Abstract: Approaches presented herein provide for high definition (HD) map data generation from standard definition (SD) map data. Geometric properties may be extracted from the SD map data to form a geometric representation. The geometric representation may be processed to determine locations to insert traffic management components to simulate traffic patterns using one or more rule-based approaches. The simulated traffic patterns may be used to generate a road layer level mapping of one or more segments of the SD map data that can be converted into HD map data.

Abstract: According to an aspect of an embodiment, operations may comprise accessing high definition (HD) map data of a region, presenting, via a user interface, information describing the HD map data, receiving instructions, via the user interface, for modifying the HD map data by adding one or more synthetic objects to locations in the HD map data, modifying the HD map data based on the received instructions, and generating a synthetic track in the modified HD map data comprising, for each of one or more vehicle poses, generated synthetic sensor data based on the one or more synthetic objects in the modified HD map data.

Abstract: According to an aspect of an embodiment, operations may comprise accessing high definition (HD) map data of a region, presenting, via a user interface, information describing the HD map data, receiving instructions, via the user interface, for modifying the HD map data by adding one or more synthetic objects to locations in the HD map data, modifying the HD map data based on the received instructions, and generating a synthetic track in the modified HD map data comprising, for each of one or more vehicle poses, generated synthetic sensor data based on the one or more synthetic objects in the modified HD map data.

Abstract: The autonomous vehicle generates an overlapped image by overlaying HD map data over sensor data and rendering the overlaid images. The visualization process is repeated as the vehicle drives along the route. The visualization may be displayed on a screen within the vehicle or at a remote device. The system performs reverse rendering of a scene based on map data from a selected point. For each line of sight originating at the selected point, the system identifies the farthest object in the map data. Accordingly, the system eliminates objects obstructing the view of the farthest objects in the HD map as viewed from the selected point. The system further allows filtering of objects using filtering criteria based on semantic labels. The system generates a view from the selected point such that 3D objects matching the filtering criteria are eliminated from the view.

Abstract: The autonomous vehicle generates an overlapped image by overlaying HD map data over sensor data and rendering the overlaid images. The visualization process is repeated as the vehicle drives along the route. The visualization may be displayed on a screen within the vehicle or at a remote device. The system performs reverse rendering of a scene based on map data from a selected point. For each line of sight originating at the selected point, the system identifies the farthest object in the map data. Accordingly, the system eliminates objects obstructing the view of the farthest objects in the HD map as viewed from the selected point. The system further allows filtering of objects using filtering criteria based on semantic labels. The system generates a view from the selected point such that 3D objects matching the filtering criteria are eliminated from the view.

Abstract: The autonomous vehicle generates an overlapped image by overlaying HD map data over sensor data and rendering the overlaid images. The visualization process is repeated as the vehicle drives along the route. The visualization may be displayed on a screen within the vehicle or at a remote device. The system performs reverse rendering of a scene based on map data from a selected point. For each line of sight originating at the selected point, the system identifies the farthest object in the map data. Accordingly, the system eliminates objects obstructing the view of the farthest objects in the HD map as viewed from the selected point. The system further allows filtering of objects using filtering criteria based on semantic labels. The system generates a view from the selected point such that 3D objects matching the filtering criteria are eliminated from the view.

Abstract: According to an aspect of an embodiment, operations may comprise accessing high definition (HD) map data of a region, presenting, via a user interface, information describing the HD map data, receiving instructions, via the user interface, for modifying the HD map data by adding one or more synthetic objects to locations in the HD map data, modifying the HD map data based on the received instructions, and generating a synthetic track in the modified HD map data comprising, for each of one or more vehicle poses, generated synthetic sensor data based on the one or more synthetic objects in the modified HD map data.

Abstract: The autonomous vehicle generates an overlapped image by overlaying HD map data over sensor data and rendering the overlaid images. The visualization process is repeated as the vehicle drives along the route. The visualization may be displayed on a screen within the vehicle or at a remote device. The system performs reverse rendering of a scene based on map data from a selected point. For each line of sight originating at the selected point, the system identifies the farthest object in the map data. Accordingly, the system eliminates objects obstructing the view of the farthest objects in the HD map as viewed from the selected point. The system further allows filtering of objects using filtering criteria based on semantic labels. The system generates a view from the selected point such that 3D objects matching the filtering criteria are eliminated from the view.

Abstract: The autonomous vehicle generates an overlapped image by overlaying HD map data over sensor data and rendering the overlaid images. The visualization process is repeated as the vehicle drives along the route. The visualization may be displayed on a screen within the vehicle or at a remote device. The system performs reverse rendering of a scene based on map data from a selected point. For each line of sight originating at the selected point, the system identifies the farthest object in the map data. Accordingly, the system eliminates objects obstructing the view of the farthest objects in the HD map as viewed from the selected point. The system further allows filtering of objects using filtering criteria based on semantic labels. The system generates a view from the selected point such that 3D objects matching the filtering criteria are eliminated from the view.

Abstract: The autonomous vehicle generates an overlapped image by overlaying HD map data over sensor data and rendering the overlaid images. The visualization process is repeated as the vehicle drives along the route. The visualization may be displayed on a screen within the vehicle or at a remote device. The system performs reverse rendering of a scene based on map data from a selected point. For each line of sight originating at the selected point, the system identifies the farthest object in the map data. Accordingly, the system eliminates objects obstructing the view of the farthest objects in the HD map as viewed from the selected point. The system further allows filtering of objects using filtering criteria based on semantic labels. The system generates a view from the selected point such that 3D objects matching the filtering criteria are eliminated from the view.