A parking enforcement system includes an autonomous vehicle that is equipped with an image capture device that is configured to capture images of license plates of parked vehicles while the autonomous vehicle moves through a parking zone. The system will process the images to extract license plate numbers from the images. The system will correlate the license plate numbers with data in a parking enforcement database to determine whether the license plate numbers are associated with an unexpired parking transaction. For any license plate number that is not subject to an unexpired parking transaction, the system may initiate an enforcement action. For any license plate number that is subject to an unexpired parking transaction, the system may not initiate an enforcement action.

The present invention enables recognition of traffic light lamp color in place of or in addition to simply displaying the traffic light lamp color in a color image. A projection unit projects, towards a part subject to projection, display light L that can display an image at a variable display distance. A lamp color information acquisition unit acquires a lamp color of a traffic light which a vehicle will be driving through. A display distance adjustment unit changes the display distance of the image based on the lamp color acquired by an information acquisition unit.

The technology relates to detecting and responding to processions. For instance, sensor data identifying two or more objects in an environment of a vehicle may be received. The two or more objects may be determined to be disobeying a predetermined rule in a same way. Based on the determination that the two or more objects are disobeying a predetermined rule, that the two or more objects are involved in a procession may be determined. The vehicle may then be controlled autonomously in order to respond to the procession based on the determination that the two or more objects are involved in a procession.

An apparatus, method, and vehicle for providing a braking level of a forward vehicle may quantify a degree of braking of the forward vehicle into a braking level and provide the braking level of the forward vehicle to a driver. The apparatus includes a brake lamp position recognizing device configured to recognize positions of brake lamps of the forward vehicle based on an image and a relative acceleration of the forward vehicle, a braking determining device configured to determine whether or not braking of the forward vehicle is performed based on a brake lamp image extracted from the image of the forward vehicle, a braking level determining device configured to determine a braking level of the forward vehicle based on the relative acceleration of the forward vehicle, and a braking level image providing device configured to provide the determined braking level of the forward vehicle through an image.

A method for controlling a vehicle including: based on map information including information of an installation position of a traffic light and information of a lane controlled by the traffic light and a range of the angle of view of a camera mounted on the own vehicle, calculating an imaging-enabled area in which an image of the traffic light can be captured on the lane by the camera; determining whether or not the own vehicle is positioned in the imaging-enabled area; and when the own vehicle is positioned in the imaging-enabled area, controlling the own vehicle in such a way that the traffic light is not shielded from the range of the angle of view of the camera by a preceding vehicle of the own vehicle.

A method comprising a data processing device predicting a time for a future state change of a first traffic light; a method comprising a data processing device generating a map of traffic lights, wherein the map of traffic lights comprises a location of at least one localization traffic light, and methods combining these methods, and to corresponding systems.

Systems, methods, and devices are disclosed for predicting behaviors of objects (vehicles, bicycles, pedestrians, etc.) at a location. A model descriptive of a possible object behavior can be received by an autonomous vehicle, where the model provides conditional predictions about a future behavior of an object based on a position of the object in a lane. The autonomous vehicle can detect the position of a specific object in the lane, and the model can then be applied to determine probabilities of a future behavior of the specific object.

Described herein are methods of detecting and labeling features within an image of an environment. Methods may include: receiving sensor data from an image sensor, where the sensor data is representative of a first image including an aerial view of a geographic region; detecting, using a perception module, at least one vehicle within the image of the geographic region; identifying an area around the at least one vehicle as a road segment in response to detecting the at least one vehicle; based on the identification of the area around the vehicle as a road segment, identifying features within the area as road features based on a context of the area; generating a map update for the road features of the road segment; and causing a map database to be updated with the road features of the road segment.

Among other things, we describe techniques for traffic light estimation using range sensors. A planning circuit of a vehicle traveling on a first drivable region that forms an intersection with a second drivable region receives information sensed by a range sensor of the vehicle. The information represents a movement state of an object through the intersection. A traffic signal at the intersection controls movement of objects through the intersection. The planning circuit determines a state of the traffic signal at the intersection based, in part, on the received information. A control circuit controls an operation of the vehicle based, in part, on the state of the traffic signal at the intersection.

A deep neural network(s) (DNN) may be used to detect objects from sensor data of a three dimensional (3D) environment. For example, a multi-view perception DNN may include multiple constituent DNNs or stages chained together that sequentially process different views of the 3D environment. An example DNN may include a first stage that performs class segmentation in a first view (e.g., perspective view) and a second stage that performs class segmentation and/or regresses instance geometry in a second view (e.g., top-down). The DNN outputs may be processed to generate 2D and/or 3D bounding boxes and class labels for detected objects in the 3D environment. As such, the techniques described herein may be used to detect and classify animate objects and/or parts of an environment, and these detections and classifications may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.