Synkrotron provides advanced solutions for autonomous driving simulation built on top of CARLA. Synkrotron's product suite OASIS supports a broad range of applications including scenario generation, sensor modelling, traffic simulation and data management. Based on a flexible architecture OASIS can be deployed on the cloud at scale, or in a developer's local environment for prototyping.
OASIS Sim is a fully-fledged, scalable simulation platform with CARLA at its core. It supports the complete life-cycle of AD simulation:
- scenario import & editing with a graphical user interface
- sensor configuration
- distributed task management
- diagnosis through rich simulation data and logs
Both cloud and local deployment are available through containerized packaging. Comprehensive APIs are exposed for integration with DevOps too. You can [request a trial](https://synkrotron.ai/contact.html) from Synkrotron.
OASIS Data is a platform for managing the high volume of data flowing through the autonomous driving R&D pipeline. Data-driven development of AD systems are made possible with OASIS Data via the following functionalities:
* auto-labeling of data with pre-trained perception models
* scenario tagging and reconstruction with OpenX format output
The processed data from the platform supports downstream applications such as scenario re-simulation with Oasis Sim, retraining of perception models with new labeled data and operational fleet management.
## __Synkrotron Tools and Services__
In addition to the complete solutions introduced above, Synkrotron also offers the following developer tools and services to facilitate AD simulation development.
| Product/service | Description |
| -----------| ------ |
| __Sensor model__: fish-eye camera | Fisheye camera with configurable distortion parameters |
| __Sensor model__: LIDAR | Advanced lidar model with configurable shutter mode and material reflections |
| __SOTIF__ scenario generation tool | Given the ODD description from user, identify important scenario elements and generate critical scenarios to help uncover unknown unsafe domains of the autonomous driving systems, achieved by using an ontology-based method combined with iterative optimizations against evaluations in Carla |
| __Map creation__ | Given lidar scan data (can also provide road-test data collection equipments & service), create HD maps for the user and output OpenDrive files which can be loaded in Carla for PnC tests or logsim |
| __Sensor model__: physics models for camera | Supports CMOS simulations and outputs 12bit raw sensor data, for customers who develop ISP algorithms or who has ECUs that need raw data from camera instead of RGBs |
| __Static scene creation__ | Combined HD mapping, 3D asset development and procedural modeling to create 3D static scenes/digital twins for users |
| __Dynamic scenario reconstruction__ | On top of static scenes, also detect and track various traffic participants from user's road-test data; the recovered trajectories/behaviors are turned into OpenScenario 1.0 files and can be re-simulated in CARLA |
