Abstract: This disclosure provides systems and methods for controlling a vehicle. The method comprises receiving data from a set of sensors, wherein the data represents objects or obstacles in an environment of the autonomous vehicle; identifying objects or obstacles from the received data; determining multiple sets of attributes of the objects or obstacles, wherein each set of attributes of the objects or obstacles are determined based on data received by an individual sensor; determining a candidate trajectory for the autonomous vehicle based on the multiple sets of attributes of the objects or obstacles; and controlling the autonomous vehicle according to the candidate trajectory.

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.

Abstract: This disclosure provides systems and methods for controlling a vehicle based on a combination of high precision detection and high recall detection. The disclosed systems and methods can efficiently generate trajectories by reducing duplicate detection or duplicate calculation of objects or obstacles of common and known object types and objects or obstacles without class identification.

Abstract: This disclosure provides systems and methods for controlling a vehicle. The method comprises receiving data from a set of sensors, wherein the data represents objects or obstacles in an environment of the autonomous vehicle; identifying objects or obstacles from the received data; determining multiple sets of attributes of the objects or obstacles, wherein each set of attributes of the objects or obstacles are determined based on data received by an individual sensor; determining a candidate trajectory for the autonomous vehicle based on the multiple sets of attributes of the objects or obstacles; and controlling the autonomous vehicle according to the candidate trajectory.

Abstract: This disclosure provides systems and methods for controlling a vehicle. The method comprises receiving data from a set of sensors, wherein the data represents objects or obstacles in an environment of the autonomous vehicle; determining attributes of each of the objects or obstacles based on the received data from the set of sensors; integrating the attributes of the each of the objects or obstacles; identifying objects or obstacles based on the integrated attributes; determining a candidate trajectory for the autonomous vehicle to avoid the objects or obstacles; and controlling the autonomous vehicle according to the candidate trajectory.

Abstract: This disclosure provides systems and methods for path planning by a planner of an autonomous vehicle. The method may include receiving by the planner perception data from a perception module, wherein the perception data comprises tracking or predicted object data associated with objects or obstacles in an environment of the autonomous vehicle, and wherein the tracking or predicted object data are determined based on high recall detection data and high precision detection data, generating by the planner a trajectory for controlling the autonomous vehicle based on the perception data received from the perception module, and transmitting to a controller of the autonomous vehicle the trajectory such that the autonomous vehicle is navigated by the controller to a destination.

Abstract: This disclosure provides systems and methods for detecting and tracking objects or obstacles in an environment of an autonomous vehicle. The method may include receiving data from a set of sensors, wherein the data represents objects or obstacles in an environment of the autonomous vehicle; and using a processor: generating high precision detection data based on the received data; identifying, from the high precision detection data, a set of objects that are classifiable by at least one known classifier; generating high recall detection data based on the received data; identifying from the high recall detection data a set of obstacles; and performing an operation on the high precision detection data of the objects and the high recall detection data of the obstacles, based on a status of the autonomous vehicle or based on one or more characteristics of the objects or the obstacles.

Abstract: This disclosure provides methods and systems for dynamically detecting and tracking objects in an environment of an autonomous vehicle. In some embodiments, the method comprises: receiving image data from sensors of the autonomous vehicle, the image data comprising a plurality of images representative of objects in a field of view of the autonomous vehicle; detecting the objects in the plurality of images through an object detector; generating image embeddings for the objects detected in the plurality of images; determining similarity scores of the image embeddings of the objects that are detected in images received from two or more different sensors; identifying the objects that are detected in the images received from the two or more different sensors as a candidate object for tracking, if the objects have a similarity score of the image embeddings equal to or greater than a threshold value; and initializing a track for the candidate object.

Abstract: A time delay mechanism is provided for a circuit breaker stored energy assembly including a mount, a spring coupled to the mount, at least one charging mechanism for charging the spring to store energy, at least one actuator for releasing the stored energy, and a drive assembly for transferring the stored energy into movement of the circuit breaker operating mechanism. First and second trip shafts of the time delay mechanism are pivotably coupled to the mount. Linking elements interconnect the first and second trip shafts. A trip catch and a drive lever are coupled to the first trip shaft. The linking elements and a damper, which is connected to the drive lever, contribute to a delay from a first time that the first trip shaft initially moves, to a second time that the second trip shaft moves to release the trip catch. The damper is adjustable to adjust the delay.

Abstract: A time delay mechanism is provided for a circuit breaker stored energy assembly including a mount, a spring coupled to the mount, at least one charging mechanism for charging the spring to store energy, at least one actuator for releasing the stored energy, and a drive assembly for transferring the stored energy into movement of the circuit breaker operating mechanism. First and second trip shafts of the time delay mechanism are pivotably coupled to the mount. Linking elements interconnect the first and second trip shafts. A trip catch and a drive lever are coupled to the first trip shaft. The linking elements and a damper, which is connected to the drive lever, contribute to a delay from a first time that the first trip shaft initially moves, to a second time that the second trip shaft moves to release the trip catch. The damper is adjustable to adjust the delay.