Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.

Provided are methods, systems, devices, and tangible non-transitory computer readable media for providing data including vehicle map service data. The disclosed technology can perform operations including receiving vehicle map service data from a plurality of service systems that include a plurality of client systems associated with a vehicle. The vehicle map service data can include information associated with a geographic area. A local map of the geographic area within a predetermined distance of the vehicle can be generated based on the vehicle map service data. Portions of the local map to which each client system is subscribed can be determined for each client system of the plurality of client systems. Additionally, the portions of the local map to which each client system is subscribed can be sent to a respective client system of the plurality of client systems.

A computer-implemented method and system for map matching Global Navigation Satellite System (GNSS) positions of a vehicle with location information of a digital map, including map matching the determined distance between two respective detected consecutive GNSS positions of the vehicle with a route length between the two respective consecutive GNSS positions of the vehicle on the digital map.

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.

An approach is provided for generating/breaking vehicle paths on a limited graph area. The approach, for example, determining an expected frequency of location data collected from a sensor of a vehicle traveling on a roadway, wherein the location data include a plurality of probe points that are time-sequenced. The approach also involves detecting an exit or an entry of the vehicle on the roadway based on comparing the expected frequency to an observed frequency of the location data on at least one portion of the roadway. The approach further involves initiating an identification, a creation, a breaking, or a combination thereof of a path constructed from the location data based on the detecting of the exit or the entry of the vehicle. The approach further involves providing the path as an output.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for planning a trajectory of a vehicle. One of the methods includes obtaining input data for planning a driving trajectory for a vehicle, the input data comprising an intended route for the vehicle and data characterizing an environment in a vicinity of the vehicle; processing the input data using an input encoder neural network to generate feature data that includes a respective feature representation for each of a plurality of locations in the environment; applying spatial attention to the feature representations to generate a respective attention weight for each of the plurality of locations; generating a respective attended feature representation for each of the plurality of locations; generating a bottlenecked representation of the attended feature representations; and generating a planned future trajectory from at least the bottlenecked representation.

A plurality of fault conditions are detected on a communication network onboard a vehicle. The detected fault conditions, a fault condition importance, environment conditions, and a vehicle operation mode are input to a neural network that outputs rankings for respective detected fault conditions. The neural network is trained by determining a loss function based on a maximum likelihood principle that determines a probability distribution that ranks the detected fault conditions. The vehicle is operated based on the rankings of the fault conditions.

An electronic device may include: a communication module comprising communication circuitry, a processor operatively coupled to the communication module, and a memory storing instructions operatively coupled to the processor. The instructions, when executed, may cause the processor to: obtain driving route traffic information of a first driving route of a vehicle connected to the electronic device, analyze the first driving route traffic information and determine a traffic event occurrence requiring a change in a driving route or a driving lane of the vehicle connected to the electronic device, based on the traffic event occurring, determine a severity level of the traffic event based on a driving time increase and an accident risk, and control a display device to display at least one of line change information, lane change information and a traffic sign corresponding to the severity level on a display device.

The present invention discloses a unique system for travel contemplation. The present invention particularly discloses a travel contemplating system with consolidating features of planning and navigating for site visits and accommodation at the same time simultaneously. In addition, the aforementioned system also provides integrated facilities of accessing all sorts of multimedia for entertainment while travelling.