Volvo Car Corporation

Niklas Ohlsson

Mats Jonasson

Sohini Roy Chowdhury

Minming Zhao

Srikar Muppirisetty

A system and method for utilizing aggregated weather data (AWD) for deriving road surface condition (RSC) estimates. This system and method supplements road friction estimates (RFEs) made at the vehicle level with AWD in the cloud to form the RSC estimates, which are then transmitted to the vehicles such that more accurate RFEs can be made locally, and so on. Conventional RFE physics-based models are replaced with enhanced RFE trained machine learning (ML) models accordingly. Global RSC estimates are derived for each geographical region using weather and location constraints. Thus, improved autonomous driving and driver assist functions may be implemented, better driver warnings may be provided, and safer road trips may be planned in advance based on a thorough analysis of the drivable conditions.

System and method for utilizing aggregated weather data for road surface condition and road friction estimates

A method for estimating a tire property of a vehicle based on tire-to-road friction properties for a fleet of vehicles. The method includes: determining a tire-to-road friction for a plurality of vehicles, belonging to the fleet of vehicles, at a plurality of specified locations; determining a reference tire-to-road friction for the fleet of vehicles at each specified location; in a vehicle, determining a current tire-to-road friction at a first location being one of the specified locations; determining a difference between the current tire-to-road friction and the reference tire-to-road friction of the fleet for the first location; and estimating a tire property of the vehicle based on the determined difference. There is also provided a system configured to perform the described method.

Method and system for determining tire-to-road friction in a vehicle

Methods and systems for generating and utilizing a road friction estimate (RFE) indicating the expected friction level between a road surface and the tires of a vehicle based on forward looking camera image signal processing. A forward-looking camera image is pre-processed, patch segmented (both laterally and longitudinally, as defined by wheel tracks or the like), transformed into a bird's eye view (BEV) image using perspective transformation, patch quantized, and finally classified. The resulting RFE may be used to provide driver information, automatically control the associated vehicle's motion, and/or inform a cloud-based alert service to enhance driver safety.

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Gothenburg, Sweden

Niklas Ohlsson