According to an embodiment, an information processing device includes a memory and processing circuitry. The processing circuitry is configured to acquire a map defining a target in a coordinate space in which a direction along a traveling direction of a moving object is one of coordinate axes, and approximate a movement route of the moving object with a specified shape area along a coordinate axis in the coordinate space, the specified shape area being a basic unit of collision determination.

A method and an apparatus for controlling personal mobility device (PMD) depending on congestion includes identifying a target area according to an expected route of the PMD, acquiring a degree of congestion of the target area based on at least one of a type of objects, a movement of the objects, a distance between objects, the number of objects that is expected, and a speed limit of the target area, and controlling speed of the PMD according to the degree of congestion.

Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

A traveling control apparatus performs a target-following control process on a target to be followed detected by a target detecting unit. Further, the traveling control apparatus calculates a probability that the target to be followed is within an own lane, and determines whether a degree of recognition by the target detecting unit of the target to be followed is in a weakly recognized state where the degree of recognition is weaker than a predetermined degree. The apparatus sets a reliability of the target to be followed on the basis of the probability calculated by a probability calculating process and a determination result by a determining process, and controls acceleration of an own vehicle so that a jerk which is a differential value of the acceleration becomes smaller as the reliability of the target to be followed is lower while the target-following control process is performed.

A driver assistance system for automated longitudinal guidance of a motor vehicle includes a sensor system configured to identify an upcoming traffic scene, including locating road users situated ahead of the motor vehicle; and a control unit. The control unit is configured to carry out an automated longitudinal guidance of the motor vehicle depending on the upcoming traffic scene such that, in response to detecting that the upcoming traffic scene is a following-travel standstill situation, the motor vehicle is braked to a standstill at a target stopping distance from a road user situated ahead and identified as a target object. The control unit is also configured to detect a manual request to reduce the predefined target stopping distance. The control unit is further configured to reduce the target stopping distance to a reduced target stopping distance on the basis of the request.

Disclosed are a short cut-in target identification apparatus and an identification method thereof. The short cut-in target identification apparatus includes an occupancy distance map (ODM) information calculator configured to calculate ODM information based on subject vehicle and surrounding object information, a track information calculator configured to calculate track information based on the subject vehicle and surrounding object information, and a short cut-in target selector configured to select a short cut-in target based on the ODM information and the track information.

A vehicle and an adaptive cruise control system (ACC) is provided. The ACC comprises a steering wheel system with a steering wheel arranged to allow the provision of manual steering input to the steering system of the vehicle and a steering angle sensor, wherein the steering system is configured to identify a specific momentary manual steering wheel actuation by comparing data from the steering angle sensor with predetermined thresholds, and to select a next one of the moving or stationary objects in the surroundings in front of said vehicle to control the speed of said vehicle in relation to, based on the direction of the specific momentary manual steering wheel actuation indicated by the steering angle sensor.

A vehicle identification system includes a communication device receiving other vehicle information related to other vehicle around a host vehicle, a detection device detecting other vehicle around the host vehicle, and a vehicle identification device identifying a vehicle transmitting the other vehicle information on the basis of the other vehicle information received by the communication device and the detection device, wherein the vehicle identification device is switching between a capturing mode capturing the transmitting vehicle on the basis of the other vehicle information received by the communication device and the detection device, and a tracking mode identifying the transmitting vehicle on the basis of a positional relation between the host vehicle and the transmitting vehicle at the time of the capturing mode, and motion information related to the transmitting vehicle based on the other vehicle information received by the communication device, after the capturing mode.

A driving assistance system includes at least one receiving module designed to receive perception data from a driving environment, a control module designed to control an on-board system, a conversion module designed to generate, on the basis of the perception data, a plurality of instances of classes of an ontology stored by the driving assistance system and defining relations between classes, and a reasoning tool designed to deduce, on the basis of the ontology, at least one property of an instance of the plurality. The control module is designed to control the on-board system on the basis of the deduced property.