An ultra-wideband, UWB, receiver module (213) comprising: an antenna for wirelessly receiving UWB signalling from a UWB transmitter module (212) and a processor. The processor is configured to: determine a channel impulse response, CIR, (519) of the wirelessly received UWB signalling, wherein the CIR comprises a plurality of channel taps each having a tap-response-value; identify a predetermined feature (520) in the CIR and an associated channel tap; and based on the channel tap that is associated with the identified feature (520) in the CIR (519), synchronize the UWB receiver module (213) for reception of subsequent UWB signalling.

Example embodiments relate to techniques for detecting adverse road conditions using radar. A computing device may generate a first radar representation that represents a field of view for a radar unit coupled to a vehicle and during clear weather conditions and store the first radar representation in memory. The computing device may receive radar data from the radar unit during navigation of the vehicle on a road and determine a second radar representation based on the radar data. The computing device may also perform a comparison between the first radar representation and the second radar representation and determine a road condition for the road based on the comparison. The road condition may represent a quantity of precipitation located on the road and provide control instructions to the vehicle based on the road condition for the road.

The disclosure relates to a method of providing a current local environment status map for a motor vehicle, and to a motor vehicle and a system for carrying out the method. The method includes generating own driving situation data which describe a current, position-related vehicle parameter of the motor vehicle, and generating environment situation data which describe a current arrangement of a further motor vehicle located in a predefined environment of the motor vehicle. The method also includes generating, based on these data, a vehicle environment map which describes a current local traffic situation in the predefined environment. The further vehicle environment maps of the environment of the vehicle are received from at least one other the further motor vehicle and are combined with the generated vehicle environment map using a map data evaluation criterion in order to generate an improved current local environment status map for the motor vehicle.

Disclosed herein is a system and method for facilitating estimation of a spatial profile of an environment based on a light detection and ranging (LiDAR) based technique. By repurposing the optical energy for communications needs, the present disclosure facilitates spatial profile estimation by optical means while facilitating free-space optical communication.

A LADAR device, including: a rangefinder; a signal transmission module; a power transmission module; a mechanical rotating part; a housing; a signal processing board; and a timing module. The signal transmission module includes at least one optical communication transmitter and one optical communication receiver. The power transmission module includes coupled magnet rings and communicates with the signal transmission module through electromagnetic induction to achieve wireless power transmission. The mechanical rotating part is adapted to drive the rangefinder to rotate axially in 360 degrees. The rangefinder is disposed on the housing, and includes a laser, an emitting lens assembly, a receiving sensor, and a receiving lens. The emitting lens assembly includes a first accommodation space and the laser is disposed in the first accommodation space. The receiving lens includes a second accommodation space and the receiving sensor is disposed in the second accommodation space.

Examples disclosed herein relate to an autonomous driving system in an ego vehicle. The autonomous driving system includes a radar system configured to detect and identify a target in a path and a surrounding environment of the ego vehicle. The autonomous driving system also includes a sensor fusion module configured to receive radar data on the identified target from the radar system and compare the identified target with one or more targets identified by a plurality of perception sensors that are geographically disparate from the radar system. Other examples disclosed herein include a method of operating the radar system in the autonomous driving system of the ego vehicle.

Examples are disclosed relating to performing denoising and adaptive precision control on time-of-flight sensor data using noise metrics. One example provides a computing system, comprising, a logic machine, and a storage machine holding instructions executable by the logic machine to obtain time-of-flight (ToF) image data comprising a plurality of pixels, for each pixel of the ToF image data, determine one or more noise metrics by applying a spatial kernel, segment the ToF image data based on the one or more noise metrics to obtain differently classified pixels, during a denoising phase, process pixels of a first classification differently than pixels of a second classification, after the denoising phase, determine a depth image, and output the depth image.

An electronic device may include a set of antenna panels (APs) that transmit and receive signals within a set of signal beams. A proximity sensor such as a radar sensor may gather sensor data indicative of the position an external object. The device may select an AP and a beam that maximize wireless performance in communicating with a base station while also complying with the radio-frequency exposure (RFE). The device may select the AP and the beam based on the sensor data, per-panel and per-beam projected RFE values, antenna port RFE characteristics, per-panel and per-beam transmit power limits, per-beam transmit power backoffs, an RFE lookup table, regulatory RFE limits, and antenna performance metrics. The device may transmit an RFE report to the base station that identifies some or all of this information for use in updating scheduling for the device.

Methods, systems, and devices for wireless communication are described. A communication device (e.g., a vehicle) in wireless communications system (e.g., a cellular-vehicle-to-everything (V2X) system) may support adaptive radar transmissions based on information received in a public safety message. The communication device may use information included in the public safety message to adapt radar transmissions to enable timely detection of vulnerable road users (VRUs). In some examples, based on a location and a velocity estimate provided in the public safety message, the communication device may adjust the radar transmissions to experience a trade-off between range and velocity estimation performance. Additionally or alternatively, based on positional accuracy estimates provided in the public safety message, the communication device may adjust the radar transmissions to improve beamforming. By adapting the radar transmissions, the communication device may experience low latency and high reliability for VRU collision warnings in the C-V2X system.

Provided are methods, systems, and computer program products for machine-learning based point cloud alignment classification. An example method may include: obtaining at least two light detection and ranging (LiDAR) point clouds; processing the at least two LiDAR point clouds using at least one classifier network; obtaining at least one output dataset from the at least one classifier network; determining that the at least two LiDAR point clouds are misaligned based on the at least one output dataset; and performing a first action based on the determining that the at least two LiDAR point clouds are misaligned.