Volvo Car Corporation

Minming Zhao

Sohini Roy Chowdhury

Srikar Muppirisetty

Mats Jonasson

Niklas Ohlsson

Sihao Ding

Ying Li

Saurabh Gawande

Andreas Wallin

Harshavardhan Reddy Dasari

Gambhir Bista

A LIDAR-based method of determining an absolute speed of an object at a relatively longer distance from an ego vehicle, including: estimating a self speed of the ego vehicle using a first frame t−1 and a second frame t obtained from a LIDAR sensor by estimating an intervening rotation θ about a z axis and translation in orthogonal x and y directions using a deep learning algorithm over a relatively closer distance range; dividing each of the first frame t−1 and the second frame t into multiple adjacent input ranges and estimating a relative speed of the object at the relatively longer distance by subsequently processing each frame using a network, with each input range processed using a corresponding convolutional neural network; and combining the estimation of the estimating the self speed with the estimation of the estimating the relative speed to obtain an estimation of the absolute speed.

Long range lidar-based speed estimation

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 vehicle control method and system, including: receiving road friction information indicating road friction estimates for a plurality of regions surrounding the vehicle; detecting and determining a predicted trajectory for an object within the plurality of regions surrounding the vehicle; wherein the predicted trajectory for the object is determined based in part on the road friction estimates for the plurality of regions surrounding the vehicle; and modifying operation of the vehicle based on the predicted trajectory for the object. The predicted trajectory for the object is determined based in part on a risk map for the plurality of regions surrounding the vehicle that is generated from a road friction map for the plurality of regions surrounding the vehicle.

Systems and methods for fusing road friction to enhance vehicle maneuvering

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

A LIDAR-based method of determining an absolute speed of an object at a relatively longer distance from an ego vehicle, including: estimating a self speed of the ego vehicle using a first frame t-and a second frame t obtained from a LIDAR sensor by estimating an intervening rotation &#x3b8; about a z axis and translation in orthogonal x and y directions using a deep learning algorithm over a relatively closer distance range; dividing each of the first frame t-and the second frame t into multiple adjacent input ranges and estimating a relative speed of the object at the relatively longer distance by subsequently processing each frame using a network, with each input range processed using a corresponding convolutional neural network; and combining the estimation of the estimating the self speed with the estimation of the estimating the relative speed to obtain an estimation of the absolute speed.

Long range LIDAR-based speed estimation

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.

Methods and systems for generating and using a road friction estimate based on camera image signal processing

Parking spot detection methods and systems that detect and pair parking line markers, improving the accuracy and speed of parking spot detection in vehicular driver assist (DA) and autonomous driving (AD) applications. These methods and systems incorporate three major steps: (1) preprocessing&#x2014;one or more standard camera images are input into the algorithm and a bird's-eye-view (BEV) image is output from the algorithm; (2) deep neural network (DNN) segmentation of the parking line markers&#x2014;the BEV image is input into the algorithm and a binary image is output from the algorithm (with, e.g., the parking line markers areas displayed in white and the background displayed in black); and (3) binary image processing&#x2014;the binary image is input into the algorithm and detected and paired parking line markers representing the parking spots are output from the algorithm.

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Mountain View, CA, United States of America

Minming Zhao