A vehicle control apparatus comprises a first detection unit configured to have a first detection range, a second detection unit configured to have a second detection range which at least partially overlaps the first detection range, and a vehicle control unit configured to be capable of performing vehicle control based on a first control state and vehicle control based on a second control state which has a high vehicle control automation rate or a reduced degree of vehicle operation participation requested to a driver compared to the first control state. The vehicle control unit performs control to shift from the first control state to the second control state based on a condition that a match degree between pieces of preceding object information of a vehicle detected by the first detection unit and the second detection unit.

A method for setting a tuning parameter for an Automated Driving System (ADS) of a vehicle is disclosed. A corresponding non-transitory computer-readable storage medium, vehicle control device and a vehicle comprising such a control device are also disclosed. The method comprises receiving environmental data from a perception system of the vehicle, said environmental data comprising a plurality of environmental parameters, determining, by means of a self-learning model, an environmental scenario based on the received environmental data; setting the tuning parameter for the ADS based on the self-learning model and the determined environmental scenario, the tuning parameter defining a dynamic parameter of the ADS, receiving at least one signal representative of a vehicle user feedback on the set tuning parameter, and updating the self-learning model for the set tuning parameter for the identified environmental scenario based on the received vehicle user feedback.

To provide a technology that makes it possible to recognize a target object quickly and accurately. An information processing apparatus according to the present technology includes a controller. The controller recognizes a target object on the basis of event information that is detected by an event-based sensor, and transmits a result of the recognition to a sensor apparatus that includes a sensor section that is capable of acquiring information regarding the target object.

Trajectory generation for controlling motion or other behavior of an autonomous vehicle may include alternately determining a candidate action and predicting a future state based on that candidate action. The technique may include determining a cost associated with the candidate action that may include an estimation of a transition cost from a current or former state to a next state of the vehicle. This cost estimate may be a lower bound cost or an upper bound cost and the tree search may alternately apply the lower bound cost or upper bound cost exclusively or according to a ratio or changing ratio. The prediction of the future state may be based at least in part on a machine-learned model's classification of a dynamic object as being a reactive object or a passive object, which may change how the dynamic object is modeled for the prediction.

Systems and methods for determining object prioritization and predicting future object locations for an autonomous vehicle are provided. A method can include obtaining, by a computing system comprising one or more processors, state data descriptive of at least a current or past state of a plurality of objects that are perceived by an autonomous vehicle. The method can further include determining, by the computing system, a priority classification for each object in the plurality of objects based at least in part on the respective state data for each object. The method can further include determining, by the computing system, an order at which the computing system determines a predicted future state for each object based at least in part on the priority classification for each object and determining, by the computing system, the predicted future state for each object based at least in part on the determined order.

Systems and methods are provided for predicting blind spot incursions for a host vehicle. In one implementation, a navigation system for a host vehicle may comprise a processor. The processor may be programmed to receive, from an image capture device located on a rear of the host vehicle, at least one image representative of an environment of the host vehicle. The processor may be programmed to analyze the at least one image to identify an object in the environment of the host vehicle and to determine kinematic information associated with the object. The processor may further be programmed to predict, based on the kinematic information, that the object will travel in a region outside of a field of view of the image capture device and perform a control action based on the prediction.

A vehicle control device includes a first control unit that executes, when an abnormality of a driver of a vehicle is detected, stop control, a second control unit that executes, when the vehicle is determined to have a risk of collision, deceleration control, a determination unit that identifies an object around the vehicle as a target candidate of the collision and determines whether or not there is the risk of the collision with the identified target candidate, and a setting unit that sets, when the abnormality is detected, an operation mode of the deceleration control to a special mode from a normal mode, the normal mode provided for cases in which the abnormality is undetected. The determination unit expands a range for identifying the object around the vehicle as the target candidate of the collision in the special mode as compared with the range in the normal mode.

A vehicle driving assistance apparatus predicts (i) a first consumed energy amount corresponding to a consumed energy amount consumed by a driving apparatus of an own vehicle when executing a first following control and (ii) a second consumed energy amount corresponding to the consumed energy amount consumed by the driving apparatus of the own vehicle when executing the second following control. The apparatus executes the second following control when the second consumed energy amount is smaller than the first consumed energy amount. On the other hand, the apparatus executes the first following control when the second consumed energy amount is equal to or greater than the first consumed energy amount.

A deceleration support device in which an electronic control unit controls a speed reduction device to execute automatic deceleration control when an object that requires deceleration of a vehicle is detected and a braking/driving operation is not detected, and interrupt the automatic deceleration control when a driving operation is detected in a situation where the automatic deceleration control is being executed, and with an object that requires deceleration of the vehicle and does not prevent the vehicle from passing through a position of the object being defined as a specific object, the electronic control unit resumes the automatic deceleration control if it is determined that the object is not a specific object, but does not resume the automatic deceleration control if it is determined that the object is a specific object, when the driving operation is not detected in a situation where the automatic deceleration control is interrupted.

An estimation apparatus of the disclosure includes a determiner that determines whether or not an attention target is visually recognizable from a driver of a vehicle based on information related to a visual field range of the driver; and an estimator that estimates a visual field abnormality of the driver based on a driving operation of the driver in a case where the determiner determines that the attention target is visually recognizable from the driver.