A method for operating a higher-level automated vehicle (HAV), in particular a highly automated vehicle, is provided, including: S1 for providing a digital map, which may be a highly accurate digital map, in a driver assistance system of the HAV; S2 for determining an instantaneous vehicle position and localizing the vehicle position in the digital map; S3 for providing an expected setpoint traffic density at the vehicle position; S4 for ascertaining an instantaneous actual traffic density in the surroundings of the HAV; S5 for comparing the actual traffic density to the setpoint traffic density and ascertaining a difference value as the result of the comparison; S6 for checking the vehicle position of the HAV for plausibility at least partially based on the difference value and/or S7 for updating the digital map at least partially based on the difference value. Also described are a corresponding driver assistance system and a computer program.

An object recognition apparatus mounted in a vehicle including a sensor is provided. The apparatus includes a detection unit configured to detect an object present in a same lane as that of the vehicle by using information from the sensor, an acquisition unit configured to acquire information concerning lights which the object turns on by using the information from the sensor, and a determining unit configured to determine a type of the object based on the information concerning the lights. A condition in which the determination unit determines that the type of the object is a two-wheeled vehicle includes a case in which the object includes not less than two lights arrayed in a vertical direction with respect to the ground surface.

A trajectory setting device that sets a trajectory of a host vehicle includes a first path generation unit configured to generate a first path by assuming all obstacles around the host vehicle to be stationary obstacles, a second path generation unit configured to generate a second path when the moving obstacle is assumed to move independently, a third path generation unit configured to generate a third path when the moving obstacle is assumed to move while interacting with at least one of the other obstacles or the host vehicle, a reliability calculation unit configured to calculate reliability of the second path and reliability of the third path, and a trajectory setting unit configured to set the trajectory for traveling from the first path, the second path, and the third path based on the reliability of the second path and the reliability of the third path.

Disclosed herein are systems and methods for autonomous vehicle operation, in which a processor is configured to receive sensor data collected by a first sensor of a first autonomous vehicle during navigation of the first autonomous vehicle through a particular location and prior to a control signal subsequently generated by a controller of the first autonomous vehicle; determine based on the sensor data an event that triggered the control signal. A communication device coupled to the processor is configured to transmit to a second autonomous vehicle an instruction, based on the determined event, to adjust sensor data collected by a second sensor of the second autonomous vehicle during navigation of the second autonomous vehicle in the particular location.

A method for controlling a robot is provided. The method includes the steps of: acquiring information on a sound associated with a robot call in a serving place; determining a call target robot associated with the sound, among a plurality of robots in the serving place, on the basis of the acquired information; and providing feedback associated with the sound by the call target robot.

A method for controlling a robot is provided. The method includes the steps of: acquiring information on a sound associated with a robot call in a serving place; determining a call target robot associated with the sound, among a plurality of robots in the serving place, on the basis of the acquired information; and providing feedback associated with the sound by the call target robot.

Systems and methods for cooperatively managing mixed traffic at an intersection are disclosed herein. One embodiment determines, at an autonomous sensor-rich vehicle, that one or more other vehicles are following in the same lane, the one or more other vehicles including at least one legacy vehicle; communicates with the one or more other vehicles to form a platoon; receives Signal Phase and Timing (SpaT) information from a roadside unit associated with an intersection toward which the platoon is traveling; calculates at the autonomous sensor-rich vehicle, based at least in part on the SPaT information and location information for the platoon, a speed profile and a trajectory for the autonomous sensor-rich vehicle that minimize a delay of the platoon in traversing the intersection while accounting for fuel consumption; and executes the speed profile and the trajectory to control indirectly the one or more other vehicles while the platoon traverses the intersection.

An apparatus for de-icing a pathway, the apparatus comprising a frame including a set of wheels, a salt dispenser, a servo attached to the salt dispenser, one or more motors, the motors attached to at least one of the set of wheels, and a microcontroller communicatively coupled to the servo and the one or more motors, wherein the microcontroller instructs the servo to operate the salt dispenser and activates the one or more motors to drive the at least one of the set of wheels.

An autonomous vehicle can classify and prioritize agent of interest (AOI) objects located around the autonomous vehicle to manage computational resources. An example method performed by an autonomous vehicle includes determining, based on a location of the autonomous vehicle and based on a map, an area in which the autonomous vehicle is operated, determining, based on sensor data received from sensors located on or in the autonomous vehicle, attributes of objects located around the autonomous vehicle, where the attributes include information that describes a status of the objects located around the autonomous vehicle, selecting, based at least on the area, a classification policy that includes a plurality of rules that are associated with a plurality of classifications to classify the objects, and for each of the objects located around the autonomous vehicle: monitoring an object according to a classification of the object based on the classification policy.

A mobile platform includes at least one first sensor mounted at a first position on the mobile platform and at least one second sensor mounted at a second position on the mobile platform, where the first position is offset from the second position. The at least one first sensor is configured to capture first data measurements of plants in the growing area. The at least one second sensor is configured to capture second data measurements of the plants in the growing area. Each of the first and second data measurements is associated with a three-dimensional position within the growing area and a time. The first and second sensors are configured to generate at least one common type of data measurement such that at least some of the first and second data measurements represent stereo-spatio-temporal data measurements of the plants in the growing area.