A method for training a trajectory planning model, an apparatus, and computer storage medium are provided. The method may include: obtaining an image of a physical environment in which a vehicle is located via at least one sensor of the vehicle, the image including multiple objects surrounding the vehicle; obtaining a feature chart indicating multiple initial trajectory points of the vehicle in the image from a trajectory planning model based on the image; identifying the image to determine in the image a first area associated with a road object in multiple objects and a second area associated with a non-road object in the multiple objects; determining a planning trajectory point based on positional relationship of the multiple initial trajectory points with respect to the first area and the second area; and training a trajectory planning model based on the planning track point and the actual trajectory point of the vehicle.

A vehicle control apparatus selects, as a first group, objects in a predetermined area from objects included in object information, selects, as a second group, an upper limit number of the objects from the first group in descending order of a priority value when the number of the objects of the first group is greater than the upper limit number, and executes a collision avoidance control when an index value satisfies a predetermined condition. The priority value represents a probability that the own vehicle collides with the object. The apparatus reduces the priority value of a particular object of the first group when the moving speed of the own vehicle is equal to or lower than a moving speed threshold. The particular object is an object which is deemed to have a moving speed lower than a moving speed of a four-wheel vehicle.

In a vehicle control device for an autonomous driving vehicle that autonomously travels based on an operation command, a gesture image of a person around the autonomous driving vehicle is acquired, and a stored reference gesture image is collated with the acquired gesture image. At this time, when it is discriminated that the gesture of the person around the autonomous driving vehicle is a gesture requesting the autonomous driving vehicle to stop, it is determined whether a disaster has occurred. When it is determined that the disaster has occurred, the autonomous driving vehicle is caused to stop around the person requesting the autonomous driving vehicle to stop.

Aspects of the disclosure relate to detecting and responding to malfunctioning traffic signals for a vehicle having an autonomous driving mode. For instance, information identifying a detected state of a traffic signal for an intersection. An anomaly for the traffic signal may be detected based on the detected state and prestored information about expected states of the traffic signal. The vehicle may be controlled in the autonomous driving mode based on the detected anomaly.

A system in a vehicle includes a camera to obtain video in a field of view over a number of frames and a controller to process the video to identify one or more pedestrians. The controller also implements a neural network to provide a classification of a motion of each pedestrian among a set of classifications of pedestrian motion. Each classification among the set of classifications indicates initiation of the motion or no change in the motion. The controller predicts a trajectory for each pedestrian based on the classification of the motion, and controls operation of the vehicle based on the trajectory predicted for each pedestrian.

Systems and methods are provided for navigating a host vehicle. A processing device may be programmed to receive an image representative of an environment of the host vehicle; determine a planned navigational action for the host vehicle; analyze the image to identify a target vehicle travelling toward the host vehicle; determine a next-state distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken; determine a stopping distance for the host vehicle based on a braking profile, a maximum acceleration capability, and a current speed of the host vehicle; determine a stopping distance for the target vehicle based on a braking profile and a current speed of the target vehicle; and implement the planned navigational action if the determined next-state distance is greater than a sum of the stopping distances for the host vehicle and the target vehicle.

A driving assist system executes driving assist control for avoiding a collision with a target ahead of a vehicle. The driving assist control operates when the target exists within an assist area. A crossing target is the target crossing a roadway area ahead of the vehicle from a first side toward a second side. The assist area for the crossing target is divided into a plurality of divided assist areas including a first assist area located on the first side as viewed from the vehicle and a second assist area located on the second side as viewed from the vehicle. When the crossing target exists in the second assist area, the driving assist system decreases a control strength of the driving assist control as compared with a case where the crossing target exists in the first assist area.

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.

A vehicle configured to alert pedestrians. The vehicle includes a driver-assist system including a memory device, a processor, and at least one camera. The memory device includes instructions which, when executed by the processor, cause the processor to detect, utilizing the at least one camera, at least one pedestrian near the vehicle, determine a proximity of the at least one pedestrian to the vehicle, compare the proximity to a threshold proximity, and automatically emit an audible alert from the vehicle in response to the proximity being less than the threshold proximity.

An apparatus and method are provided for controlling autonomous driving of a vehicle which may derive predicted paths of a pedestrian and a two-wheel vehicle during autonomous driving of the vehicle so as to minimize accidents. The method includes calculating first height information allocating a first gradient that descends in a proceeding direction of objects, including a vehicle and a pedestrian, from respective positions of the objects based on dynamic information of the objects, calculating second height information allocating a second gradient based on a probability that the pedestrian will occupy infrastructure, calculating final height information by fusing the first height information and the second height information, generating a predicted path of the pedestrian, determining a driving strategy of a host vehicle based on a predicted path of the host vehicle and the predicted path of the pedestrian.