A computer-implemented method and system for usage-controlled service delivery to an onboard system of a vehicle are designed for delivering updatable services to the onboard system of the vehicle by way of a vehicle-external server according to data of the service usage behavior of a vehicle user of the updatable services.

Systems and methods are provided for finding an available pickup/drop-off zone (PDZ) for an autonomous vehicle (AV) to use to pick up a passenger. A PDZ is selected that is likely to be available and that is within a reasonable walking distance of a passenger. The AV and the passenger are guided to the available PDZ. In selecting the available PDZ, the system balances the human and vehicle routing by taking into account the distance possible PDZs are from the passenger, the likelihood the respective PDZs will be available, the passenger's desire/ability to walk to the respective PDZs (e.g., due to physical limitations, weather, etc.), the driving time of the AV to the respective PDZs, the walking time of the passenger to the respective PDZs, and the like.

Disclosed are high-precision-map data collection methods, devices and storage mediums, which relate to the field of computer technologies, and particularly to the fields of big data technologies, autonomous driving technologies, intelligent transportation technologies, or the like. The data collection method includes: receiving a collection task generated by a server, where the collection task includes to-be-collected track points; generating a navigation path based on the to-be-collected track points, and displaying the navigation path; and acquiring map data collected at the to-be-collected track points when a vehicle travels based on the navigation path.

A navigation system includes: a control circuit configured to: generate a video clip by parsing an interval of a sensor data stream for a region of travel; analyze the video clip submitted to a deep learning model, already trained, including identifying a traffic flow estimate; access a position coordinate for calculating a distance to intersection; generate a traffic flow state by fusing a corrected speed, the traffic flow estimate, and the distance to intersection; merge a vehicle maneuvering instruction into the traffic flow state for maneuvering through the region of travel; and a communication circuit, coupled to the control circuit, configured to: communicate the traffic flow state for displaying on a device.

A navigation system includes: a traffic condition calculation unit (222) that calculates an evaluation value indicating a traffic condition of each road segment included in a target area; a guidance route determination unit (223) that determines a route in which a degree of non-congestion based on the traffic condition evaluation value of a road segment included in a route to a destination of each vehicle is the highest as a guidance route on the basis of the traffic condition evaluation value calculated by the traffic condition calculation unit and the destination of each vehicle; and a guidance route providing unit (224) that provides the guidance routes determined by the guidance route determination unit to a driver of the corresponding vehicle.

One or more processors identify an occupant of a passenger vehicle, and then receive biometric sensor readings from a biometric sensor that is monitoring the occupant in real time, where the biometric sensor readings indicate a real-time emotional state of the occupant. The processor(s) generate a personal profile for the occupant of the passenger vehicle based on the biometric sensor readings. The processor(s) receive a desired destination and travel schedule for the occupant of the passenger vehicle, as well as environmental sensor readings indicating a real-time environmental state of the passenger vehicle. The processor(s) then create a travel route for the passenger vehicle based on the biometric sensor readings, the personal profile of the vehicle occupant, the desired destination and travel schedule, and the real-time environmental state of the passenger vehicle. One or more processors then transmit, to the passenger vehicle, directions for the travel route.

System and methods for using probes to detect and elicit dangerous driving conditions. Historical probe data is aggregated and analyzed to identify reoccurring dangerous driving conditions down to the lane level. The lane level locations with high recurring dangerous driving condition metrics are identified and analyzed by a mapping system. The mapping system uses machine learning and other techniques to investigate, validate, and verify the dangerous driving conditions to ascertain the cause and then categorize the dangerous driving condition.

In some implementations, a mobile device transmits traffic information to a server for analysis. The traffic information includes movement information including detected stops and durations of detected stops. The traffic information is analyzed to detect traffic patterns that indicate locations of stop signs and/or stop lights. The traffic information is analyzed to determine durations of stops at stop signs and/or stop lights. The durations of stops is associated with a time of day and/or day of the week. In some implementations, navigational routes is determined based stop sign and/or stop light information, including the delays attributable to detected stop signs and/or stop lights.

A computer-implemented method of updating an auto insurance policy is provided. The method may include (1) determining that a customer's mobile device has a Telematics Application (“App”) installed on it, the Telematics App configured to (i) receive telematics data associated with another vehicle via a wireless communication broadcast; (ii) determine a travel event from analysis of the telematics data received, and (iii) generate a corrective action based upon the telematics data received or travel event determined that alleviates the risk of vehicle collision. The method may also include (2) monitoring, with the customer's permission, an amount or percentage of usage of the Telematics App on the customer's mobile device while the customer is driving in an insured vehicle; and (3) adjusting an insurance policy premium or discount based upon usage of the Telematics App to facilitate rewarding risk-averse drivers and encourage usage of risk mitigation or prevention technology.

An information processing apparatus of this application includes a controller. The controller is configured to execute: identifying a behavior pattern of a user using a behavior model of the user, the behavior model being generated based on past behavior information of the user detected by a sensor capable of detecting the behavior information of the user, predicting a scheduled arrival time, which is an arrival time of the user at a building to which the user travels, based on the identified behavior pattern of the user, and providing information to the user regarding the movement to the building so that the user can arrive at the building by the predicted scheduled arrival time.