Systems and methods are provided for navigating a host vehicle. At least one processing device may be programmed to receive an image of an environment of the host vehicle; detect, based on analysis of the image, a pedestrian crosswalk in the image; detect a presence of a traffic light and determine whether the traffic light is relevant to the host vehicle and the pedestrian crosswalk; determine a state of the traffic light; determine, when a pedestrian appears in the image, a proximity of the pedestrian relative to the pedestrian crosswalk; determine a planned navigational action for navigating the host vehicle relative to the pedestrian crosswalk based on a driving policy, the state of the traffic light and the proximity of the pedestrian relative to the pedestrian crosswalk; and cause one or more actuator systems of the host vehicle to implement the planned navigational action.

A method of and system for processing Light Detection and Ranging (LIDAR) point cloud data. The method is executable by an electronic device, communicatively coupled to a LIDAR installed on a vehicle, the LIDAR having a plurality of lasers for capturing LIDAR point cloud data. The method includes receiving a first LIDAR point cloud data captured by the LIDAR; executing a Machine Learning Algorithm (MLA) for: analyzing a first plurality of LIDAR points of the first point cloud data in relation to a response pattern of the plurality of lasers; retrieving a grid representation data of a surrounding area of the vehicle; determining if the first plurality of LIDAR points is associated with a blind spot, the blind spot preventing a detection algorithm of the electronic device to detect presence of at least one object surrounding the vehicle conditioned on the at least one object is present.

In various examples, a deep neural network(s) (e.g., a convolutional neural network) may be trained to detect moving and stationary obstacles from RADAR data of a three dimensional (3D) space, in both highway and urban scenarios. RADAR detections may be accumulated, ego-motion-compensated, orthographically projected, and fed into a neural network(s). The neural network(s) may include a common trunk with a feature extractor and several heads that predict different outputs such as a class confidence head that predicts a confidence map and an instance regression head that predicts object instance data for detected objects. The outputs may be decoded, filtered, and/or clustered to form bounding shapes identifying the location, size, and/or orientation of detected object instances. The detected object instances may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.

Exemplary embodiments described in this disclosure are generally directed to a collaborative relationship between a vehicle and a UAV. In one exemplary implementation, a computer that is provided in the vehicle uses images captured by an imaging system in the UAV together with images captured by an imaging system in the vehicle, to modify a suspension system of the vehicle based on a nature of the terrain located below, or ahead, of the vehicle. The computer may, for example, modify a suspension system before the vehicle reaches a rock or a pothole on the ground ahead. In another exemplary implementation, the computer may generate an augmented reality image that includes a 3D model of the vehicle rendered on an image of a terrain located below, or ahead of, the vehicle. The augmented reality image may be used by a driver of the vehicle to drive the vehicle over such terrain.

A robot climbing control method is disclosed. The method obtains an RGB color image and a depth image of stairs, extracts an outline of a target object of a target step on the stairs from the RGB color image, determines relative position information of the robot and the target step according to the depth image and the outline of the target object, and controls the robot to climb the target step according to the relative position information. The embodiment of the present disclosure allows the robot to effectively adjust postures and forward directions on any size of and non-standardized stairs and avoids the deviation of the walking direction, thereby improving the effectiveness and safety of the stair climbing of the robot.

A distance measurement device includes: a light source configured to emit visible illumination light; an imaging element configured to receive reflected light of the illumination light from an object; and a signal processing circuit configured to reduce the emission of the illumination light in a predetermined period, detect a timing when the reception of the reflected light at the imaging element is reduced due to the reduction of the illumination light, and measure a distance to the object on the basis of the detected timing.

A method, apparatus, and system for generating tight two-dimensional (2D) bounding boxes for visible objects in a three-dimensional (3D) scene is disclosed. A two-dimensional (2D) segmentation image of a three-dimensional (3D) scene comprising one or more objects is generated by rendering the 3D scene with a segmentation camera. Each of the objects is rendered in a single respective different color. Next, one or more visible objects in the 3D scene are identified among the one or more objects based on the segmentation image. Next, a 2D amodal segmentation image for each of the visible objects in the 3D scene is generated separately. Each amodal segmentation image comprises only the single visible object for which it is generated. Thereafter, a 2D bounding box is generated for each of the visible objects in the 3D scene based on the amodal segmentation image for the visible object.

Provided are an inexpensive and safe moving object detection apparatus and moving object detection method that enable accurate detection of a moving object from an image sequence captured by a monocular camera at a high speed. A representative configuration of the moving object detection apparatus according to the present invention is provided with a horizon line detection unit that detects a horizon line in a frame image, an edge image generation unit that generates an edge image from a frame image, and a moving object detection unit that sets a detection box on a moving object, and the edge image generation unit extracts an edge image below a horizon line detected by the horizon line detection unit, and the moving object detection unit generates a foreground by combining the difference between the edge image below the horizon line and a background image of the edge image with the difference between a gray scale image and a background image of the gray scale image.

A method for detecting fallen cargo, the method may include receiving by a computerized system, sensed information related to driving sessions of multiple vehicles; applying a machine learning process on the sensed information to detect fallen cargo and to classify the fallen cargo to fallen cargo classes; estimating, from the sensed information, an impact of at least some of the fallen cargo classes on a behavior of at least some of the multiple vehicles; and determining, based on the impact, at least one suggested vehicle behavior as a response to a detection of at least some of the fallen cargo classes.

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.