Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

A system for displaying information indicative of driving conditions, to a driver, using a smart ring are disclosed. An exemplary system includes a smart ring with a ring band having a plurality of surfaces including an inner surface, an outer surface, a first side surface, and a second side surface. The system further includes a processor, configured to obtain data from a communication module within the ring band, or from one or more sensors disposed within the ring band. The obtained data are representative of information indicative of one or more driving conditions to be displayed to the driver. The smart ring also includes an electronic ink (e-ink) display disposed on at least one of the plurality of surfaces, and configured to present information indicative of the one or more driving conditions.

Techniques for detecting and responding to an emergency vehicle are discussed. A vehicle computing system may determine that an emergency vehicle based on sensor data, such as audio and visual data. In some examples, the vehicle computing system may determine aggregate actions of objects (e.g., other vehicles yielding) proximate the vehicle based on the sensor data. In such examples, a determination that the emergency vehicle is operating may be based on the actions of the objects. The vehicle computing system may, in turn, identify a location to move out of a path of the emergency vehicle (e.g., yield) and may control the vehicle to the location. The vehicle computing system may determine that the emergency vehicle is no longer relevant to the vehicle and may control the vehicle along a route to a destination. Determining to yield and/or returning to a mission may be confirmed by a remote operator.

Technologies for managing interoperable high definition (HD) maps for autonomous vehicles includes a HD map server to distribute interoperable HD map tiles to various autonomous vehicles, each of which may be configured to utilize proprietary HD map tiles of different propriety formats. As such, each of the autonomous vehicles is configured to translate the interoperable HD map tile to the proprietary format used by that particular vehicle. Additionally, each autonomous vehicle may submit crowd-sourced sensor data to the interoperable HD map server using a universal data set by converting the sensor data to a universal data structure.

Methods, systems, and products for resolving level ambiguity for radar systems of autonomous vehicles may include detecting a plurality of objects with a radar system. Each first detected object may be associated with an existing tracked object based on a first position thereof. First tracked object data based on a first height determined for each first detected object may be stored. The first height may be based on the position of the detected object, the existing tracked object, and a tile map. Second tracked object data based on a second height determined for each second detected object not associated with the existing tracked object(s) may be stored. The second height may be based on a position of each second detected object, a vector map, and the tile map. A command to cause the autonomous vehicle to perform at least one autonomous driving operation may be issued.

A system and method for controlling vehicles and for providing assistance to operated vehicles is discussed and described herein.

Traveling road information including traveling roads of another vehicle and the own vehicle is acquired on the basis of own-vehicle information and other-vehicle information received by a reception unit. Whether or not verification processing is needed for verification information accompanying the other-vehicle information received by the reception unit is determined on the basis of the traveling road information and the other-vehicle information. Thus, a message unnecessary for the own vehicle is not subjected to verification, so that the load on the vehicle communication device is reduced.

A vehicle-mounted device includes a data collecting device that collects sensor data from a vehicle sensor, a data transmitting device that transmits the sensor data to a server, a data receiving device that receives data about an outside-vehicle status, and an inside-vehicle/outside-vehicle cooperation device that, in response to receipt of the data about the outside-vehicle status, cooperates with a device outside the vehicle and controls an operation inside the vehicle. The server includes a map creating device that maintains a traffic status overview map on the basis of received sensor data, a vehicle selecting device that selects a vehicle capable of transmitting optimum sensor data for maintaining the traffic status overview map in view of a line status among vehicle-mounted devices present within a specific area, and a transmission permission/prohibition signal transmitting device that transmits an instruction of permitting transmission of sensor data to the vehicle.

Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Techniques are provided which may be implemented using various methods and/or apparatuses in a vehicle to determine location relative to a roadside unit (RSU) or other nearby point of reference. Vehicles within a pre-designated range or within broadcast distance or otherwise geographically proximate to a roadside unit, through the use of broadcast or other messages sent by the vehicles and/or the RSU may share carrier GNSS phase measurement data, wherein the shared GNSS carrier phase measurement data may be utilized to control and coordinate vehicle movements, velocity and/or position by the RSU and/or to determine location of each vehicle relative to the RSU and/or to other vehicles or determine the absolute location of each vehicle. An RSU may coordinate vehicle access to an intersection, manage vehicle speeds and coordinate or control vehicle actions such as slowing, stopping, and changing lanes or sending a vehicle to a particular location.