Techniques are discussed for predicting locations of an object based on attributes of the object and/or attributes of other object(s) proximate to the object. The techniques can predict locations of a pedestrian proximate to a crosswalk as they traverse or prepare to traverse through the crosswalk. The techniques can predict locations of objects as the object traverses an environment. Attributes can comprise information about an object, such as a position, velocity, acceleration, classification, heading, relative distances to regions or other objects, bounding box, etc. Attributes can be determined for an object over time such that, when a series of attributes are input into a prediction component (e.g., a machine learned model), the prediction component can output, for example, predicted locations of the object at times in the future. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on the predicted locations.

Aspects of the disclosure relate to controlling a vehicle having an autonomous driving mode. For instance, that the vehicle is approaching a crosswalk may be determined. A set of segments may be identified for the crosswalk. A set of potential occluded pedestrians may be generated. Each potential occluded pedestrian of the set is assigned a speed characteristic and a segment. The segments of the set of potential occluded pedestrians may be updated over time using the assigned speed characteristics. Sensor data from a perception system of the vehicle is received, and one or more potential occluded pedestrians an having an updated assigned segment corresponding to a segment that is visible to a perception system of the vehicle may be removed from the set of potential occluded pedestrians. After the removing, the set may be used to control the vehicle in the autonomous driving mode.

A moving range of an object is estimated on the basis of image information. An information processing apparatus includes an input unit that inputs an image, a region estimation unit that estimates a region of an object contained in the image, a moving history information acquisition unit that acquires information associated with a moving history of the object, a contact region determination unit that determines a contact region in contact with the object on the basis of an estimation result obtained by the region estimation unit, and a moving range estimation unit that estimates a moving range of the object on the basis of the moving history containing the contact region of the object. The moving range estimation unit estimates the moving range of the object on the basis of the moving history containing the contact region of the object and a moving track of the object.

Embodiments of the disclosure provide methods and systems for predicting a movement trajectory of a pedestrian. The system includes a communication interface configured to receive a map of an area in which the pedestrian is traveling and sensor data acquired associated with the pedestrian. The system includes at least one processor configured to position the pedestrian in the map, and extract pedestrian features from the sensor data. The at least one processor is further configured to identify one or more objects surrounding the pedestrian based on the positioning of the pedestrian, and extract object features of the one or more objects from the sensor data. The at least one processor is also configured to predict the movement trajectory and a movement speed of the pedestrian based on the extracted pedestrian features and object features using a learning model.

A driving assistance system includes a processor and a memory that stores surroundings information indicating the surroundings of a vehicle detected by sensors mounted on the vehicle. The processor is configured to acquire the position of a target in front of the vehicle and the position of the boundary of a roadway area in front of the vehicle based on the surroundings information. The processor is configured to determine whether the target is in the roadway area based on the position of the target and the position of the boundary. The processor is configured to calculate the distance between the target and the boundary when the target is in the roadway area. The processor is configured to determine whether the target is crossing the roadway area based on the relationship between the distance and a time.

A vehicle driving assistance apparatus includes a sensing unit for sensing an object outside the vehicle and a processor for obtaining surrounding situation information, based on a location of the object outside the vehicle. The processor is further configured to determine whether the object approaches the vehicle from a traveling lane or a lateral lane, based on the determination of whether the object approaches the vehicle from a traveling lane or a lateral lane, to generate a control signal, and to provide the control signal to a vehicle control system of the vehicle. The generated control signal can control at least one of a drive apparatus of the vehicle, a steering apparatus of the vehicle, or a brake apparatus of the vehicle to either avoid collision between the vehicle and the object or to perform an action that reduces an impulse on the vehicle from the collision.

One variation of a method for autonomously navigating along a crosswalk includes: at a first time, navigating autonomously along a sidewalk toward a crosswalk coinciding with a navigation route assigned to the autonomous vehicle; recording optical data of a scene proximal the autonomous vehicle via an optical sensor integrated into the autonomous vehicle; aligning an anteroposterior axis of the autonomous vehicle to the crosswalk detected in the optical data; identifying a pedestrian proximal the crosswalk in the optical data; in response to the pedestrian entering the crosswalk at a second time succeeding the first time, predicting right of way of the autonomous vehicle to enter the crosswalk; and, in response to predicting right of the autonomous vehicle to enter the crosswalk, autonomously navigating from the sidewalk into the crosswalk and autonomously navigating along the crosswalk to an opposing sidewalk according to the navigation route.

Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicle and/or smart homes are described herein. Autonomous operation features and related components can be assessed using direct or indirect data regarding operation. Such assessment may be performed to determine the condition of components for salvage following a collision or other loss-event. To this end, the information regarding a plurality of components may be received. A component of the plurality of components may be identified for assessment. Assessment may including causing test signals to be sent to the identified component. In response to the test signal, one or more responses may be received. The received response may be compared to an expected response to determine whether the identified component is salvageable.

Various technologies described herein pertain to controlling an autonomous vehicle to provide indicators that signal a driving intent of the autonomous vehicle. The autonomous vehicle includes a plurality of sensor systems that generate a plurality of sensor signals, a notification system, and a computing system. The computing system determines that the autonomous vehicle is to execute a maneuver that will cause the autonomous vehicle to traverse a portion of a driving environment of the autonomous vehicle. The computing system predicts that a person in the driving environment is to traverse the portion of the driving environment based upon the plurality of sensor signals. The computing system then controls the notification system to output a first indicator indicating that the autonomous vehicle plans to yield to the person or a second indicator indicating that the autonomous vehicle plans to execute the maneuver prior to the person traversing the portion of the driving environment.

Techniques are discussed for controlling a vehicle, such as an autonomous vehicle, based on occluded areas in an environment. An occluded area can represent areas where sensors of the vehicle are unable to sense portions of the environment due to obstruction by another object. An occlusion grid representing the occluded area can be stored as map data or can be dynamically generated. An occlusion grid can include occlusion fields, which represent discrete two- or three-dimensional areas of driveable environment. An occlusion field can indicate an occlusion state and an occupancy state, determined using LIDAR data and/or image data captured by the vehicle. An occupancy state of an occlusion field can be determined by ray casting LIDAR data or by projecting an occlusion field into segmented image data. The vehicle can be controlled to traverse the environment when a sufficient portion of the occlusion grid is visible and unoccupied.