Volvo Car Corporation

Srikar Muppirisetty

Sohini Roy Chowdhury

Minming Zhao

Mats Jonasson

Niklas Ohlsson

Saurabh Gawande

Vehicle alert and control systems and methods taking into account a detected road friction at a following vehicle and a predicted road friction by the following vehicle. The detected road friction between the following vehicle tires and the road surface may be assessed using a variety of methodologies and is used to compute a critical safety distance between the following vehicle and the preceding vehicle and a critical safety speed of the following vehicle. The predicted road friction ahead of the following vehicle may also be assessed using a variety of methodologies (lidar, camera, and cloud-based examples are provided) and is used to compute a warning safety distance between the following vehicle and the preceding vehicle and a warning safety speed of the following vehicle. These functionalities may be applied to vehicle/stationary object warning and response scenarios as well.

System and method for providing vehicle safety distance and speed alerts under slippery road conditions

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 system and method for estimating road conditions ahead of a vehicle, including: a LIDAR sensor operable for generating a LIDAR point cloud; a processor executing a road condition estimation algorithm stored in a memory, the road condition estimation algorithm performing the steps including: detecting a ground plane or drivable surface in the LIDAR point cloud; superimposing an M&#xd7;N matrix on at least a portion of the LIDAR point cloud; for each patch of the LIDAR point cloud defined by the M&#xd7;N matrix, statistically evaluating a relative position, a feature elevation, and a scaled reflectance index; and, from the statistically evaluated relative position, feature elevation, and scaled reflectance index, determining a slipperiness probability for each patch of the LIDAR point cloud; and a vehicle control system operable for, based on the determined slipperiness probability for each patch of the LIDAR point cloud, affecting an operation of the vehicle.

Vehicle systems and methods utilizing LIDAR data for road condition estimation

Methods and systems for generating annotated data for training vehicular driver assist (DA) and autonomous driving (AD) active safety (AS) functionalities and the like. More specifically, methods and systems for the fast estimation of three-dimensional (3-D) bounding boxes and drivable surfaces using LIDAR point clouds and the like. These methods and systems provide fast and accurate annotation cluster pre-proposals on a minimally-supervised or unsupervised basis, segment drivable surfaces/ground planes in a bird's-eye-view (BEV) construct, and provide fast and accurate annotation cluster pre-proposal labels based on the feature-based detection of similar objects in already-annotated frames. The methods and systems minimize the expertise, time, and expense associated with the manual annotation of LIDAR point clouds and the like in the generation of annotated data for training machine learning (ML) algorithms and the like.

Methods and systems for the fast estimation of three-dimensional bounding boxes and drivable surfaces using LIDAR point clouds

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 providing fast semantic proposals for image and video annotation including: extracting image planes from an input image; linearizing each of the image planes to generate a one-dimensional array to extract an input feature vector per image pixel for the image planes; abstracting features for a region of interest using a modified echo state network model, wherein a reservoir increases feature dimensions per pixel location to multiple dimensions followed by feature reduction to one dimension per pixel location, wherein the echo state network model includes both spatial and temporal state factors for reservoir nodes associated with each pixel vector, and wherein the echo state network model outputs a probability image; post-processing the probability image to form a segmented binary image mask; and applying the segmented binary image mask to the input image to segment the region of interest and form a semantic proposal image.

Methods and systems for providing fast semantic proposals for image and video annotation

The present invention relates to methods and systems for generating annotated data for training vehicular driver assist (DA) and autonomous driving (AD) active safety (AS) functionalities and the like. More specifically, the present invention relates to methods and systems for the fast estimation of three-dimensional (3-D) bounding boxes and drivable surfaces using LIDAR point clouds and the like. These methods and systems provide fast and accurate annotation cluster pre-proposals on a minimally-supervised or unsupervised basis, segment drivable surfaces/ground planes in a bird's-eye-view (BEV) construct, and provide fast and accurate annotation cluster pre-proposal labels based on the feature-based detection of similar objects in already-annotated frames. The methods and systems minimize the expertise, time, and expense associated with the manual annotation of LIDAR point clouds and the like in the generation of annotated data for training machine learning (ML) algorithms and the like.

Growing community of inventors

Gothenburg, Sweden

Srikar Muppirisetty