Simulation Environment

Our simulation environment enables every ADAS engineer to test, validate and calibrate algorithms and software at their desktop, enabling considerable savings in development cost

Photorealistic Rendering

Development and Validation of Computer Vision / DL Algorithms

Vehicle Models

Accurate vehicle models. Easy Integration of 3rd party models.

Sensor Models

Lidar, Radar, Camera, GPS, IMU, Ultrasonic, Raw and Labelled Values

AI Controlled Agents

Artificial Intelligence Controlled Agents with Calibratable Parameters

Scenario Generation

Define Agents and Trajectories Weather Conditions Automate Testing

Environment Generation

Import HD Map Easily Recreate Infrastructure and Landmarks

Photorealistic Rendering

Based on the industry leading Unreal Engine development platform, SIMSTAR simulation environments are perfectly suited to the development of Computer Vision algorithms including;

  • Training
  • Validation
  • Integration

Vehicle & Sensor Models

Built in vehicle model is based on Nvidia PhysX 3.X Engine, which allows realistic modelling of; “engine, transmission, differential, suspension and tire model.” Sensor suite includes:

  • RADAR (Raw / Smart)
  • Multiple Cameras (Raw / Smart)
  • LIDAR
  • IMU
  • Ultrasonic

3rd Party models can be integrated as a Simulink or compiled model.

Vehicle & Sensor Models

Built in vehicle model is based on Nvidia PhysX 3.X Engine, which allows realistic modelling of; “engine, transmission, differential, suspension and tire model.” Sensor suite includes:

  • RADAR (Raw / Smart)
  • Multiple Cameras (Raw / Smart)
  • LIDAR
  • IMU
  • Ultrasonic

3rd Party models can be integrated as a Simulink or compiled model.

Custom Environment and Scenario Definition

Through the Environment Editor user can easily modify the built-in environments or create new ones from scratch, including importing map information from OpenStreetMap. The Scenario Definition API gives user the possibility to;

  • Set-up automated testing in “infinite” environment
  • Define number of agents and their default behaviour (aggressive / assertive / defensive)
  • Define special agent trajectories / behaviours
  • Change weather conditions