Abstract: According to an aspect of an embodiment, operations may comprise receiving a point cloud representing a region. The operations may also comprise identifying a cluster of points in the point cloud having a higher intensity than points outside the cluster of points. The operations may also comprise determining a bounding box around the cluster of points. The operations may also comprise identifying a traffic sign within the bounding box. The operations may also comprise projecting the bounding box to coordinates of an image of the region captured by a camera. The operations may also comprise employing a deep learning model to classify a traffic sign type of the traffic sign in a portion of the image within the projected bounding box. The operations may also comprise storing information regarding the traffic sign and the traffic sign type in a high definition (HD) map of the region.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: According to an aspect of an embodiment, operations may comprise receiving a point cloud representing a region. The operations may also comprise identifying a cluster of points in the point cloud having a higher intensity than points outside the cluster of points. The operations may also comprise determining a bounding box around the cluster of points. The operations may also comprise identifying a traffic sign within the bounding box. The operations may also comprise projecting the bounding box to coordinates of an image of the region captured by a camera. The operations may also comprise employing a deep learning model to classify a traffic sign type of the traffic sign in a portion of the image within the projected bounding box. The operations may also comprise storing information regarding the traffic sign and the traffic sign type in a high definition (HD) map of the region.

Abstract: 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.

Abstract: An HD map system represents landmarks on a high definition map for autonomous vehicle navigation, including describing spatial location of lanes of a road and semantic information about each lane, and along with traffic signs and landmarks. The system generates lane lines designating lanes of roads based on, for example, mapping of camera image pixels with high probability of being on lane lines into a three-dimensional space, and locating/connecting center lines of the lane lines. The system builds a large connected network of lane elements and their connections as a lane element graph. The system also represents traffic signs based on camera images and detection and ranging sensor depth maps. These landmarks are used in building a high definition map that allows autonomous vehicles to safely navigate through their environments.

Abstract: An HD map system represents landmarks on a high definition map for autonomous vehicle navigation, including describing spatial location of lanes of a road and semantic information about each lane, and along with traffic signs and landmarks. The system generates lane lines designating lanes of roads based on, for example, mapping of camera image pixels with high probability of being on lane lines into a three-dimensional space, and locating/connecting center lines of the lane lines. The system builds a large connected network of lane elements and their connections as a lane element graph. The system also represents traffic signs based on camera images and detection and ranging sensor depth maps. These landmarks are used in building a high definition map that allows autonomous vehicles to safely navigate through their environments.