The invention relates to a method and a device for fusion of measurements from various information sources (I 1, I 2, . . . , I m) in conjunction with filtering of a filter vector, wherein the information sources (I 1, I 2, . . . , I m) comprise one or more environment detection sensor(s) of an ego vehicle, wherein in each case at least one measured quantity derived from the measurements is contained in the filter vector, wherein the measurements from at least one individual information source (I 1; I 2; . . . , I m) are mapped nonlinearly to the respective measured quantity, wherein at least one of these mapping operations depends on at least one indeterminate parameter, wherein the value to be determined of the at least one indeterminate parameter is estimated from the measurements of the different information sources (I 1, I 2, . . . , I m) and wherein the filter vector is not needed for estimating the at least one indeterminate parameter.

A system, a method, and a computer program product may be provided for generating traffic congestion estimation data of one or more lanes in a region. A system may include a memory configured to store computer program code and a processor configured to execute the computer program code to obtain image data associated with the region. The processor may be configured to determine a count of one or more first movable objects in one or lanes, based on image data, calculate a lane object static capacity of the one or more lanes, based on one or more map free flow or speed limit attributes associated with the one or more lanes and generate the traffic congestion estimation data based on count of first movable objects in the one or more lanes, the moving speed of movable objects crossing multiple image frames, the lane object static capacity of lanes.

A system and method for locating a traffic light at a host vehicle. A probe vehicle obtains image data on an intersection that includes the traffic light when the probe vehicle is at the intersection and identifies the traffic light in the intersection from the data. A remote processor creates an observed node in a digital map corresponding to the traffic light and updates a mapped position of a mapped node within the digital map based on an observed position of the observed node. The host vehicle uses the mapped node of the digital map to locate the traffic light when the host vehicle is at the intersection.

Systems and methods are provided for vehicle navigation. In one implementation, a system for navigating a vehicle may include at least one processor configured to receive a first image frame; detect in the first image frame a representation of a traffic light and determine a color state associated with lamps included on the traffic light. The at least one processor may receive an additional image frame includes a representation of the at least one traffic light; and determine, based on a comparison of the first image frame and the additional image frame, whether the at least one traffic light includes a blinking lamp. If the at least one traffic light includes a blinking lamp, the processor may cause the vehicle to implement a navigational action relative the traffic light in accordance with the determination and also based on a detected color state for the blinking lamp.

Provided is a display control apparatus that generates a display overhead image including overhead images of each of a vacant parking space and a vehicle and outputs the display overhead image to a display apparatus. The display overhead image includes at least one of an overhead image of the vacant parking space outside of a current viewing angle of a vehicle-mounted camera and an overhead image of the vacant parking space hidden behind a static object as seen from a current position of the vehicle.

A controlling and warning system based on traffic conditions feedback and a method thereof are disclosed. In the controlling and warning system, a camera disposed on a rear of a vehicle body is configured to generate and transmit a rear video to a controlling host, and the controlling host identifies a vehicle object in the rear video and calculates a separation distance between the vehicle object and the vehicle body; the vehicle object is inputted into a large-sized vehicle recognition model which is built based on artificial intelligence neural network and trained completely, to recognize whether the vehicle object is a large-sized vehicle; when the vehicle object is recognized as the large-sized vehicle and the separation distance reaches to a safe distance, a warning signal is generated. Therefore, the technical effect of improving the warning immediacy of an approaching large vehicle can be achieved.

Systems and methods for guiding a vehicle occupant's attention are disclosed herein. One embodiment selects automatically a particular region of an environment external to a vehicle and guides an occupant of the vehicle to look at the particular region by automatically adjusting the transparency of one or more windows of the vehicle such that a first portion of the one or more windows through which the occupant is able to see the particular region is more transparent than a second portion of the one or more windows that is adjacent to the first portion.

A control device includes a control unit that acquires an image taken by a vehicle provided with a power supply function for a user, identifies an electric light that is turned off and that is included in the acquired image, refers to definition data that defines whether the identified electric light should be turned on during a time zone when the image was taken to determine whether a power outage has occurred.

An autonomous vehicle control system is provided. The control system may include a plurality of cameras to acquire a plurality of images of an area in a vicinity of a vehicle; and at least one processing device configured to: recognize a curve to be navigated based on map data and vehicle position information; determine an initial target velocity for the vehicle based on at least one characteristic of the curve as reflected in the map data; adjust a velocity of the vehicle to the initial target velocity; determine, based on the plurality of images, observed characteristics of the curve; determine an updated target velocity based on the observed characteristics of the curve; and adjust the velocity of the vehicle to the updated target velocity.

Embodiments relate to methods for efficiently encoding sensor data captured by an autonomous vehicle and building a high definition map using the encoded sensor data. The sensor data can be LiDAR data which is expressed as multiple image representations. Image representations that include important LiDAR data undergo a lossless compression while image representations that include LiDAR data that is more error-tolerant undergo a lossy compression. Therefore, the compressed sensor data can be transmitted to an online system for building a high definition map. When building a high definition map, entities, such as road signs and road lines, are constructed such that when encoded and compressed, the high definition map consumes less storage space. The positions of entities are expressed in relation to a reference centerline in the high definition map. Therefore, each position of an entity can be expressed in fewer numerical digits in comparison to conventional methods.