Here Global B.v.

Vladimir Boroditsky

Leon Stenneth

Gavril Adrian Giurgiu

James Adeyemi Fowe

Leo Modica

Sanjay Chand Sood

Aaron J Dannenbring

Alex Averbuch

Dongwook Jang

Anton Anastassov

Eric Linder

Sanjay C Sood

Methods for providing a highly assisted driving (HAD) service include: (a) transmitting telematics sensor data from a vehicle to a remote first server; (b) transmitting at least a portion of the telematics sensor data from the remote first server to a remote second server, wherein the remote second server is configured to execute a HAD application using received telematics sensor data, and wherein the HAD application is configured to output a HAD service result; and (c) transmitting the HAD service result from the remote second server to a client. Apparatuses for providing a HAD service are described.

Highly assisted driving platform

Embodiments include apparatus and method for collecting observation data for updating a geographic database. An initial observation is collected by a first mobile device, a first vehicle, or a first sensor. Along with the geographic position, data indicative of the first observation is send to a server. The central server may analyze of the initial observation data to determine if additional observations should be made and define a bounding box from the geographic position of the first mobile device and the analysis of the initial observation data. A request for additional observations is generated and sent to at least one second mobile device, second vehicle, or second sensor based on the bounding box.

Map based feedback loop for vehicle observations

Map based feedback loop for vehicle observation

A method, apparatus and computer program products are provided for grouping a plurality of vehicles together into a platoon and using navigation and other available data to optimize the efficiencies of operating the vehicles together as a platoon. Methods may include receiving a first trip request including a first vehicle identification, trip destination, and associated preferences; receiving a second trip request including a second vehicle identification, trip destination, and associated preferences; and generating a platooning plan including assignment of platoon leader to the first vehicle identification and a joining location where a vehicle of the first vehicle identification is to form a platoon with a vehicle of the second identification. Methods may include joining the first vehicle and the second vehicle in a secure vehicle-to-vehicle communication session.

Method, apparatus and computer program product for grouping vehicles into a platoon

Methods for personalized driving of autonomously driven vehicles include: (a) downloading at least a portion of a driving profile to a vehicle, wherein the vehicle is configured for autonomous driving and wherein the driving profile is associated with a specific driver; and (b) executing one or a plurality of operating instructions prescribed by the driving profile, such that the vehicle is operable in a driving style imitative of the specific driver. Apparatuses for personalized driving of autonomously driven vehicles are described.

Personalized driving of autonomously driven vehicles

Probabilistic road system reporting may involve determining a probability that a section of road is congested, calculating the congestion levels for sections of road having a high probability of congestion, and providing calculated congestion levels. The high probability congestion road sections may also be subject to more frequent congestion level calculation and updating than road sections having lesser probabilities of congestion.

Probabilistic road system reporting

An autonomous vehicle may be operable in an autonomous mode and a manual mode. A confidence threshold is accessed from a database. The confidence threshold may be associated with a particular geographic area containing the autonomous vehicle. The confidence threshold may be constant for the geographic area accessible by the autonomous vehicle. A computing device calculates a vehicle confidence level based on at least one confidence factor and compares the confidence threshold to the vehicle confidence level. The computing device generates a driving mode command for a vehicle based on the comparison. In one example, the driving mode command transitions the autonomous vehicle to the autonomous mode, if applicable, when the vehicle confidence score exceeds the confidence threshold. In one example, the driving mode command transitions the autonomous vehicle to the manual mode, if applicable, when the vehicle confidence score does not exceed the confidence threshold.

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Northbrook, IL, United States of America

Vladimir Boroditsky