An active sensor for performing active measurements of a scene is presented. The active sensor includes at least one transmitter configured to emit light pulses toward at least one target object in the scene, wherein the at least one target object is recognized in an image acquired by a passive sensor; at least one receiver configured to detect light pulses reflected from the at least one target object; a controller configured to control an energy level, a direction, and a timing of each light pulse emitted by the transmitter, wherein the controller is further configured to control at least the direction for detecting each of the reflected light pulses; and a distance measurement circuit configured to measure a distance to each of the at least one target object based on the emitted light pulses and the detected light pulses.

An active sensor for performing active measurements of a scene is presented. The active sensor includes at least one transmitter configured to emit light pulses toward at least one target object in the scene, wherein the at least one target object is recognized in an image acquired by a passive sensor; at least one receiver configured to detect light pulses reflected from the at least one target object; a controller configured to control an energy level, a direction, and a timing of each light pulse emitted by the transmitter, wherein the controller is further configured to control at least the direction for detecting each of the reflected light pulses; and a distance measurement circuit configured to measure a distance to each of the at least one target object based on the emitted light pulses and the detected light pulses.

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to train a neural network included in a memory augmented neural network based on one or more images and corresponding ground truth in a training dataset by transforming the one or more images to generate a plurality of one-hundred or more variations of the one or more images including variations in the ground truth and process the variations of the one or more images and store feature points corresponding to each variation of the one or more images in memory associated with the memory augmented neural network. The instructions can include further instructions to process an image acquired by a vehicle sensor with the memory augmented neural network, including comparing a feature variance set for the image acquired by the vehicle sensor to the stored processing parameters for each variation of the one or more images, to obtain an output result.

A method for depth estimation performed by a depth estimation system of an autonomous agent includes determining a first pose of a sensor based on a first image captured by the sensor and a second image captured by the sensor. The method also includes determining a first depth of the first image and a second depth of the second image. The method further includes generating a warped depth image based on at least the first depth and the first pose. The method still further includes determining a second pose based on the warped depth image and the second depth image. The method also includes updating the first pose based on the second pose and updating a first warped image based on the updated first pose.

In some implementations, a device may receive video data associated with video frames that depict an environment of a vehicle. The device may identify an object depicted in the video frames, wherein an object detection model indicates bounding boxes associated with the object. The device may determine, based on the bounding boxes, a configuration of the bounding boxes within the video frames. The device may determine, based on the configuration of the bounding boxes, that the object is a vulnerable road user (VRU) that is in the environment. The device may determine a trajectory of the VRU based on a change in the configuration between video frames of a set of the video frames. The device may determine, based on the trajectory, a probability of a collision between the VRU and the vehicle. The device may perform, based on the probability, an action associated with the vehicle.

A method of tracking an object of interest between temporally successive images taken from a vehicle based camera system comprising: a) from an initial image, determining an initial patch with a respective boundary box encapsulating an identified object of interest; b) using a search template in Kanade Lucas Tomasi (KLT) methodology to track said object of interest in a temporally successive image from said camera system; so as to determine therein a new patch having a respective new boundary box or portion thereof, with respect to said object of interest, characterized in; and c) performing a check on the robustness of the tracking step in step b) by analyzing one or more parameters output from step b).

An interactive vehicle safety system having capabilities to improve peripheral vision, provide warning, and improve reaction time for operators of vehicles. For example, the interactive vehicle safety system may have capabilities for portraying objects, which are being blocked by any of the structural pillars and/or mirrors of a vehicle (such as a truck, van, train, etc.). The interactive vehicle safety system disclosed may comprise one or more image capturing devices (such as camera, sensor, laser), distance and object sensors (such as ultrasonic sensor, LIDAR radar sensor, photoelectric sensor, and infrared sensor), a real-time image processing of an object, and one or more display systems (such as LCD or LED displays). The interactive vehicle safety system may give a seamless 360-degree front panoramic view to a driver.

A point cloud or map of an environment is generated by determining sets of landmark points from multiple images, such as through use of a Simultaneous Localization and Mapping (SLAM) algorithm. Images acquired using a depth camera are used to determine depth points indicative of at least one object not represented by the landmark points. A combined map is generated to include both the landmark points and the depth points. The set of depth points is mapped to a corresponding set of landmark points based on proximity of the points, similarity of the camera poses, or times that the images were acquired. The relationship between the depth and landmark points may be determined. When the landmark points are moved, such as to account for error in the SLAM algorithm, the depth points may be moved to a modified location relative to the landmark points so that the relationship remains constant.

Provided is a mobile object control device comprising a storage medium storing computer-readable commands and a processor connected to the storage medium, the processor executing the computer-readable commands to: acquire a subject bird's eye view image obtained by converting an image, which is photographed by a camera mounted in a mobile object to capture a surrounding situation of the mobile object, into a bird's eye view coordinate system; input the subject bird's eye view image into a trained model, which is trained to receive input of a bird's eye view image to output at least a three-dimensional object in the bird's eye view image, to detect a three-dimensional object in the subject bird's eye view image; detect a travelable space of the mobile object based on the detected three-dimensional object; and cause the mobile object to travel so as to pass through the travelable space.

Aspects of the present disclosure relate generally to limiting the use of an autonomous or semi-autonomous vehicle by particular occupants based on permission data. More specifically, permission data may include destinations, routes, and/or other information that is predefined or set by a third party. The vehicle may then access the permission data in order to transport the particular occupant to the predefined destination, for example, without deviation from the predefined route. The vehicle may drop the particular occupant off at the destination and may wait until the passenger is ready to move to another predefined destination. The permission data may be used to limit the ability of the particular occupant to change the route of the vehicle completely or by some maximum deviation value. For example, the vehicle may be able to deviate from the route up to a particular distance from or along the route.