A temporal prediction model for semantic intent understanding is described. An agent (e.g., a moving object) in an environment can be detected in sensor data collected from sensors on a vehicle. Computing device(s) associated with the vehicle can determine, based partly on the sensor data, attribute(s) of the agent (e.g., classification, position, velocity, etc.), and can generate, based partly on the attribute(s) and a temporal prediction model, semantic intent(s) of the agent (e.g., crossing a road, staying straight, etc.), which can correspond to candidate trajectory(s) of the agent. The candidate trajectory(s) can be associated with weight(s) representing likelihood(s) that the agent will perform respective intent(s). The computing device(s) can use one (or more) of the candidate trajectory(s) to determine a vehicle trajectory along which a vehicle is to drive.

An autonomous vehicle configured for active sensing may also be configured to weigh expected information gains from active-sensing actions against risk costs associated with the active-sensing actions. An example method involves: (a) receiving information from one or more sensors of an autonomous vehicle, (b) determining a risk-cost framework that indicates risk costs across a range of degrees to which an active-sensing action can be performed, wherein the active-sensing action comprises an action that is performable by the autonomous vehicle to potentially improve the information upon which at least one of the control processes for the autonomous vehicle is based, (c) determining an information-improvement expectation framework across the range of degrees to which the active-sensing action can be performed, and (d) applying the risk-cost framework and the information-improvement expectation framework to determine a degree to which the active-sensing action should be performed.

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a driver-assist object detection system is provided for a vehicle. One or more processing devices associated with the system receive at least two images from a plurality of captured images via a data interface. The device(s) analyze the first image and at least a second image to determine a reference plane corresponding to the roadway the vehicle is traveling on. The processing device(s) locate a target object in the first two images, and determine a difference in a size of at least one dimension of the target object between the two images. The system may use the difference in size to determine a height of the object. Further, the system may cause a change in at least a directional course of the vehicle if the determined height exceeds a predetermined threshold.

The vehicle control device recognizes an object around an own-vehicle and acquires information related to a road shape ahead of the own-vehicle. The vehicle control device detects, when a road shape prompting cut-in to an own-lane is acquired, a monitoring target vehicle likely to be prompted to cut into the own-lane among the recognized objects. The vehicle control device executes an evasive preparation for cut-in of the monitoring target vehicle when the monitoring target vehicle is detected. The vehicle control device executes an evasive action to avoid interference between the monitoring target vehicle and the own-vehicle when a predetermined motion from which cut-in of the monitoring target vehicle to the own-lane is expected is sensed after the evasive preparation is executed.

A turn signal system for activating a turn signal of a vehicle includes an electronic control unit having a processor and a non-transitory computer readable memory including a machine-readable instruction set. The electronic control unit is communicatively coupled to one or more external vehicle environment sensors, a vehicle speed sensor, and a turn signal. The machine-readable instruction set causes the processor to determine a vehicle speed based on an output signal of the vehicle speed sensor, predict a vehicle turning maneuver based on one or more environment signals output by the one or more external vehicle environment sensors when the vehicle speed is below a threshold, and automatically activate the turn signal in response to predicting the vehicle turning maneuver.

A device for setting a target vehicle that sets a target vehicle to be subjected to driving assistance control of a host vehicle includes: a detection signal acquisition device capable of acquiring a first detection signal representing an object by an image, and a second detection signal representing the object by a reflection point; and setting control unit, which determines whether to set a forward object as a target vehicle, wherein if a movement history is not associated with the forward object, and a combination history is associated with the forward object, then as a selection threshold of a first determination parameter for determining whether to set the forward object as the target vehicle, a selection threshold is used such that the forward object is less likely to be selected as the target vehicle than with the selection threshold which would be used if a movement history is associated with the forward object.

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 system includes a computer including a processor and a memory, the memory storing instructions executable by the processor to determine respective threat numbers for each of a plurality of targets based on an angular acceleration of a host vehicle and actuate a component in the host vehicle based on the threat numbers.

An apparatus is mounted on a vehicle that is autonomously driven and that is used for passenger transportation. The apparatus includes: a processor; and a memory storing thereon a computer program. When executed by the processor, the program causes the processor to perform operations including: selecting a target person who is at least one of humans who are present around the vehicle and are detected using sensing information about surroundings of the vehicle; acquiring a first state indicating a state of the selected target person; acquiring a second state indicating a state of the vehicle changing a speed setting of the vehicle in accordance with the first state and the second state; and controlling traveling of the vehicle at a speed indicated by the speed setting.

A method and a system for predicting a near future path and an associated output control signal for a vehicle. Prediction sensor data, vehicle driving data, and road data are collected. An input control signal indicative of an intended driving action is received. The sensor data and the vehicle driving data are pre-processed to provide a set of object data comprising a time series of previous positions of a respective object relative the vehicle, a time series of the previous headings of the object, and a time series of previous velocities of the object. The object data, the road data, the vehicle driving data, the control signal, and the sensor data are processed in a deep neural network. Based on the processing in the deep neural network, a predicted path output and an output control signal are provided.