Abstract: Aspects of the disclosure provide for controlling an autonomous vehicle. For example, a set of potential stopping locations may be identified based on a current location of an autonomous vehicle. A set of potential states may be identified for the autonomous vehicle. A fallback cost for each potential state of the set of potential states to reach each potential stopping location of the set of potential stopping locations may be determined. A trajectory for an autonomous vehicle may be planned based on a route to a destination and the determined fallback costs. The autonomous vehicle may be controlled according to at least a portion of the trajectory.

Abstract: Systems, methods, and other embodiments described herein relate to detecting reflection artifacts in a point cloud. In one embodiment, a method includes generating a first convex hull for defining an outline of a first cluster of points in a depth image and generating a second convex hull for defining an outline of a second cluster of points in the depth image. The first cluster of points originates from a point cloud and the second cluster of points originates from the point cloud. The method includes determining whether the second cluster of points is a reflection artifact based on identifying if a portion of the second convex hull is enclosed in the first convex hull and a range of the second convex hull is larger than a range of the first convex hull.

Abstract: Systems, methods, controllers, and techniques for addressing the parallax occlusion effect caused by non-collocated sensors are disclosed. A controller is configured to fuse image data received from an imaging device and depth data received from a depth sensor to form a mesh, project a ray from the imaging device to a pixel of the image data fused with a point of the depth data forming the mesh, determine an occlusion boundary surface within the depth data, and in response to determining that the ray intersects the occlusion boundary surface, determine that the imaging device is occluded from a fused point in the mesh.

Abstract: System, methods, and other embodiments described herein relate to tracking dynamic objects in a surrounding environment of a vehicle. In one embodiment, a method includes, in response to acquiring sensor data from at least one sensor, generating a current occupancy map that indicates locations of occupied grid cells as identified by the sensor data. The method includes updating a difference map according to the current occupancy map. The difference map encodes temporal changes in relation to prior states of occupancy of the grid cells to track dynamic objects in the surrounding environment over a defined temporal horizon. The method includes computing dynamics of the dynamic objects according to the difference map. The method includes providing the dynamics to at least one vehicle system within the vehicle.

Abstract: System, methods, and other embodiments described herein relate to tracking dynamic objects in a surrounding environment of a vehicle. In one embodiment, a method includes, in response to acquiring sensor data from at least one sensor, generating a current occupancy map that indicates locations of occupied grid cells as identified by the sensor data. The method includes updating a difference map according to the current occupancy map. The difference map encodes temporal changes in relation to prior states of occupancy of the grid cells to track dynamic objects in the surrounding environment over a defined temporal horizon. The method includes computing dynamics of the dynamic objects according to the difference map. The method includes providing the dynamics to at least one vehicle system within the vehicle.