A vehicle external environment recognition apparatus to be applied to a vehicle includes one or more processors and one or more memories configured to be coupled to the one or more processors. The one or more processors are configured to: calculate three-dimensional positions of respective blocks in a captured image; group the blocks to put any two or more of the blocks that have the three-dimensional positions differing from each other within a predetermined range in a group and thereby determine three-dimensional objects; identify each of a preceding vehicle of the vehicle and a sidewall on the basis of the determined three-dimensional objects; and track the preceding vehicle. The one or more processors are configured to determine, upon tracking the preceding vehicle, whether the preceding vehicle to track is to be hidden by the sidewall on the basis of a border line between a blind region and a viewable region.

A computer implemented method for determining an occupancy map in the vicinity of a vehicle comprises the following steps: successively acquiring sensor data of a sensor system, determining object detections based on the sensor data, overlaying the object detections in a spatial representation of the vicinity of the vehicle, defining, for an object detection of a first data acquisition process, an expectation area extending around the object detection, adjusting, if an object detection of a second data acquisition process is present within the expectation area, the position of the expectation area based on a difference between the position of the object detection of the first data acquisition process and the position of the object detection of the second data acquisition process, and removing an object detection of the expectation area from the occupancy map if no object detection can be determined in the expectation area for a predetermined number of successive data acquisition processes.

In a road obstacle detection device, a first derivation unit derives, for each of a plurality of local regions, a probability that a local region is the road, such that the probability is higher as the ratio of a road region in the local region is higher; and a second derivation unit derives a probability that a target local region is not a previously decided normal physical body, and derives a probability that a road obstacle exists at the target local region, based on the derived probability that the target local region is not the normal physical body and a probability that a peripheral local region is the road, the peripheral local region being a local region at a periphery of the target local region, the probability that the peripheral local region is the road being derived by the first derivation unit.

Methods, systems, and devices for wireless communications are described. An example method for object detection is provided which may include capturing at least three polarization angles of a scene. The method may include translating polarization parameters associated with the at least three polarization angles to a reference angle to create a vector map and resolving the vector map into parallel components and perpendicular components, wherein the parallel components are parallel to a plane of incidence of light in the scene and the perpendicular components are perpendicular. The method may further include determining a range map based at least in part on the parallel and perpendicular components, detecting an object present in the scene using the range map and an airlight scattering polarization component, and outputting an indication of the object.

A visual guidance system for a vehicle includes an imaging system for producing a digital image of an environment, a three-dimensional scanning system for producing a digital point cloud of the environment, and a memory storing instructions executable by a processor to: process the digital image to detect an object and classify the object; process the point cloud to group points into a grouping representing the object; fuse the point cloud grouping with the digital image object to produce a fused data set frame, and determine the object location from the fused data set frame; determine an object velocity relative to the vehicle by comparing the fused data set frame to a previous data set frame; determine whether the object is an obstacle based on the object location and relative velocity; determine a threat level of the obstacle; and report a threat if the threat level exceeds a threshold.

A method of adjusting a grid spacing of a height map for autonomous driving, may include acquiring a 2D image of a region ahead of a vehicle, generating a depth map using depth information on an object present in the 2D image, converting the generated depth map into a 3D point cloud, generating the height map by mapping the 3D point cloud onto a grid having a predetermined size, and adjusting a grid spacing of the height map in consideration of the driving state of the vehicle relative to the object.

One or more embodiments herein can enable identification of an obstacle free area about an object. An exemplary system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise an obtaining component that obtains raw data defining a physical state of an environment around an object from a vantage of the object, and a generation component that, based on the raw data, generates a dimension of a portion or more of a virtual polygon representing a boundary about the object, wherein the boundary bounds free space about the object. A sensing sub-system can comprise both an ultrasonic sensor and a camera that can separately sense the environment about the object from the vantage of the object to thereby generate separate polygon measurement sets.

This document discloses system, method, and computer program product embodiments for image-based pedestrian speed estimation. For example, the method includes receiving an image of a scene, wherein the image includes a pedestrian and predicting a speed of the pedestrian by applying a machine-learning model to at least a portion of the image that includes the pedestrian. The machine-learning model is trained using a data set including training images of pedestrians, the training images associated with corresponding known pedestrian speeds. The method further includes providing the predicted speed of the pedestrian to a motion-planning system that is configured to control a trajectory of an autonomous vehicle in the scene.

Provided are a system and a method for drive control of front ultrasonic sensors, which may automatically control a waveform output from the front ultrasonic sensor detecting the presence or absence of an obstacle in front of a vehicle to prevent the sensor from acting as an interference source for another vehicle even when the sensor is constantly driven.

A method for estimating depth implemented in an electronic device includes obtaining a first image; inputting the first image into a depth estimation model, and obtaining a first depth image; obtaining a depth ratio factor, the depth ratio factor indicating a relationship between a relative depth and a depth of each pixel in the first depth image; and obtaining depth information of the first depth image according to the first depth image and the depth ratio factor.