See how tapered burner tubes with raised gas ports punched from inside out eliminate burrs, red
Regression on Doppler and azimuth records estimates rigid-body velocity and angular motion accurately even when elevation data is missing.
Vehicle strobe pulses and retroreflector patterns replace heavy radar to guide UAM aircraft to specific landing pads with precise optical navigation.
Marker and vehicle feature scans are combined to calibrate vehicle sensors accurately without guide rails or rollers.
Segmented beam intensities mapped to adjacent receivers let LiDAR detect weak and highly reflective surfaces without saturation.
Time-multiplexed transceiver control lets a LIDAR share ADCs across optical channels, reducing interference while preserving detection accuracy.
Correlation data convolution with Gaussian, Lorentzian, or polynomial functions reduces peak-fitting bias for more reliable LIDAR range and velocity measurement.
Calibration targets at energy supply stations let autonomous vehicles recalibrate perception sensors during automated resupply.
An impedance-matched interface unit lets vehicle radar sit behind fascia with lower wave attenuation, flexible placement, and cleaner exterior styling.
A trench sidewall conductivity layer stabilizes potential gradients in SPAD pixels, reducing charge diffusion and improving temporal resolution.
Piecewise inverse gain and intercept correction linearize compressed lidar ADC signals while avoiding lookup-table power overhead.
By splitting LiDAR detections into upper and lower spaces, the controller filters tunnel features and large vehicles from true obstacles.
External devices and onboard sensors are compared by reliability factors to recalibrate vehicle perception without stopping the vehicle.
Chaotic signal generation and frequency-domain phase analysis improve small-displacement LiDAR accuracy without high-speed modulators or faster ADCs.
Clusters repeated LiDAR returns from homogeneous reflective surfaces to flag specular false positives before driver assistance actions.
Real captured object and background radar data are probabilistically merged to create more realistic autonomous driving simulations.
A delayed-signal differential comparator extracts LiDAR pulse timing and amplitude without high-speed ADCs, reducing pulse pileup and noise sensitivity.