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    Validation of global navigation satellite system location data with other sensor data
    2.
    发明授权
    Validation of global navigation satellite system location data with other sensor data 有权

    公开(公告)号：US11340355B2

    公开(公告)日：2022-05-24

    申请号：US16564303

    申请日：2019-09-09

    申请人： DeepMap Inc.

    发明人： Mark Damon Wheeler ,  Jeff Adachi ,  Chen Chen ,  Gregory William Coombe ,  Di Zeng

    IPC分类号： G01S19/39 ,  G05D1/02 ,  G01S19/47 ,  G01S19/46 ,  G01S19/52 ,  G01C21/32

    摘要： A vehicle computing system validates location data received from a Global Navigation Satellite System receiver with other sensor data. In one embodiment, the system calculates velocities with the location data and the other sensor data. The system generates a probabilistic model for velocity with a velocity calculated with location data and variance associated with the location data. The system determines a confidence score by applying the probabilistic model to one or more of the velocities calculated with other sensor data. In another embodiment, the system implements a machine learning model that considers features extracted from the sensor data. The system generates a feature vector for the location data and determines a confidence score for the location data by applying the machine learning model to the feature vector. Based on the confidence score, the system can validate the location data. The validated location data is useful for navigation and map updates.

    Navigable boundary generation for autonomous vehicles
    3.
    发明授权
    Navigable boundary generation for autonomous vehicles 有权

    公开(公告)号：US11320273B2

    公开(公告)日：2022-05-03

    申请号：US16721516

    申请日：2019-12-19

    申请人： DeepMap Inc.

    发明人： Derek Thomas Miller ,  Lin Yang ,  Mark Damon Wheeler

    IPC分类号： G01C21/32 ,  G05D1/00 ,  G08G1/0968 ,  G05D1/02 ,  G08G1/01

    摘要： A system accesses a three-dimensional map of a geographic region and generates a two-dimensional projection of the road based on the three-dimensional map. The two-dimensional projection comprises a plurality of points along the road and each point is assigned a score measuring a navigability of the point. Based on the navigability score of each point and history of vehicle positions on the road, the system identifies a plurality of navigable points on the two-dimensional projection of the road. Based on the plurality of navigable points, the system determines a navigable surface corresponding to a physical area over which a vehicle may safely navigate and navigable surface boundaries surrounding that area. The navigable surface area and boundaries on the two-dimensional projection are converted into a three-dimensional representation, which the system uses to generate an updated three-dimensional map of the geographic region.

    OCCUPANCY MAP UPDATES BASED ON SENSOR DATA COLLECTED BY AUTONOMOUS VEHICLES
    4.
    发明申请
    OCCUPANCY MAP UPDATES BASED ON SENSOR DATA COLLECTED BY AUTONOMOUS VEHICLES 有权

    公开(公告)号：US20210254983A1

    公开(公告)日：2021-08-19

    申请号：US17079057

    申请日：2020-10-23

    申请人： DeepMap Inc.

    发明人： Mark Damon Wheeler ,  Xiaqing Wu

    IPC分类号： G01C21/32 ,  G06F16/29 ,  G06K9/00 ,  G06K9/03 ,  G06K9/62 ,  H04L29/08 ,  H04L29/14 ,  G01C21/00 ,  G01C21/36 ,  G05D1/00 ,  G06T17/05 ,  B60W30/095 ,  B60W40/02 ,  G01C21/30 ,  G05D1/02

    摘要： An online system builds a high definition (HD) map for a geographical region based on sensor data captured by a plurality of autonomous vehicles driving through a geographical region. The autonomous vehicles detect map discrepancies based on differences in the surroundings observed using sensor data compared to the high definition map and send messages describing these map discrepancies to the online system. The online system updates existing occupancy maps to improve the accuracy of the occupancy maps (OMaps), and to thereby improve passenger and pedestrian safety. While vehicles are in motion, they can continuously collect data about their surroundings. When new data is available from the various vehicles within a fleet, this can be updated in a local representation of the occupancy map and can be passed to the online HD map system (e.g., in the cloud) for updating the master occupancy map shared by all of the vehicles.

    High definition map updates with vehicle data load balancing
    5.
    发明授权
    High definition map updates with vehicle data load balancing 有权

    公开(公告)号：US10429194B2

    公开(公告)日：2019-10-01

    申请号：US15858987

    申请日：2017-12-29

    申请人： DeepMap Inc.

    发明人： Mark Damon Wheeler

    IPC分类号： B60R16/037 ,  G01C21/32 ,  G06F16/29 ,  G01C21/36 ,  G05D1/00 ,  G06K9/00 ,  G06T17/05 ,  B60W30/095 ,  B60W40/02 ,  G01C21/30 ,  G05D1/02 ,  H04L29/08 ,  G06K9/03 ,  G06K9/62 ,  H04L29/14

    摘要： An online system builds a high definition (HD) map for a geographical region based on sensor data captured by a plurality of autonomous vehicles driving through a geographical region. The autonomous vehicles detect map discrepancies based on differences in the surroundings observed using sensor data compared to the high definition map and send messages describing these map discrepancies to the online system. The online system ranks the autonomous vehicles based on factors including an upload rate indicating how often the vehicle was used providing data to the online system. The sensor data from vehicles is uploaded to the online system (e.g., in the cloud) to create the HD map while spreading the burden of uploading this data as evenly as possible across a fleet of vehicles. Data uploads are expensive and time consuming, so the system makes this negligible for each vehicle by balancing/managing the uploads carefully across the fleet.

    Vector data encoding of high definition map data for autonomous vehicles
    7.
    发明授权
    Vector data encoding of high definition map data for autonomous vehicles 有权

    公开(公告)号：US10359518B2

    公开(公告)日：2019-07-23

    申请号：US15857383

    申请日：2017-12-28

    申请人： DeepMap Inc.

    发明人： Mark Damon Wheeler

    IPC分类号： G06K9/00 ,  G01S17/89 ,  H04N19/17 ,  G01C21/30 ,  G01C21/32 ,  G06T9/20 ,  G01S17/02 ,  G01S17/93 ,  G05D1/02 ,  G06F16/174 ,  B60R11/04 ,  G01C11/02

    摘要： Embodiments relate to methods for efficiently encoding sensor data captured by an autonomous vehicle and building a high definition map using the encoded sensor data. The sensor data can be LiDAR data which is expressed as multiple image representations. Image representations that include important LiDAR data undergo a lossless compression while image representations that include LiDAR data that is more error-tolerant undergo a lossy compression. Therefore, the compressed sensor data can be transmitted to an online system for building a high definition map. When building a high definition map, entities, such as road signs and road lines, are constructed such that when encoded and compressed, the high definition map consumes less storage space. The positions of entities are expressed in relation to a reference centerline in the high definition map. Therefore, each position of an entity can be expressed in fewer numerical digits in comparison to conventional methods.

    LIDAR AND CAMERA SYNCHRONIZATION
    8.
    发明申请
    LIDAR AND CAMERA SYNCHRONIZATION 审中-公开

    公开(公告)号：US20190120948A1

    公开(公告)日：2019-04-25

    申请号：US16163463

    申请日：2018-10-17

    申请人： DeepMap Inc.

    发明人： Lin Yang ,  Mark Damon Wheeler

    IPC分类号： G01S7/497 ,  G01S17/02 ,  G01S17/89 ,  G01S17/42 ,  H04N5/232 ,  H04N5/04 ,  G06T7/33 ,  G06T7/55 ,  G01C21/36

    摘要： A method and system for synchronizing a lidar and a camera on an autonomous vehicle. The system selects a plurality of track samples for a route including a lidar scan and an image. For each track sample, the system calculates a time shift by iterating many time deltas. For each time delta, the system adjusts a camera timestamp by that time delta, projects a lidar scan onto the image as a lidar projection according to the adjusted camera timestamp, and calculates an alignment score of the lidar projection for that time delta. The system defines the time shift for each track sample as the time delta with the highest alignment score. The system then models time drift of the camera compared to the lidar based on the calculated time shifts for the track samples and synchronizes the lidar and the camera according to the modeled time drift.