Motional Ad LLC

Thomas Koelbaek Jespersen

Yu Pan

Jiaxin Li

Abbas Shikari

Jiong Yang

Marc Dominik Heim

Juraj Kabzan

Boaz Cornelis Floor

In some aspects and/or embodiments, systems, methods, and computer program products described herein include and/or implement technology for encoding dynamic homotopy constraints in spatio-temporal grids, including a method comprising: determining a plurality of dynamic homotopy constraints associated with a scenario involving a vehicle in an environment; embedding the plurality of dynamic homotopy constraints in a plurality of spatio-temporal grids, where each spatio-temporal grid includes individual grids for each timestep of a prediction horizon, generating a plurality of trajectories based on the plurality of dynamic homotopy constraints embedded in the plurality of spatio-temporal grids; selecting a particular trajectory from among the plurality of trajectories generated; and controlling the vehicle in the environment based on the particular trajectory selected from among the plurality of trajectories.

Encoding homotopy constraints in spatio-temporal grids

Among other things, techniques are described for spatially and temporally consistent ground modelling with information fusion. A vehicle location and orientation is obtained and an instantaneous ground height estimation is obtained for anchor cells of a grid map of the vehicle based on the obtained vehicle location and orientation. A pseudo ground height associated with non-anchor cells of the grid map is computed. A Bayesian filtering framework is used to generate a final ground height estimate for the anchor cells and the non-anchor cells based on the instantaneous ground height estimation for the anchor cells, the pseudo ground height associated with non-anchor cells, an estimated ground height from a previous timestamp, or any combinations thereof. The vehicle operates according to the final ground height estimate.

Spatially and temporally consistent ground modelling with information fusion

Techniques are provided for foreground extraction from a point cloud (e.g., a LiDAR point cloud) using surface fitting. In an embodiment, one or more processors of a vehicle can receive point cloud data from one or more vehicle sensors. The one or more processors can identify points in the point cloud data as foreground points or ground points using a spatial Kalman filter to capture changes in the terrain. A route or trajectory in a driving area for the vehicle can be generated using the identified foreground points and ground points. A vehicle controller can control the vehicle while the vehicle is traveling on the route or trajectory in the driving area.

Foreground extraction using surface fitting

Among other things, techniques are described for obtaining a range image related to a depth sensor of a vehicle operating in an environment. A first data point is identified in the range image with an intensity at or below a first intensity threshold. A first number of data points are determined in the range image that have an intensity at or above a second intensity threshold in a first region of the range image. Then, it is determined whether the first number of data points is at or above a region number threshold. The first data point is removed from the range image if the first number of data points is at or above the region number threshold. Operation of the vehicle is then facilitated in the environment based at least in part on the range image. Other embodiments may be described or claimed.

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Singapore, Singapore

Thomas Koelbaek Jespersen