A track fusion method for an unmanned surface vehicle includes: (a) obtaining perception information of the unmanned surface vehicle, where the perception information includes GPS data information and radar data information; (b) pre-processing the radar data information to obtain target radar information; (c) constructing a track correlation model; and performing track correlation between the GPS data information and the target radar information based on the track correlation model; and (d) constructing a fusion data weight allocation model; and subjecting between the GPS data information and the target radar information correlated therewith to track fusion based on the fusion data weight allocation model. This application further provides a track fusion device for unmanned surface vehicles.

A driving support device includes a storage device storing a program and a hardware processor. The hardware processor executes the program stored in the storage device to perform driving support of a vehicle based on a detection result of at least one of a radar device and an imaging device mounted in the vehicle, determine whether the vehicle is traveling in an underpass which is a traffic route along which the vehicle is able to pass under an overlying structure, and suppress an operation of the driving support when the vehicle is determined to be traveling under the underpass.

The present disclosure relates to a driving control system and a method of controlling the same using sensor fusion between vehicles, and the driving control system allows vehicles to share sensor data, fuses the sensor data, matches the adjacent vehicle and the sensor data, improves recognition performance in respect to a periphery, controls driving in accordance with a change in peripheral environment and a traveling state of another vehicle, receives sensor data of another vehicle, converts a coordinate of sensor data of the matched vehicle, and fuses host vehicle sensor information, which makes it possible to improve recognition performance and accuracy in respect to a surrounding environment and object, enable stable autonomous driving, and improve stability of the vehicle.

This document describes techniques and systems to detect and localize NLOS objects using multipath radar reflections and map data. In some examples, a processor of radar system can identify a detection of an object using reflected EM energy and determine, using map data, whether a direct-path reflection associated with the detection is within a roadway. In response to determining that the direct-path reflection is not located within the roadway, the processor can determine whether a multipath reflection (e.g., a multipath range and multipath angle) associated with the detection is viable. In response to determining that the multipath reflection is viable, the processor can determine that the detection corresponds to an NLOS object. The processor can also provide the NLOS object as an input to an autonomous or semi-autonomous driving system of the vehicle, thereby improving the safety of such systems.

A method and apparatus with vehicle radar control is disclosed. An apparatus with vehicle radar control includes a radio frequency (RF) transceiver including a transmitting antenna array and a receiving antenna array, and at least one processor configured to collect environmental information of the vehicle, determine a radar mode of the vehicle based on the collected environmental information, generate one or more control signal configured to control one or more of the transmitting antenna array and the receiving antenna array based on the determined radar mode, and provide the generated one or more control signals to the RF transceiver, wherein one or more of the transmitting antenna array and the receiving antenna array operate according to the one or more generated control signals.

The technologies described herein relate to a domain adaptation system for sensor data. A computer-implemented model is trained using a set of training sensor data to facilitate classification of objects that are in the vicinity of an autonomous vehicle (AV). The set of training data corresponds to a first domain, such as firmware version of a sensor system, model of a sensor system, position of the sensor system on a vehicle, an environmental condition, etc. The set of training data is generated based upon pre-existing training data that corresponds to a second domain that is different from the first domain. Put differently, the pre-existing training data is transformed to correspond to the domain of a sensor system as it will be used on the AV.

A control system and method dynamically adjust radar parameters of a radar system on a platform. The method includes obtaining inputs including platform parameters, wherein the platform parameters includes speed and braking duration, and obtaining a characterization of driving behavior based on the inputs. Modifying the radar parameters is based on the inputs and the characterization, wherein the modifying includes changing a maximum range, and providing alerts to a driver of the platform is based on the radar system.

Examples disclosed herein relate to an autonomous driving system in an vehicle. The autonomous driving system includes a radar system configured to detect a target in a path and a surrounding environment of the vehicle and produce radar data with a first resolution that is gathered over a continuous field of view on the detected target. The system includes a super-resolution network configured to receive the radar data with the first resolution and produce radar data with a second resolution different from the first resolution using first neural networks. The system also includes a target identification module configured to receive the radar data with the second resolution and to identify the detected target from the radar data with the second resolution using second neural networks. Other examples disclosed herein include a method of operating the radar system in the autonomous driving system of the vehicle.

Methods and apparatuses disclosed within provide a solution to problems associated with sensing apparatus of an automated vehicle (AV) not being able to adequately evaluate risks associated with objects that are not characteristic with a given driving environment. A sensing apparatus tuned to drive at quickly down highway may not adequately identify risks associated with pedestrians walking along that highway. A solution to this problem involves configuring a sensing apparatus to operate in two different modes, for example, a highway mode and a pedestrian mode at virtually the same time. This may include allocating a first percentage of processing resources to a highway mode and allocating a second percentage of processing resources to a pedestrian mode. When objects along the highway are consistent with a pedestrian, additional processing resources of the sensing apparatus may be allocated to the pedestrian mode to reduce a risk of impacting the pedestrian.

A radome device for a radar sensor of a vehicle has a radome with a central region which is permeable to electromagnetic radiation from the radar sensor. The radome device includes a heating element for controlling the temperature of a central region of the radome, wherein a heat output can be introduced into the central region of the radome by way of electrical energy. The central region has a preferred accumulation region, within which precipitation from the surroundings of the vehicle preferably accumulates when the radome device is arranged on the vehicle as intended. An overall heat output that is different from the heat output can be introduced into the preferred accumulation region.