A vehicular vision system includes a camera disposed at a vehicle and capturing image data. The system, via processing at an electronic control unit (ECU) of image data captured by the camera, detects an object at a first orientation relative to camera. A first local binary pattern represents in binary form a first portion of the image data that includes the detected object. The system detects the object at a second orientation relative to camera. A second local binary pattern represents in binary form a second portion of the image data that includes the detected object. The second orientation is different from the first orientation and the second local binary pattern is different than the first local binary pattern. The system groups the first and second local binary patterns into a common histogram bin. The system classifies the detected object based at least in part on the common histogram bin.

Devices and methods for determining an action in the presence of road users are provided in this disclosure. A device may include a processor. The processor may be configured to access environment information including an indication of a size of road users intersecting with a predetermined route of a vehicle in a road environment. The processor may further be configured to prioritize an anticipated movement of at least one of the road users over a predicted movement of the vehicle within the predetermined route based on the size of road users. The processor may further be configured to determine a vehicle action allowing the anticipated movement of the at least one road user.

System, methods, and embodiments described herein relate to predicting a future state of an object detected in a vicinity of a vehicle. In one embodiment, a method for predicting a state of an object includes detecting, at a plurality of discrete times [t, t−1, t−2, . . . ], a respective plurality of states of the object, obtaining, based at least in part on a present location of the vehicle, supplemental information, associated with an environment of the present location, that indicates at least a speed reduction factor, executing a prediction operation to determine a predicted state of the object at a time t+1 based at least in part on the detected plurality of states and the supplemental information, determining an actual state of the object at a time t+1 based on data from the one or more sensors, and modifying the prediction operation based at least in part on the actual state.

An autonomous driving system acquires information concerning a vehicle density in an adjacent lane that is adjacent to a lane on which an own vehicle is traveling, when the own vehicle travels on a road having a plurality of lanes. The autonomous driving system selects the adjacent lane as an own vehicle travel lane, when the vehicle density in the adjacent lane that is calculated from the acquired information is lower than a threshold density that is determined in accordance with relations between the own vehicle and surrounding vehicles. The autonomous driving system performs lane change to the adjacent lane autonomously, or propose lane change to the adjacent lane to a driver, when the adjacent lane is selected as the own vehicle travel lane.

Methods, systems, and apparatus for generation and use of surfel maps to plan for occlusions. One of the methods includes receiving a previously-generated surfel map depicting an area in which a vehicle is located, the surfel map comprising a plurality of surfels, each surfel corresponding to a respective different location in the area in which a vehicle is located; receiving, from one or more sensors, sensor data representing the area in which the vehicle is located; determining, based on the sensor data, that the area in which a vehicle is located includes a dynamic object having a changed shape relative to its representation in the surfel map; and generating an updated path for the vehicle to travel that avoids an occlusion by the changed shape of the dynamic object of a line of sight of one or more sensors to an area of interest.

A vehicle control device includes a peripheral object recognition unit configured to recognize an object that is present around a vehicle to be controlled, an object state recognition unit configured to recognize, in a situation where a first object and a second object are recognized by the peripheral object recognition unit, states of the first object and the second object including distance between the vehicle to be controlled and the first object, distance between the vehicle to be controlled and the second object, moving direction of the first object, and moving direction of the second object, and a display control unit configured to cause the display device to execute external notification processing for displaying a notification to the first object or the second object based on the states of the first object and the second object recognized by the object state recognition unit.

A the method for controlling a vehicle: recognizing at least a position of a traffic participant around a vehicle and a road environment around the traffic participant, setting a risk region for the traffic participant based on at least the recognized position of the traffic participant, correcting the set risk region based on a width of a sidewalk where the traffic participant is present or a width of a roadway around the traffic participant which is the recognized road environment, and controlling a speed and steering of the vehicle based on the corrected risk region.

A trajectory setting device that sets a trajectory of a host vehicle includes a first path generation unit configured to generate a first path by assuming all obstacles around the host vehicle to be stationary obstacles, a second path generation unit configured to generate a second path when the moving obstacle is assumed to move independently, a third path generation unit configured to generate a third path when the moving obstacle is assumed to move while interacting with at least one of the other obstacles or the host vehicle, a reliability calculation unit configured to calculate reliability of the second path and reliability of the third path, and a trajectory setting unit configured to set the trajectory for traveling from the first path, the second path, and the third path based on the reliability of the second path and the reliability of the third path.

The present disclosure provides autonomous vehicle systems and methods that include or otherwise leverage a machine-learned yield model. In particular, the machine-learned yield model can be trained or otherwise configured to receive and process feature data descriptive of objects perceived by the autonomous vehicle and/or the surrounding environment and, in response to receipt of the feature data, provide yield decisions for the autonomous vehicle relative to the objects. For example, a yield decision for a first object can describe a yield behavior for the autonomous vehicle relative to the first object (e.g., yield to the first object or do not yield to the first object). Example objects include traffic signals, additional vehicles, or other objects. The motion of the autonomous vehicle can be controlled in accordance with the yield decisions provided by the machine-learned yield model.

A method and an apparatus for controlling an autonomous vehicle are provided according to the embodiments of the disclosure. The method includes: sending, in response to determining that a pedestrian is in a first target area, behavior prompt information representing prompting the pedestrian to make a corresponding behavior; determining whether a deceleration condition matching the behavior prompt information is satisfied based on acquired behavior information of the pedestrian; and sending control information for reducing a moving speed of the autonomous vehicle, in response to determining that the deceleration condition is satisfied and determining that a speed of the autonomous vehicle is greater than a preset deceleration threshold. According to the embodiments, deceleration control of the autonomous vehicle is achieved based on the response of the pedestrian to the behavior prompt information.