Abstract: Techniques for determining drivable area(s), parking location(s), or other incident areas in an environment are discussed herein. The drivable area(s), parking location(s), and/or other incident areas can be determined by a machine learned model. Training of the machine learned model can be based on sensor data and map data. The sensor data and the map data can be utilized to determine a representation (e.g., a top-down representation) of an environment. The representation can include at least road marking and velocity information associated with a dynamic object in the environment. The sensor data can be utilized to determine the dynamic object. The machine learned model can generate outputs including probabilities that elements of the outputs represent a drivable area, non-drivable area, a parking location, and/or an incident area. The outputs can be utilized to generate a trajectory. The trajectory can be utilized to control a vehicle to traverse the environment.

Abstract: Techniques for determining drivable area(s), parking location(s), or other incident areas in an environment are discussed herein. The drivable area(s), parking location(s), and/or other incident areas can be determined by a machine learned model. Training of the machine learned model can be based on sensor data and map data. The sensor data and the map data can be utilized to determine a representation (e.g., a top-down representation) of an environment. The representation can include at least road marking and velocity information associated with a dynamic object in the environment. The sensor data can be utilized to determine the dynamic object. The machine learned model can generate outputs including probabilities that elements of the outputs represent a drivable area, non-drivable area, a parking location, and/or an incident area. The outputs can be utilized to generate a trajectory. The trajectory can be utilized to control a vehicle to traverse the environment.