A braking control arrangement is for a braking system of a vehicle. The braking system comprises an anti-lock braking system, ABS, and an electronic stability control, ESC. The braking control arrangement comprises a module that is arranged to operate with the anti-lock braking system and the electric stability control. The arrangement is also arranged to drive a gear arrangement of the vehicle in such a way that when braking the vehicle, the anti-lock braking system works, and a slippery road is detected, the gear arrangement of the vehicle is driven to shift a reverse gear.

A vehicle control device calculates a target line pressure based on an instructed torque capacity of a friction engaging element and a belt capacity when the friction engaging element is determined as not engaging. Belt capacity is calculated using an input torque of a continuously variable transmission mechanism. The device calculates torque down in a driving source based on an upper limit line pressure when the calculated target line pressure exceeds such line pressure. A limit torque capacity of the friction engaging element is calculated using the input torque and a belt capacity when the friction engaging element is determined as not engaging. The belt capacity is calculated using an actual line pressure. The device restrains a slip between pulleys and a power transmitting member using the target line pressure, the torque down, and the limit torque capacity when the friction engaging element is determined as not engaging.

One or more techniques and/or systems are provided for determining a scaled flow rate of traffic for a road segment. For example, probe flow rate information is determined based upon locational information from one or more probe vehicles on a road segment (e.g., a flow rate of probe vehicles corresponding to a sum of probe vehicles identified from time stamped global positioning system coordinates provided by the probe vehicles). Satellite imagery of the road segment is analyzed to identify a count of vehicles on the road segment. Scale factor and offset information is estimated based upon the probe flow rate information and the count of vehicles. The scale factor and offset information is used to scale the probe flow rate information to determine a scaled flow rate that may be a relatively accurate flow rate of traffic, which may correspond to an inferred traffic volume along the road segment.

Users within transit in a vehicle may initiate location queries to fulfill a set of interests, such as stops for food, fuel, and lodging. A device may fulfill the queries according to various factors, such as the distance of nearby locations to the user or to another location specified by the user, and the popularity of various locations. However, the user may not have specified or even chosen a route, and may wish to have interests fulfilled at a later time (e.g., stopping for food in 30 minutes), and a presentation of search results near the user's current location may be unhelpful. Presented herein are techniques for fulfilling location queries that involve predicting a route of the user, and identifying a timing window for the query results (e.g., locations that are likely to be near the user's projected location when the wishes to stop for food in 30 minutes).

One or more techniques and/or systems are provided for determining a scaled flow rate of traffic for a road segment. For example, probe flow rate information is determined based upon locational information from one or more probe vehicles on a road segment (e.g., a flow rate of probe vehicles corresponding to a sum of probe vehicles identified from time stamped global positioning system coordinates provided by the probe vehicles). Satellite imagery of the road segment is analyzed to identify a count of vehicles on the road segment. Scale factor and offset information is estimated based upon the probe flow rate information and the count of vehicles. The scale factor and offset information is used to scale the probe flow rate information to determine a scaled flow rate that may be a relatively accurate flow rate of traffic, which may correspond to an inferred traffic volume along the road segment.

Vehicles in transit within a location may transmit and/or receive information about transit events arising within the location, such as accidents, developing weather, and road obstructions. Because localized exchange channels, such as a radiofrequency broadcast, may be range-limited and/or unreliable, a centralized service may be provided to facilitate the exchange of notifications about transit events, but it may be difficult to provide a centralized service that is both scalable to millions of vehicles and capable of low-latency exchange of time-sensitive notifications for transit events. The techniques presented herein provide an architecture for broadcasting transit events through a transit service that maintains vehicle area groups, respectively identifying the vehicles that are associated with each location. The service may receive a notification of a transit event for a location, and may utilize the vehicle area group for the location to broadcast the notification to the other vehicles of the area group.

One or more techniques and/or systems are provided for training and/or utilizing a traffic obstruction identification model for identifying traffic obstructions based upon vehicle location point data. For example, a training dataset, comprising sample vehicle location points (e.g., global positioning system location points of vehicles) and traffic obstruction identification labels (e.g., locations of known traffic obstructions such as stop signs, crosswalks, stop lights, etc.), may be evaluated to extract a set of training features indicative of traffic flow patterns. The set of training features and the traffic obstruction identification labels may be used to train a traffic obstruction identification model to create a trained traffic obstruction identification model. The trained traffic obstruction identification model may be used to determine whether a road segment has a traffic obstruction or not.

A user driving a vehicle may be monitored by a device on behalf of a third party, such as employers and insurers. The device may generate an objective evidentiary record of the user's driving safety and/or proficiency for use by the third party. The user may wish to share the evidentiary record with other parties, but the third party that controls the record may not agree and/or release the record. A user-generated record of the user's driving behavior may be untrustworthy and/or unverifiable. Instead, a device of the user monitors the operation of the vehicle by the user, generates a driving profile of the user's driving behavior and risk rating, and cryptographically signs the driving profile. The cryptographically signed driving profile is transmitted to the user for sharing with third parties, e.g., potential employers and insurers, and the authenticity of the driving profile is verifiable using the cryptographic signature.

The environmental impact of vehicle transit through an area is often evaluated through indirect and/or aggregate metrics, such as visibility and/or health effects from smog, or the contamination of air or water quality. However, such environmental metrics may be inaccurate, incomplete, delayed, and/or insufficient to inform a user of a vehicle as to the environmental impact of the vehicle transit of his or her vehicle on the environment. Instead, a vehicle device may collect driving metrics for a vehicle, and may transmit such driving metrics to an environmental monitoring service, which may correlate such driving metrics for the vehicle with the environmental impact. A notification of environmental impact may be transmitted back to the vehicle device, which may present the environmental impact to the user, and/or may adjust an autonomous operation of the vehicle, such as a speed or route of the vehicle, in view of the environmental impact.

One or more techniques and/or systems are provided for determining a scaled flow rate of traffic for a road segment. For example, probe flow rate information is determined based upon locational information from one or more probe vehicles on a road segment (e.g., a flow rate of probe vehicles corresponding to a sum of probe vehicles identified from time stamped global positioning system coordinates provided by the probe vehicles). Satellite imagery of the road segment is analyzed to identify a count of vehicles on the road segment. Scale factor and offset information is estimated based upon the probe flow rate information and the count of vehicles. The scale factor and offset information is used to scale the probe flow rate information to determine a scaled flow rate that may be a relatively accurate flow rate of traffic, which may correspond to an inferred traffic volume along the road segment.