width="700" height="280<br/>"/> | <img src="main/smaller_digital/ASU/1.png" width="700" height="400"/> | ## Smaller Test Basemap | Expressway | Mountain Road | Rural Road | |:-------------------------------------------------------:|:----------------------------------------------------:|:----------------------------------------------------:| |  |  |  | | Urban Arterial Road | Urban Intersection | Urban Local Road | |  |  |  | | Urban Tunnel | |  | ## Features | Digital Twin Base Map | Sumo Carla Co-Simulation | Croner Cases Testing | |:---------------------------------------------------------------------------------:|:--------------------------------------------------------------------------:|:-----------------------------------------------------------------------:| | **<img src="main/feature/digital_twin_basemap/1.png" width="450" height="300"/>** | **<img src="main/feature/Co_Simulation/1.png" width="450" height="300"/>** | **<img src="main/feature/Croner/1.png" width="450" height="300"/>** | | Human Factor Study | Sensor Simulation | Autonomous Vehicle Simulation | | **<img src="main/feature/Human/1.png" width="450" height="300"/>** | **<img src="main/feature/Sensor/1.png" width="450" height="300"/>** | **<img src="main/feature/Autonomous/1.png" width="450" height="300"/>** |
