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Unified boundary machine learning model for autonomous vehicles
Patent published on the 2025-03-06 in WO under Ref WO2025048871 by AURORA OPERATIONS INC [US] (Chaabane Mohamed [us], Kaplan Benjamin [us], Litvin Yevgeni [us], O'hara Stephen [us], Vig Sean [us])
Abstract: A unified boundary machine learning model is capable of processing perception data received from various types of perception sensors on an autonomous vehicle to generate perceived boundaries of various semantic boundary types. Such perceived boundaries may then be used, for example, to control the autonomous vehicle, e.g., by generating a trajectory therefor. In some instances, the various semantic boundary types detectable by a unified boundary machine learning model may include at least a virt[...]
Our summary: Unified boundary machine learning model processes perception data from various sensors on autonomous vehicles to generate perceived boundaries for vehicle control, including virtual construction and other types of boundaries.
machine learning, autonomous vehicles, perception sensors, semantic boundaries
Patent
Integrated sensor assembly
Patent published on the 2025-03-06 in WO under Ref WO2025049123 by PLUSAI INC [US] (Deck James Wiley [us], Bharwani Murad Mehdi Mohammad [us])
Abstract: An integrated sensor assembly is described. The integrated sensor assembly can include a housing comprising a compartment. The housing can be arranged on a side surface of a cabin of an autonomous vehicle and within a threshold distance of a cabin roof of the autonomous vehicle. The assembly can include a first sensor arranged in the compartment, the first sensor configured to receive a first signal from a vehicle computer and collect information for mapping an environment of the autonomous vehi[...]
Our summary: Integrated sensor assembly with housing and compartment for sensors and light source in autonomous vehicle. Light source emits patterns for indicating hazards, turns, or autonomous mode towards the rear of the vehicle.
sensor assembly, housing, compartment, autonomous vehicle
Patent
Dynamic augmentation of autonomous vehicle map data using perceived lane elements
Patent published on the 2025-03-06 in WO under Ref WO2025048910 by AURORA OPERATIONS INC [US] (Kaplan Benjamin [us], O'hara Stephen [us], Ullmann Kevin [us])
Abstract: Discrepancies between mapped lanes on a roadway and perceived lanes perceived by the perception system of an autonomous vehicle may be identified and used to augment map data used by the autonomous vehicle, thereby enabling the autonomous vehicle to operate even in the event of the identified discrepancy.[...]
Our summary: Dynamic augmentation of autonomous vehicle map data using perceived lane elements, Identification of discrepancies between mapped lanes and perceived lanes, Augmentation of map data to enable vehicle operation in case of discrepancies.
autonomous vehicle, map data, perceived lane elements, discrepancies
Patent
Lidar sensor denoising for adverse conditions and/or nonsalient objects
Patent published on the 2025-03-06 in WO under Ref WO2025049190 by ZOOX INC [US] (Terefe Nehemia [us])
Abstract: Particulate matter, such as fog, snow, rain, steam, vehicle exhaust, debris (plastic bags), etc. may cause one or more sensor types to generate false positive solid surface detections. In particular, various depth measurements may be impeded by particulate matter. Identifying false positive return(s) may comprise clustering lidar points, determining differences in range indicated by two different lidar devices having lidar points in the cluster, determining first differences that are more negati[...]
Our summary: Lidar sensor denoising for adverse conditions and/or nonsalient objects by identifying and suppressing false positive solid surface detections caused by particulate matter such as fog, snow, rain, and debris.
lidar sensor, denoising, adverse conditions, false positive
Patent
Combining multiband signals in lidar systems
Patent published on the 2025-03-06 in WO under Ref WO2025049069 by SILC TECH INC [US] (Behroozpour Behnam [us], Asghari Mehdi [us])
Abstract: The LIDAR system outputs a system output signal and receives a system return signal that includes light from the system output signal that was reflected by an object located outside of the LIDAR system. The LIDAR system includes data lines that each carries a different preliminary channel signal. A selection of the preliminary channel signals is beating at a beat frequency. Each of the preliminary channel signals in the selection of the preliminary channel signals is generated from light include[...]
Our summary: Combining multiband signals in lidar systems involves utilizing bandpass filter components to process preliminary channel signals, resulting in the generation of channel signals on filtered data lines.
multiband signals, lidar systems, bandpass filter components, channel signal
Patent
Enhancement of the 3d outdoor positioning by augmenting a multitude of 3d imaging, lidar distance corrections and imu sensors
Patent published on the 2025-03-06 in WO under Ref WO2025046560 by BAJNAID MOHAMMADFAWZI [SA] (Bajnaid Mohammadfawzi [sa])
Abstract: Three-dimensional outdoor positioning can be considerably enhanced by using various 3D imaging techniques, LiDAR ranging measurements of surrounding landmarks, and Inertial Measurement Units (IMUs) reaching accuracies in the centimetres range. These technologies, in conjunction with global navigation satellite systems (GNSS) such as GPS, GLONASS, Galileo, and BeiDou, provide autonomous geo positioning. It is crucial to acknowledge the constraints of dead reckoning, which may result in mistakes i[...]
Our summary: Enhancement of outdoor positioning through 3D imaging, LiDAR corrections, and IMU sensors for accurate and reliable geo positioning. Integration of modern imaging techniques improves outdoor location precision and expands applications in autonomous navigation and geospatial analysis. Hardware system includes GNSS, INS, LiDAR, and imaging technologies for detailed mapping and analysis.
3D imaging, LiDAR, IMU, GNSS
Patent
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