A system for determining and executing an autonomous-vehicle vehicle travel route, including a hardware-based processing unit and a non-transitory computer-readable storage medium. The storage medium includes an input-interface module that, when executed by the hardware-based processing unit, obtains factor data indicating factors relevant to determining a vehicle travel route. The storage medium also includes a route-generation module comprising a route-complexity sub-module. The route-complexity sub-module determines, based on the factor data, route-complexity indexes corresponding to respective optional routes. The route-generation module determines the vehicle travel route based on the route-complexity indexes. The storage in various embodiments includes other sub-modules associated with other elements, such as autonomous-driving safety, comfort, stress, pollution, scenery, or infrastructure-accessibility, for determining and executing an autonomous-driving travel route. In some embodiments, the storage includes an autonomous-driving perceptions module and an autonomous-driving control module for modifying vehicle functions in executing the autonomous-driving travel route.

Disclosed is a location tracking system and associated methods for precisely locating a target device with a recipient device via different forms of location tracking and augmented reality. The recipient device receives a first position of the target device over a data network. The recipient device is moved according to the first position until the target device is in Ultra-WideBand (“UWB”) signaling range of the recipient device. The recipient device then measures a distance and direction of the target device relative to the recipient device based on Time-of-Flight (“ToF”) measurements generated from the UWB signaling. The recipient device determines a second position of the target device based on the distance and direction of the target device, and generates an augmented reality view with a visual reference at a particular position in images of a captured scene that corresponds to the second position of the target device.

An information providing device includes an acquirer configured to acquire one or more pieces of orientation information indicating orientations of one or more users, a determiner configured to determine priority item information, which is item information to be preferentially provided, based on reference information in which a specific point and item information for each item of the specific point are associated with each other, and the one or more pieces of orientation information acquired by the acquirer, and a provider configured to allow an information output device associated with at least a part of the one or more users to output the priority item information.

A racing coach device stores a first path of travel along a racetrack over a first time period and a second path of travel along the racetrack over a second time period. The racing coach device identifies, for each of a plurality of geolocations along the racetrack, one of the first path of travel or the second path of travel that is associated with a shorter duration of time over which the user traversed a segment of the path of travel associated with each of the plurality of geolocations. The device determines an optimal path of travel along the racetrack based on the identified first and second path of travel for each segment of the path of travel at each of the plurality of geolocations that results in a calculated lap time to traverse the racetrack that is less than the first time period and the second time period.

A system for navigating a vehicle automatically from a current location to a destination location without a human operator is provided. The system of the vehicle includes a global positioning system (GPS) for identifying a vehicle location and a communications system for communicating with a server of a cloud system. The server is configured to identify that the vehicle location is near or at a parking location. The communications system is configured to receive mapping data for the parking location from the server, and the mapping data is at least in part used to find a path at the parking location to avoid a collision of the vehicle with at least one physical object when the vehicle is automatically moved at the parking location. The mapping data is processed by electronics of the vehicle so that when the vehicle is automatically moved collision with the at least one physical object is avoided and the electronics of the vehicle is configured to process a combination of sensor data obtained by sensors of the vehicle. The processing of the sensor data uses image data obtained from one or more cameras and light data obtained from one or more optical sensors.

Aspects of the disclosure provide for controlling an autonomous vehicle using no block costs in space and time. For instance, a trajectory for the autonomous vehicle to traverse in order to follow a route to a destination may be generated. A set of no-block zones through which the trajectory traverses may be identified. A no-block zone may be region where the autonomous vehicle should not stop but can drive through in an autonomous driving mode. For each given no-block zone of the set, a penetration cost that increases towards a center of the no-block zone and decreases towards edges of the no-block zone may be determined. Whether the autonomous vehicle should follow the trajectory may be determined based on the penetration cost. An autonomous vehicle may be controlled in the autonomous driving mode according to the trajectory based on the determination of whether the autonomous vehicle should follow the trajectory.

Methods and apparatus provide physiological movement detection, such as gesture, breathing, cardiac and/or gross motion, such as with sound, radio frequency and/or infrared generation, by electronic devices such as vehicular processing devices. The electronic device in a vehicle may, for example, be any of an audio entertainment system, a vehicle navigation system, and a semi-autonomous or autonomous vehicle operations control system. One or more processors of the device, may detect physiological movement by controlling producing sensing signal(s) in a cabin of a vehicle housing the electronic device. The processor(s) control sensing, with a sensor, reflected signal(s) from the cabin. The processor(s) derive a physiological movement signal with the sensing signal and reflected signal and generate an output based on an evaluation of the derived physiological movement signal. The output may control operations or provide an input to any of the entertainment system, navigation system, and vehicle operations control system.

A vehicle traveling control system according to the example in the present disclosure communicates with an automatic operation control system which drafts a traveling plan of the vehicle, and performs an automatic traveling control for automatically running the vehicle along the traveling plan received from the automatic operation control system. The vehicle traveling control system predicts a risk based on information about surrounding environment of the vehicle, and performs, when the risk is predicted, a risk avoidance control to intervene in the automatic traveling control in order to avoid the risk. When the risk avoidance control is executed, the vehicle traveling control system transmits information on the risk avoidance control to the automatic operation control system.

Techniques to predict object behavior in an environment are discussed herein. For example, such techniques may include determining a trajectory of the object, determining an intent of the trajectory, and sending the trajectory and the intent to a vehicle computing system to control an autonomous vehicle. The vehicle computing system may implement a machine learned model to process data such as sensor data and map data. The machine learned model can associate different intentions of an object in an environment with different trajectories. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on object's intentions and trajectories.

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.