Systems and methods use cameras to provide autonomous navigation features. In one implementation, a method for navigating a user vehicle may include acquiring, using at least one image capture device, a plurality of images of an area in a vicinity of the user vehicle; determining from the plurality of images a first lane constraint on a first side of the user vehicle and a second lane constraint on a second side of the user vehicle opposite to the first side of the user vehicle; enabling the user vehicle to pass a target vehicle if the target vehicle is determined to be in a lane different from the lane in which the user vehicle is traveling; and causing the user vehicle to abort the pass before completion of the pass, if the target vehicle is determined to be entering the lane in which the user vehicle is traveling.

A method for tracking occluded objects includes encoding locations of a plurality of objects in an environment, determining a target object, receiving a first end point corresponding to a position of the target object before occlusion behind an occlusion object, distributing a hypothesis between both sides of the occlusion object during occlusion from a subsequent frame of the sequence of frames, receiving a second end point corresponding to a position of the target object after emerging from occlusion from another subsequent frame of the sequence of frames, and determining a trajectory of the target object when occluded by the occlusion object by performing inferences using a spatio-temporal probabilistic graph based on the current frame and the subsequent frames of the sequence of frames. The trajectory of the target object when occluded is used as a learning model for future target objects that are occluded by the occlusion object.

A method for tracking occluded objects includes encoding locations of a plurality of objects in an environment, determining a target object, receiving a first end point corresponding to a position of the target object before occlusion behind an occlusion object, distributing a hypothesis between both sides of the occlusion object during occlusion from a subsequent frame of the sequence of frames, receiving a second end point corresponding to a position of the target object after emerging from occlusion from another subsequent frame of the sequence of frames, and determining a trajectory of the target object when occluded by the occlusion object by performing inferences using a spatio-temporal probabilistic graph based on the current frame and the subsequent frames of the sequence of frames. The trajectory of the target object when occluded is used as a learning model for future target objects that are occluded by the occlusion object.

An environmental information acquiring unit (11) to acquire environmental information on an environment around a mobile object, an action information acquiring unit (12) to acquire action information on an action of a driver of the mobile object, a calculation unit (13) to obtain control information for performing automated driving control of the mobile object on the basis of the environmental information acquired by the environmental information acquiring unit (11) and a machine learning model (18) that uses the environmental information as an input and outputs the control information, a contribution information determining unit (14) to determine contribution information having a high degree of contribution to the control information on the basis of the environmental information and the control information, a cognitive information calculating unit (15) to calculate cognitive information indicating a cognitive region of the driver in the environment around the mobile object on the basis of the action information and the environmental information, a specification unit (16) to specify unrecognized contribution information estimated not to be recognized by the driver on the basis of the contribution information and the cognitive information, and an information output control unit (17) to output driving assistance information necessary for driving assistance on the basis of the unrecognized contribution information specified by the specification unit (16) are provided.

A system for controlling an intelligent network vehicle is provided, and the system comprises a sensor group configured to obtain sensor information; a sensing and positioning module configured to obtain sensing information and positioning information based on the sensor information; a planning and control module configured to determine vehicle planning control information based on the sensing information and the positioning information; a safety control module configured to determine safety control information based on the sensing information and the positioning information; a function assessment module configured to determine a vehicle state assessment result; a risk assessment module configured to determine a risk assessment result; a logical arbitration module configured to determine vehicle execution information by arbitrating the vehicle planning control information and the safety control information; and an execution module configured to control the vehicle driving based on the vehicle execution information.

A system for controlling an intelligent network vehicle is provided, and the system comprises a sensor group configured to obtain sensor information; a sensing and positioning module configured to obtain sensing information and positioning information based on the sensor information; a planning and control module configured to determine vehicle planning control information based on the sensing information and the positioning information; a safety control module configured to determine safety control information based on the sensing information and the positioning information; a function assessment module configured to determine a vehicle state assessment result; a risk assessment module configured to determine a risk assessment result; a logical arbitration module configured to determine vehicle execution information by arbitrating the vehicle planning control information and the safety control information; and an execution module configured to control the vehicle driving based on the vehicle execution information.

A shock damper is disposed between a vehicle body side and a wheel side. A suspension control device calculates a damping force of the shock damper on the basis of vehicle height information and controls the damping force. A steering system includes an electric motor and a steering control device that controls the electric motor, and assists steering effort of the driver through the electric motor. The suspension control device calculates the vibration generated in a steering on the basis of a detected value of a vehicle height sensor and creates a signal for generating steering torque that reduces the generated vibration. The suspension control device outputs the created signal to the steering control device. Steering torque for cancelling steering vibration is accordingly outputted from the electric motor of the steering system.

A shock damper is disposed between a vehicle body side and a wheel side. A suspension control device calculates a damping force of the shock damper on the basis of vehicle height information and controls the damping force. A steering system includes an electric motor and a steering control device that controls the electric motor, and assists steering effort of the driver through the electric motor. The suspension control device calculates the vibration generated in a steering on the basis of a detected value of a vehicle height sensor and creates a signal for generating steering torque that reduces the generated vibration. The suspension control device outputs the created signal to the steering control device. Steering torque for cancelling steering vibration is accordingly outputted from the electric motor of the steering system.

A computing framework for addressing a variety of vehicle conditions includes receiving, from a first set of sensors by an edge compute node, first transportation network data associated with a transportation network region, receiving, from a second set of sensors by a cloud computing node, second transportation network data associated multiple transportation network regions, providing, by the edge compute node to one or more autonomous vehicles at the transportation network region, real-time transportation network region information based on at least the first transportation network data to facilitate control decisions by the one or more autonomous vehicles, and providing, by the cloud computing node to at least the one or more autonomous vehicles, non-real-time transportation network region information based on at least the second transportation network data to facilitate the control decisions by the at least one or more autonomous vehicles.

A computing framework for addressing a variety of vehicle conditions includes receiving, from a first set of sensors by an edge compute node, first transportation network data associated with a transportation network region, receiving, from a second set of sensors by a cloud computing node, second transportation network data associated multiple transportation network regions, providing, by the edge compute node to one or more autonomous vehicles at the transportation network region, real-time transportation network region information based on at least the first transportation network data to facilitate control decisions by the one or more autonomous vehicles, and providing, by the cloud computing node to at least the one or more autonomous vehicles, non-real-time transportation network region information based on at least the second transportation network data to facilitate the control decisions by the at least one or more autonomous vehicles.