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.

A vehicle computer for use in a vehicle. The vehicle computer includes at least one processor; an interface to a radio communications network; and a memory including software instructions configured to control the at least one processor to implement a method including steps of: receiving, from a traffic information hub in the radio communications network, intersection condition information regarding a next intersection along a route of the vehicle; and calculating route and navigation information based at least in part on the received intersection condition information. Also disclosed is a traffic information hub including: at least one processor; an interface to a data network; and a memory including software instructions configured to control the at least one processor to implement a method including steps of: receiving intersection condition information for each one of a plurality of traffic intersections; and receiving a query from a vehicle.

An apparatus, method and computer program product are provided for predicting wrong-way-driving events. A WWD event defines an event in which a vehicle travels in a direction opposing a traffic direction designated for a portion of a road. In one example, the apparatus estimates a likelihood of portion of the road inducing a wrong-way-driving (WWD) event by using a machine learning model as a function of map data, sensor data, or a combination thereof. If the likelihood satisfies a threshold, the apparatus updates a map layer to indicate the WWD event at the portion of the road.

In one embodiment, a cognitive load driving assistant increases driving safety based on cognitive loads. In operation, the cognitive load driving assistant computes a current cognitive load of a driver based on sensor data. If the current cognitive load exceeds a threshold cognitive load, then the cognitive load driving assistant modifies the driving environment to reduce the cognitive load required to perform the primary driving task and/secondary task(s), such as texting via a cellular phone. The cognitive load driving assistant may modify the driving environment indirectly via sensory feedback to the driver or directly through reducing the complexity of the primary driving task and/or secondary tasks. In particular, if the driver is exhibiting elevated cognitive loads typically associated with distracted driving, then the cognitive load driving assistant modifies the driving environment to allow the driver to devote appropriate mental resources to the primary driving task, thereby increasing driving safety.

A vehicle is provided that comprises two or more radio frequency (RF) antennas and two or more RF transceivers to communicate wirelessly sensitive information associated with a user of the vehicle (the two or more RF antennas being at different physical locations on an exterior of the vehicle). The vehicle determines which one of the two or more RF antennas is receiving a strongest signal from a common signal source, selects a first RF transceiver associated with the RF antenna with the strongest signal to send the sensitive information associated with the user to the common signal source, and sends the sensitive information associated with the user to the first RF transceiver for transmission to the common signal source.

The disclosed technology provides solutions for facilitating the selection of a parking space by a user of a parking application. A process of the disclosed technology can include steps for monitoring sensor data and location data associated with a user device, retrieving, based on the location data associated with a user device, listing data associated with one or more parking spaces in a vicinity of the user device, and capturing image data that includes at least a portion of the one or more parking spaces. In some aspects, the process may further include steps for overlaying one or more graphical objects onto the image data, wherein the one or more graphical objects are based on the listing data associated with the one or more parking spaces. Systems and machine-readable media are also provided.

The present invention relates to a method for alerting drivers and/or autonomous vehicles of high risk scenarios. The method includes obtaining positional data of a vehicle, where the positional data is indicative of geographical position and heading of the vehicle. The method further includes obtaining environmental data of the vehicle, where the environmental data is indicative of state of the surrounding environment of the vehicle. The method includes determining, by means of trained model, accident intensity for upcoming road portion for the vehicle, the trained model being configured to determine accident intensity associated with the upcoming road portion based on the obtained environmental data and the obtained positional data. Then, if the determined accident intensity exceeds threshold, the method comprises transmitting signal indicating approaching high risk scenario to a Human-Machine-Interface, HMI, of the vehicle and/or to a control system of the vehicle.

The information processing device provides information relating to a movement of a mobile body. The receiving unit receives an information request. The load determination unit determines a load of a driver with respect to a driving of the mobile body. The voice output unit outputs, in response to the information request, a basic information when the load is large, and an additional information in addition to the basic information when the load is small.

Techniques for determining a location and type of traffic-related features and using such features in route planning are discussed herein. The techniques may include determining that sensor data represents a feature such as a traffic light, road segment, building type, and the like. The techniques further include determining a cost of a feature, whereby the cost is associated with the effect of a feature on a vehicle traversing an environment. A feature database can be updated based on features in the environment. A cost of the feature can be used to update costs of routes associated with the location of the feature.

A boarding/deboarding point providing system includes a probe vehicle group, a data server, and an on-board unit or a mobile terminal. When vehicle data transmitted from the probe vehicle group are input, the data server extracts a boarding/deboarding point suitable for boarding a vehicle or deboarding a vehicle from the vehicle data, and stores the point while updating the boarding/deboarding point information database. When a request for boarding/deboarding point information is received from the on-board unit or the mobile terminal, the boarding/deboarding point information stored in the boarding/deboarding point information database is searched for a boarding/deboarding point that meets the request conditions, and the search result that includes the boarding/deboarding point is transmitted to the on-board unit or the mobile terminal.