Abstract: Exemplary embodiments described herein include systems and method to remove tracks from a tracker when the tracks are within an occluded region, including defining a map of a driving area; defining an occlusion area within the map; detecting an object using sensor data from one or more sensors; determining that the object entered the occlusion area; creating an estimated object location for the object within the occlusion area; dropping the estimated object location for the object when the occlusion area is cleared.

Abstract: Exemplary embodiments include methods and systems to adjust a driving path in a planner-map, including: identifying a driving surface; determining a path on the driving surface; identifying one or more objects relative to the driving surface using one or more sensors; detecting one or more occlusion areas from sensor data from the one or more sensors; tracking one or more tracks of one or more objects within the one or more occlusion areas; adding uncertainty to the one or more tracks within the one or more occlusion areas; and adjusting the path on the driving surface based on the uncertainty of the one or more tracks within the one or more occlusion areas.

Abstract: Exemplary embodiments include systems and methods to maintain tracking of an object that passes into an occluded area, including defining a map of a driving area; defining one or more occlusion areas within the map; detecting an object using sensor data from one or more sensors; creating an object track for the object detected using sensor data; determining that the object track entered one of the one or more occlusion areas; and maintaining the object track while the object track remains in the one or more occlusion areas.

Abstract: Systems and methods for detecting a portion of an environment of a vehicle are provided. The method may comprise generating, using one or more sensors coupled to a vehicle, environment data from an environment of the vehicle, wherein the environmental data comprises one or more of the following: ground LiDAR data from the environment; camera data from the environment; and path data corresponding to a change in position of one or more other vehicles within the environment. The method may comprise inputting the environmental data into a machine learning model trained to generate a heatmap, and, using a processor, based on the environmental data, determining a portion of the environment, wherein the portion of the environment comprises an area having a likelihood, greater than a minimum threshold, of being adjacent to one or more pavement markings, and generating the heatmap, wherein the heatmap corresponds to the portion of the environment.

Abstract: This disclosure provides methods and systems for dynamically creating a trajectory for navigating a vehicle. The method may include receiving sensor data from at least one sensor of the autonomous vehicle, the sensor data representative of a driving surface in a field of view of the autonomous vehicle; segmenting a portion of the driving surface in the field of view of the autonomous vehicle by determining nominal path based at least in part on the image data; assigning a plurality of nodes to at least a portion of the nominal path; associating the plurality of the nodes assigned to the nominal path with a line to generate at least one segmentation polyline; determining updated nominal path by fitting the each of the plurality of segmentation lines to the nominal path; generating a trajectory based on the updated nominal path; and navigating the autonomous vehicle according to the trajectory.

Abstract: This disclosure provides methods and systems for detecting and tracking objects in an environment of an autonomous vehicle.

Abstract: This disclosure provides methods and systems for generating a training set for a neural network configured to generate candidate trajectories for an autonomous vehicle, comprising: receiving a set of sensor data representative of one or more portions of a plurality of objects in the environment of the autonomous vehicle; for each object, calculating a representative box enclosing the object, the representative box having portions comprising corners, edges, and planes; for each representative box, calculating at least one vector into the representative box from a position on the autonomous vehicle; for each vector, calculating a first and second corner position of the representative box, an edge of the representative box, and a plane of the representative box; determining the highest confidence corners, edge, and plane of each representative based on calculation from the at least one vector; and generating a training set including the highest confidence corners, edge, and plane of each representative box.