Provided is an information processing apparatus that creates map information on the basis of sensor information obtained by an on-vehicle sensor. The information processing apparatus includes a creation section that creates a map of a surrounding area of a mobile body on the basis of sensor information acquired by one or more sensors mounted on the mobile body, a request section that issues an information request to an external apparatus on the basis of a state of the map created by the creation section, and a merge section that merges information acquired by the request section from the external apparatus with the created map. The request section issues an information request to the external apparatus on the basis of a condition of a dead angle included in the map created by the creation section.

A method and device for sensor data fusion for a vehicle as well as a computer program and a computer-readable storage medium are disclosed. At least one sensor device (S1) is associated with the vehicle (F), and in the method, fusion object data is provided representative of a fusion object (O.sub.F) detected in an environment of the vehicle (F); sensor object data is provided representative of a sensor object (O.sub.S) detected by the sensor device (S1) in the environment of the vehicle (F); indicator data is provided representative of an uncertainty in the determination of the sensor object data; reference point transformation candidates of the sensor object (O.sub.S) are determined depending on the indicator data; and an innovated fusion object is determined depending on the reference point transformation candidates.

A vehicle control system includes an automated driving control unit for controlling, based on surrounding environment information of a vehicle, driving, braking, and/or steering of the vehicle without depending on a driving operation of a driver; and a driving support control unit for supporting driving of the vehicle by the driver by controlling driving, braking, and/or steering of the vehicle. The control units are communicably connected. The driving support control unit can control, depending on a reception result of a signal received from the automated driving control unit, steering, and driving and/or braking of the vehicle based on the surrounding environment information without depending on the driving operation of the driver.

An apparatus comprising at least one interface to receive a signal identifying a second vehicle in proximity of a first vehicle; and processing circuitry to obtain a behavioral model associated with the second vehicle, wherein the behavioral model defines driving behavior of the second vehicle; use the behavioral model to predict actions of the second vehicle; and determine a path plan for the first vehicle based on the predicted actions of the second vehicle.

A system for a vehicle, which drives in an at least in-part-automated manner is configured to determine a control signal for a control system. The system includes a sensor, a planning module, and a monitoring module. The sensor is configured to detect an object in a surrounding area of the vehicle and store a corresponding object representation. The planning module is configured to determine, based to the stored object representation, a first trajectory and a first probability of collision of the first trajectory for the vehicle. The monitoring module is configured to perform one of following actions when the first probability of collision exceeds a predefined probability of collision: determine, using the planning module and based on the stored object representation, a further trajectory having a further probability of collision and a maximum deceleration of the further trajectory; or assess the stored object representation of the object using the sensor.

An apparatus and method are provided for controlling autonomous driving of a vehicle which may derive predicted paths of a pedestrian and a two-wheel vehicle during autonomous driving of the vehicle so as to minimize accidents. The method includes calculating first height information allocating a first gradient that descends in a proceeding direction of objects, including a vehicle and a pedestrian, from respective positions of the objects based on dynamic information of the objects, calculating second height information allocating a second gradient based on a probability that the pedestrian will occupy infrastructure, calculating final height information by fusing the first height information and the second height information, generating a predicted path of the pedestrian, determining a driving strategy of a host vehicle based on a predicted path of the host vehicle and the predicted path of the pedestrian.

A system for controlling an autonomous vehicle is provided. The system may include a first sensor system including a first sensor and a first data box. The first sensor system may be configured to determine a first vehicle control decision. The system may further include a second sensor system including a second sensor and a second data box. The second sensor system may be configured to determine a second vehicle control decision. The system further includes a controller configured to receive the first vehicle control decision and the second vehicle control decision from the first sensor system and the second sensor system; determine a priority ranking for the first vehicle control decision and the second vehicle control decision; select, based on the priority ranking, a vehicle control decision from the first vehicle control decision and the second vehicle control decision; and implement, responsive to the determining, the selected vehicle control decision.

Systems and methods for controlling the operation of an autonomous vehicle are disclosed herein. One embodiment performs traffic light detection at an intersection using a sensor-based traffic light detector to produce a sensor-based detection output, the sensor-based detection output having an associated first confidence level; performs traffic light detection at the intersection using a vehicle-to-infrastructure-based (V2I-based) traffic light detector to produce a V2I-based detection output, the V2I-based detection output having an associated second confidence level; performs one of (1) selecting as a final traffic-light-detection output whichever of the sensor-based detection output and the V2I-based detection output has a higher associated confidence level and (2) generating the final traffic-light-detection output by fusing the sensor-based detection output and the V2I-based detection output using a first learning-based classifier; and controls the operation of the autonomous vehicle based, at least in part, on the final traffic-light-detection output.

An apparatus of determining a position of a vehicle, may include a plurality of sensors to acquire raw data for vehicle information and surrounding information related to the vehicle, and a controller to generate a plurality of vehicle position point data based on the raw data, generate respective tracklets for the sensors by combining the plurality of vehicle position point data, fuse the tracklets for the sensors, and determine a final position of the vehicle using the fused tracklets for the sensors. The position is exactly estimated, and a computation amount is prevented from being excessively increased such that real-time position information is easily acquired

Sensor data is received from a plurality of sensors, where the plurality of sensors includes a first set of sensors and a second set of sensors, and at least a portion of the plurality of sensors are coupled to a vehicle. Control of the vehicle is automated based on at least a portion of the sensor data generated by the first set of sensors. Passenger attributes of one or more passengers within the autonomous vehicles are determined from sensor data generated by the second set of sensors. Attributes of the vehicle are modified based on the passenger attributes and the sensor data generated by the first set of sensors.