Abstract: A vehicle safety system and technique are described. In one example, a vehicle safety system communicably coupled to a vehicle, comprises processing circuitry coupled to a camera unit and a LiDAR sensor, the processing circuitry to execute logic operative to analyze an image captured by one or more cameras to identify predicted regions of road damage, correlate LiDAR sensor data with the predicted regions of road damage, analyze the LiDAR sensor data correlated with the predicted regions of road damage to identify regions of road damage in three-dimensional space; and output one or more indications of the identified regions of road damage, wherein the processing circuitry is coupled to an interface to the vehicle, the processing circuitry to output an identification of road damage.

Abstract: Systems, devices, computer-implemented methods, and/or computer program products that facilitate dynamic curve speed control adaptive to road conditions. In one example, a system can comprise a process that executes computer executable components stored in memory. The computer executable components can comprise a curvature component, a road condition component, and a safety component. The curvature component can generate composite curvature data for a curve of a road preceding a vehicle using digital map data and lane marker data. The road condition component can generate friction data for a surface of the road using sensor data obtained from an on-board sensor of the vehicle. The safety component can determine a safe operational profile for traversing the curve using the composite curvature data and the friction data.

Abstract: A vehicle safety system and technique are described. In one example, a vehicle safety system communicably coupled to a vehicle, comprises processing circuitry coupled to a camera unit and a LiDAR sensor, the processing circuitry to execute logic operative to analyze an image captured by one or more cameras to identify predicted regions of road damage, correlate LiDAR sensor data with the predicted regions of road damage, analyze the LiDAR sensor data correlated with the predicted regions of road damage to identify regions of road damage in three-dimensional space; and output one or more indications of the identified regions of road damage, wherein the processing circuitry is coupled to an interface to the vehicle, the processing circuitry to output an identification of road damage.

Abstract: The present subject matter discloses a method for antenna activity detection in multi-antenna communication devices. In one embodiment, the method comprises computing a received signal strength indicator (RSSI) value for each of a plurality of antennas based on a sampled data associated with each of the antennas. The RSSI values may then be analyzed to identify an antenna having a highest RSSI value as a primary antenna and one or more antennas having the RSSI value less than the highest RSSI value as auxiliary antennas. Further, an RSSI difference for each of the auxiliary antennas is calculated and compared with a first threshold value to ascertain one or more potentially inactive antennas from among the auxiliary antennas. The potentially inactive antennas may then be further analyzed to identify one or more inactive antennas based at least in part on the RSSI value.

Abstract: The present subject matter discloses a system and a method for estimating a frequency offset in communication devices. In one embodiment, the method of estimating a frequency offset in a communication device comprises generating a reconstructed signal based at least in part on a channel impulse response (CIR) corresponding to a received signal. Further, a normalization matrix is determined for the reconstructed signal. Thereafter, based at least in part on the normalization matrix and the reconstructed signal, the frequency offset is estimated such that the frequency offset corresponds to a maximum normalized-correlation between the reconstructed signal and the received signal.

Abstract: The present subject matter discloses a method for antenna activity detection in multi-antenna communication devices. In one embodiment, the method comprises computing a received signal strength indicator (RSSI) value for each of a plurality of antennas based on a sampled data associated with each of the antennas. The RSSI values may then be analyzed to identify an antenna having a highest RSSI value as a primary antenna and one or more antennas having the RSSI value less than the highest RSSI value as auxiliary antennas. Further, an RSSI difference for each of the auxiliary antennas is calculated and compared with a first threshold value to ascertain one or more potentially inactive antennas from among the auxiliary antennas. The potentially inactive antennas may then be further analyzed to identify one or more inactive antennas based at least in part on the RSSI value.

Abstract: The present subject matter discloses a system and a method for estimating a frequency offset in communication devices. In one embodiment, the method of estimating a frequency offset in a communication device comprises generating a reconstructed signal based at least in part on a channel impulse response (CIR) corresponding to a received signal. Further, a normalization matrix is determined for the reconstructed signal. Thereafter, based at least in part on the normalization matrix and the reconstructed signal, the frequency offset is estimated such that the frequency offset corresponds to a maximum normalized-correlation between the reconstructed signal and the received signal.

Abstract: Described herein are various methods for a communication device (e.g., a mobile station) receiving an AQPSK modulated signal (e.g., a VAMOS signal) to estimate a subchannel power imbalance ratio (SCPIR). Advantageously, the methods are not computationally complex and do not suffer from poor numerical performance.

Abstract: Described herein are various methods for a communication device (e.g., a mobile station) receiving an AQPSK modulated signal (e.g., a VAMOS signal) to estimate a subchannel power imbalance ratio (SCPIR). Advantageously, the methods are not computationally complex and do not suffer from poor numerical performance.