A traffic signal recognition method and a traffic signal recognition device acquire a result sequence consisting of a plurality of determination results in time-series order obtained by determining a display state of a traffic signal based on a plurality of images of a traveling direction of a vehicle, set a first threshold as a number threshold if a specific determination result which is a latest determination result among the determination results is a determination result of a permission display that allows a passage of the stop line corresponding to the traffic signal, set a second threshold smaller than the first threshold as the number threshold if the specific determination result is other than a determination result of the permission display, and output the specific determination result if a number of determination results identical to the specific determination result is greater than the number threshold.

A traffic signal recognition method and a traffic signal recognition device acquire a result sequence consisting of a plurality of determination results in time-series order obtained by determining a display state of a traffic signal based on a plurality of images of a traveling direction of a vehicle, set a first threshold as a number threshold if a first distance from a position of the vehicle to a position of a stop line corresponding to the traffic signal is equal to or greater than a distance threshold, set a second threshold smaller than the first threshold as the number threshold if the first distance is less than the distance threshold, and output a latest determination result if a number of determination results identical to the latest determination result is greater than the number threshold, among the determination results.

An adaptive cruise control for a motor vehicle includes a detection module for detecting a vehicle ahead, a production module capable of producing a setpoint, a storage module configured to store a value corresponding to the setpoint, a chronometer configured to be initialized and activated at a moment when the vehicle stops, and a reinitialization module for reinitializing the storage module. This cruise control includes a setup module capable of determining a limit according to the environment of the vehicle. The reinitialization module reinitializes the value stored by the storage module if the chronometer provides a duration greater than the limit.

A vehicle includes a common pedal that receives an acceleration operation and a deceleration operation in accordance with a depression amount, a deceleration deriving unit that derives a deceleration of the vehicle based on the deceleration operation of the common pedal, a first distance deriving unit that derives a predicted stopping distance that is a distance between a current position of the vehicle and a predicted stop position at which the vehicle is predicted to stop when travelling at the derived deceleration, a second distance deriving unit that derives a target stopping distance that is a distance between the current position of the vehicle and a target stop position at which the vehicle is to stop, and a deceleration correcting unit that corrects the deceleration of the vehicle based on the predicted stopping distance and the target stopping distance.

A system uses video of a vehicle to detect and classify the vehicle's pose. The system generates an image stack by scaling and shifting a set of digital image frames from the video to a fixed scale, yielding a sequence of images over a time period. The system processes the image stack with a classifier to determine the pose of the object. The system also may determine state and class of visible turn signals on the object, as well as predict the vehicle's direction of travel.

Techniques for collecting, synchronizing, and displaying various types of data relating to a road segment enable, via one or more local or remote processors, servers, transceivers, and/or sensors, (i) enhanced and contextualized analysis of vehicle events by way of synchronizing different data types, relating to a monitored road segment, collected via various different types of data sources; (ii) enhanced and contextualized analysis of filed insurance claims pertaining to a vehicle incident at a road segment; (iii) advantageous machine learning techniques for predicting a level of risk assumed for a given vehicle event or a given road segment; (iv) techniques for accounting for region-specific driver profiles when controlling autonomous vehicles; and/or (v) improved techniques for providing a GUI to display collected data in a meaningful and contextualized manner.

A method for determining situational awareness in a worksite includes setting at least one environment modelling apparatus (EM) at least one of: on a machine or external from the machine; setting at least one tracking apparatus (TA) at least one of: on the machine or external from the machine; acquiring data by the at least one tracking apparatus (TA); and acquiring data by the at least one environment modelling apparatus. Further, the method includes receiving, by at least one position determination unit (PDU), data related to the at least one tracking apparatus (TA) and data related to the at least one environment modelling apparatus (EM); and determining, by the at least one position determination unit (PDU), based at least in part on the received data, the location and orientation of the machine in the worksite.

An anomaly of a subject can be properly determined even in false image recognition of the subject. An information processing method, performed by at least one processor in an information processing apparatus, comprises acquiring an image including a subject from an imaging apparatus; acquiring respectively items of data from a plurality of sensors provided for the subject or an object near the subject, the items of data being sensed respectively by the plurality of sensors; acquiring a determination result on whether the subject is abnormal or not by inputting the items of data acquired from the plurality of sensors to a learning model utilizing a neural network, wherein the learning model has learned presence or absence of an anomaly in the subject by using respectively past items of data from the plurality of sensors as learning data; and determining whether a recognition result of the subject based on the image is abnormal or not by using the determination result.

The technology involves to pickups performed by autonomous vehicles. In particular, it includes identifying one or more potential pullover locations adjacent to an area of interest that an autonomous vehicle is approaching. The vehicle detects that a given one of the potential pullover locations is occupied by another vehicle and determines whether the other vehicle will be vacating the given pullover location within a selected amount of time. Upon determining that the other vehicle will be vacating the given potential pullover location within the timeframe, the vehicle determines whether to wait for the other vehicle to vacate the given pullover location. Then a driving system of the vehicle either performs a first action in order to wait for the other vehicle to vacate the given pullover location or performs a second action that is different from the first action when it is determined to not wait.

A method includes determining the position of the motor vehicle, identifying at least one object not relevant to road traffic as an object in the landscape in the surroundings of the motor vehicle, depending on the determined position, detecting at least the direction of view of a passenger as an occupant of the motor vehicle, and providing a display with information about the at least one object in the landscape viewed by the passenger, such that said information is provided in the direct field of view of the passenger, as defined by the direction of view of the same, in the same place as the object.