A computer implemented method for object detection includes the following steps carried out by computer hardware components: acquiring an image; determining a pixel of the image as a base pixel; determining coordinates of a plurality of sets of target pixels, each set of target pixels including a plurality of pixels in a respective pre-determined relationship to the base pixel; for each of the sets of target pixels, determining information representing values of the pixels in the respective set of target pixels; and determining whether a pre-determined object is shown in the image based on the determined information.

A signal color determination device includes a processor configured to acquire a captured image. If a part of a plurality of signal lamps of a traffic signal is detected from the captured image, the processor determines a signal color of the traffic signal.

A vehicle traffic light information display apparatus includes a communication device that receives at least one of a vehicle surrounding image, vehicle location information, lane driving direction information, turn indicator status information, a navigation guidance route, or any combination thereof, a driving direction determiner that determines a driving direction of a vehicle based on at least one of the vehicle surrounding image, the vehicle location information, the lane driving direction information, the turn indicator status information, or any combination thereof, and a traffic light indication information generator that recognizes a traffic light around the vehicle based on the vehicle surrounding image and generates traffic light indication information based on at least one of the recognized traffic light, the driving direction, the navigation guidance route, or any combination thereof.

An information providing device includes an external environment recognition unit that recognizes an external environmental situation of a movable body, an information providing control unit that provides a notification of recommended stopping information based on a current indication of traffic signals recognized by the external environment recognition unit, and a determination unit which determines, in the case that a first intersection and a second intersection are positioned in a travel direction of the movable body, whether or not a degree of proximity of the first intersection and the second intersection satisfies a predetermined condition. In the case it is determined by the determination unit that the predetermined condition is satisfied, the information providing control unit prevents the recommended stopping information based on a current indication of the traffic signal belonging to the second intersection from being provided.

A method for providing driving assistance based on a HD map includes acquiring information such as position of a vehicle and timing, and acquiring a speed of the vehicle based on the manner of driving, and acquiring distance an intersection in front of the vehicle and information from a HD map such as traffic light intersection ahead. Deciding such as whether the vehicle can pass through the intersection based on the speed of the vehicle, intersection distance, and the traffic light information and prompting and/or controlling the vehicle if it is determined that passage through is not possible. A vehicle-mounted apparatus applying the method is also disclosed.

A vehicle detecting device includes a region setting unit that sets a plurality of regions of interest having different ranges on image data acquired from an image capturing device that captures an image of a space in front of a host vehicle; and a vehicle determining unit that determines, for each of the regions of interest, presence of a front vehicle based on a luminous point present in the region and that executes a determination at different frequencies in the respective regions of interest.

A list of images is received. The images were captured by a sensor of an ADV chronologically while driving through a driving environment. A first image of the images is identified that includes a first object in a first dimension (e.g., larger size) detected by an object detector using an object detection algorithm. In response to the detection of the first object, the images in the list are traversed backwardly in time from the first image to identify a second image that includes a second object in a second dimension (e.g., smaller size) based on a moving trail of the ADV represented by the list of images. The second object is then labeled or annotated in the second image equivalent to the first object in the first image. The list of images having the labeled second image can be utilized for subsequent object detection during autonomous driving.

A method is disclosed for evaluating a classifier used to determine a traffic light signal state in images. The method includes, by a computer vision system of a vehicle, receiving at least one image of a traffic signal device of an imminent intersection. The traffic signal device includes a traffic signal face including one or more traffic signal elements. The method includes classifying, by a traffic light classifier (TLC), a classification state of the traffic signal face using labeled images correlated to the received at least one image. The classification state controls an operation of the vehicle at the intersection. The method includes evaluating a performance of the classifying of the classification state generated by the TLC. The evaluation is a label-free performance evaluation based on unlabeled images. The method includes training the TLC based on the evaluated performance.

A method for traffic light auto-labeling includes aggregating vehicle-to-infrastructure (V2I) traffic light signals at an intersection to determine transition states of each driving lane at the intersection during operation of an ego vehicle. The method also includes automatically labeling image training data to form auto-labeled image training data for a traffic light recognition model within the ego vehicle according to the determined transition states of each driving lane at the intersection. The method further includes planning a trajectory of the ego vehicle to comply with a right-of-way according to the determined transition states of each driving lane at the intersection according to a trained traffic light detection model. A federated learning module may train the traffic light recognition model using the auto-labeled image training data during the operation of the ego vehicle.

Provided are a method, a device, and a computer program for providing a driving guide by using vehicle position information and signal light information. A method for providing a driving guide by using vehicle position information and signal light information according to various embodiments of the present invention is a method performed by a computing device. The method may comprise the steps of: collecting image data obtained by photographing a space where a vehicle is currently driving; determining a current driving lane of the vehicle by using the image data and vehicle position information; and providing a driving guide for the vehicle by using the current driving lane of the vehicle.