eHorizon Road Surface Condition
eHorizon Road Surface Condition
AI-based predictive information about the road surface conditions enabling safe assisted autonomous driving functions.
AI-based predictive information about the road surface conditions enabling assisted and autonomous driving functions.
- The innovative service delivers a completely new level of reliable information about the road condition – before the vehicle or driver can actually sense it
- The road is classified into dry, wet, heavy wet, icy and snowy road segments providing valuable insights for a large number of use cases
- AI models deliver predictive and granular information in short update cycles to avoid dangerous situations and substantially increase the safety for drivers
- Enables powertrain, chassis and ADAS function improvements and increases the availability of assisted and autonomous driving functions
Our disruptive approach supports new use cases and driving functions.
- The service comes with pre-filled road surface condition information and reduces dependency on vehicle data
- Flexible tuning of the service outputs allows for high customization of safety and comfort for drivers, e.g. availability of automated driving assistance systems
- Easy and quick deployment in new regions for large vehicle volumes
- Increases safety for drivers and avoids accidents, as road conditions beyond sensor visions are taken into account
- See here, how the eHorizon Road Surface Condition Service enables the driving function PreviewESC increasing safety
End-to-end service powered by a supervised model for reliable and highly accurate road condition information.
- Cutting-edge supervised models take into account various data sources combined with ground truth data
- Weather forecast data as basis to deliver global forecasts without pre-filling with vehicle data
- Precise service considering road topology, road type, water height and environmental conditions
- Optional enrichment of model with vehicle sensor data, e.g. outside temperature, wiper status and friction data
- Friction corridors can be derived from the road condition information
AI-based predictive information about the road surface conditions enabling assisted and autonomous driving functions.
- The innovative service delivers a completely new level of reliable information about the road condition – before the vehicle or driver can actually sense it
- The road is classified into dry, wet, heavy wet, icy and snowy road segments providing valuable insights for a large number of use cases
- AI models deliver predictive and granular information in short update cycles to avoid dangerous situations and substantially increase the safety for drivers
- Enables powertrain, chassis and ADAS function improvements and increases the availability of assisted and autonomous driving functions
Our disruptive approach supports new use cases and driving functions.
- The service comes with pre-filled road surface condition information and reduces dependency on vehicle data
- Flexible tuning of the service outputs allows for high customization of safety and comfort for drivers, e.g. availability of automated driving assistance systems
- Easy and quick deployment in new regions for large vehicle volumes
- Increases safety for drivers and avoids accidents, as road conditions beyond sensor visions are taken into account
- See here, how the eHorizon Road Surface Condition Service enables the driving function PreviewESC increasing safety
End-to-end service powered by a supervised model for reliable and highly accurate road condition information.
- Cutting-edge supervised models take into account various data sources combined with ground truth data
- Weather forecast data as basis to deliver global forecasts without pre-filling with vehicle data
- Precise service considering road topology, road type, water height and environmental conditions
- Optional enrichment of model with vehicle sensor data, e.g. outside temperature, wiper status and friction data
- Friction corridors can be derived from the road condition information
