Abstract: In an example method, a computer system receives first data representing a plurality of scenarios for estimating a performance of a vehicle in conducting autonomous operations. Further, the computer system determines, for each of the scenarios: (i) a first metric indicating an observed performance of the vehicle in that scenario, where the first metric is determined based on at least one rule, and (ii) a second metric indicating a degree of information gain associated with that scenario. The computer system selects a subset of the scenarios based on the first metrics and the second metrics, and outputs second data indicative of the subset of the scenarios.

Abstract: Provided are methods for trajectory generation based on a hierarchical plurality of rules using diverse trajectories, which can include generating a first set of trajectories for a vehicle from a first pose, identifying a first trajectory and a second trajectory from the first set of trajectories, generating a second set of trajectories for the vehicle from a second pose and a third set of trajectories for the vehicle from a third pose, identifying a third trajectory based at least in part on the second set of trajectories and the third set of trajectories, the third trajectory violating a first behavioral rule associated with a first priority that is less than a priority of behavioral rules violated by other trajectories, and determining a path for the vehicle based at least in part on the third trajectory. Systems and computer program products are also provided.

Abstract: Provided are methods, systems, and computer program products for inferring and using vehicle trajectory standards. An example method may include: obtaining a training dataset associated with an autonomous vehicle action, the training dataset comprising trajectory data for a plurality of examples of the autonomous vehicle action and labels for each of the plurality of examples; generating a decision tree based on the trajectory data and the labels of the training dataset; determining a vehicle trajectory standard based on the decision tree; and communicating the vehicle trajectory standard to at least one autonomous vehicle, wherein the at least one autonomous vehicle uses the vehicle trajectory standard to select a vehicle trajectory for the at least one autonomous vehicle.

Abstract: Provided are methods, systems, and computer program products for autonomous vehicle validation using real-world adversarial events are described herein. The method includes obtaining a record of human driver data and extracting at least one safety critical scenario from the record to create a test data set associated with operation of vehicles by human drivers during safety critical scenarios. The method includes simulating operation of a vehicle under evaluation during safety critical scenarios and comparing a response of the vehicle under evaluation to the operation of vehicles by human drivers. The method further includes validating a response of the vehicle under evaluation during the simulated operation according to the test data set by transforming the vehicle response into a safety indicator.

Abstract: A rule violation leading to a traffic conflict involving a vehicle and at least one agent in real-time driving scenarios, simulation, re-simulation or other application is determined to be the fault of the vehicle by determining whether the situation was “reasonably foreseeable,” determining whether the at least one agent is a vulnerable road user (VRU) and determining whether the at least one agent violated a higher priority rule than the rule violated by the vehicle. In an embodiment, on-vehicle decisions are based on a rulebook with a priority structure that accounts for responsibility and determines an “initiator” of the traffic conflict based on whether the vehicle or the at least one agent was the first to violate a rule and the first to violate a higher priority rule.

Abstract: Enclosed are embodiments for scoring one or more trajectories of a vehicle through a given traffic scenario using a machine learning model that predicts reasonableness scores for the trajectories. In an embodiment, human annotators, referred to as a “reasonable crowd,” are presented with renderings of two or more vehicle trajectories traversing through the same or different traffic scenarios. The annotators are asked to indicate their preference for one trajectory over the other(s). Inputs collected from the human annotators are used to train the machine learning model to predict reasonableness scores for one or more trajectories for a given traffic scenario. These predicted trajectories can be used to rank trajectories generated by a route planner based on their scores, compare AV software stacks, or used by any other application that could benefit from a machine learning model that scores vehicle trajectories.

Abstract: Provided are methods for graph exploration for rulebook trajectory generation. Some methods described include generating a next set of alternative trajectories for the vehicle from a next pose, the next set of alternative trajectories representing operation of the vehicle from the next pose, wherein the next pose is located at an end of an identified trajectory. Next trajectories are iteratively identified from corresponding next sets of alternative trajectories, wherein a next trajectory violates a lowest behavioral rule of the hierarchical plurality of rules, the lowest behavioral rule having a priority less than a priority of behavioral rules associated with other trajectories in a corresponding next set of alternative trajectories until a goal pose or timeout is reached to generate a graph. Systems and computer program products are also provided.

Abstract: Enclosed are embodiments for scenario-based behavior specification and validation.

Abstract: In an embodiment, a method comprises: selecting a scenario for simulating a vehicle and agent(s) in a virtual world environment; selecting a set of subsystem component models for the vehicle; simulating the scenario in the virtual world environment using the selected subsystem component models, wherein the simulating comprises: estimating a pose of the vehicle and the agent(s); determining a probability of detection of the agent(s) by sensor(s) of the vehicle based on a perception subsystem component model, the estimated pose of the vehicle/agent(s) and a latency model modeling latency of a message queuing network for communicating data between the subsystems; generating a set of candidate trajectories for the vehicle; evaluating each trajectory based on a number of rule violations associated with the trajectory; selecting one trajectory from the set of trajectories based on the number of rule violations; and analyzing outputs of the subsystem component models to determine their interdependencies.

Abstract: In an example method, a computer system receives first data representing a plurality of scenarios for estimating a performance of a vehicle in conducting autonomous operations. Further, the computer system determines, for each of the scenarios: (i) a first metric indicating an observed performance of the vehicle in that scenario, where the first metric is determined based on at least one rule, and (ii) a second metric indicating a degree of information gain associated with that scenario. The computer system selects a subset of the scenarios based on the first metrics and the second metrics, and outputs second data indicative of the subset of the scenarios.

Abstract: Enclosed are embodiments for scoring one or more trajectories of a vehicle through a given traffic scenario using a machine learning model that predicts reasonableness scores for the trajectories. In an embodiment, human annotators, referred to as a “reasonable crowd,” are presented with renderings of two or more vehicle trajectories traversing through the same or different traffic scenarios. The annotators are asked to indicate their preference for one trajectory over the other(s). Inputs collected from the human annotators are used to train the machine learning model to predict reasonableness scores for one or more trajectories for a given traffic scenario. These predicted trajectories can be used to rank trajectories generated by a route planner based on their scores, compare AV software stacks, or used by any other application that could benefit from a machine learning model that scores vehicle trajectories.

Abstract: Enclosed are embodiments for scenario-based behavior specification and validation.

Abstract: Methods for vehicle operation using behavioral rule checks include receiving first sensor data from first sensors and second sensor data from second sensors of the vehicle. The first sensor data represents operation of the vehicle in accordance with a first trajectory. The second sensor data represents at least one object. It is determined that the first trajectory violates a first behavioral rule of operation based on the first sensor data and the second sensor data. The first behavioral rule has a first priority. Multiple alternative trajectories are generated using control barrier functions. A second trajectory is identified that violates a second behavioral rule having a second priority less than the first priority. Responsive to identifying the second trajectory, a message is transmitted to a control circuit of the vehicle to operate the vehicle based on the second trajectory.

Abstract: Methods for vehicle operation using a behavioral rule model include receiving sensor data from a first set of sensors and a second set of sensors. The sensor data represents operation of the vehicle with respect to one or more objects. Violations of a behavioral model of the operation of the vehicle are determined based on the sensor data. A first risk level of the one or more violations is determined based on a distribution of events of the operation of the vehicle with respect to the one or more objects. Responsive to the first risk level being greater than a threshold risk level, a trajectory is generated. The trajectory has a second risk level lower than the threshold risk level. The vehicle is operated based on the trajectory to avoid a collision of the vehicle and the one or more objects.

Abstract: Enclosed are embodiments for scoring one or more trajectories of a vehicle through a given traffic scenario using a machine learning model that predicts reasonableness scores for the trajectories. In an embodiment, human annotators, referred to as a “reasonable crowd,” are presented with renderings of two or more vehicle trajectories traversing through the same or different traffic scenarios. The annotators are asked to indicate their preference for one trajectory over the other(s). Inputs collected from the human annotators are used to train the machine learning model to predict reasonableness scores for one or more trajectories for a given traffic scenario. These predicted trajectories can be used to rank trajectories generated by a route planner based on their scores, compare AV software stacks, or used by any other application that could benefit from a machine learning model that scores vehicle trajectories.

Abstract: Enclosed are embodiments for scoring one or more trajectories of a vehicle through a given traffic scenario using a machine learning model that predicts reasonableness scores for the trajectories. In an embodiment, human annotators, referred to as a “reasonable crowd,” are presented with renderings of two or more vehicle trajectories traversing through the same or different traffic scenarios. The annotators are asked to indicate their preference for one trajectory over the other(s). Inputs collected from the human annotators are used to train the machine learning model to predict reasonableness scores for one or more trajectories for a given traffic scenario. These predicted trajectories can be used to rank trajectories generated by a route planner based on their scores, compare AV software stacks, or used by any other application that could benefit from a machine learning model that scores vehicle trajectories.

Abstract: The present invention provides a composition comprising a biologically pure culture of a Paenibacillus sp. strain comprising a mutant DegU lacking a functional receiver domain or a functional DNA binding domain and/or a mutant DegS lacking a functional single binding domain or a functional ATPase domain with decreased viscosity in a liquid culture. Also provided is a method of identifying a Paenibacillus sp. mutant derivative strain with decreased viscosity in a liquid culture compared to a Paenibacillus sp. parental strain with a visual screen for mutant isolates with a non-mucoid morphology.

Abstract: The illustrative embodiments described herein are directed to apparatuses, systems, and methods for applying reduced pressure to subcutaneous tissue site. In one illustrative embodiment, the apparatus includes a sleeve adapted for placement at a subcutaneous tissue site. The sleeve is further adapted to receive a manifold. The sleeve may also have an opening operable to transfer reduced pressure from the manifold to the subcutaneous tissue site. In one embodiment, the apparatus may also include a manifold that is insertable into the sleeve. The manifold may include at least one aperture, and may be operable to deliver reduced pressure to the subcutaneous tissue site via at least one aperture.

Abstract: The disclosure provides novel molecules related to growth and differentiation factor-8 (GDF8), in particular epitopes specific to GDF8 and other specific antagonists of GDF8 in particular anti-GDF8 antibodies or antigen binding protein or fragment thereof which may inhibit GDF8 activity and signal in vitro and/or in vivo. The disclosure also provides for an immunoassay used to detect and quantitate GDF8. The disclosure also provides methods for diagnosing, preventing, ameliorating, and treating GDF8-associated disorders, e.g., degenerative orders of muscle, bone, and insulin metabolism. Finally, the disclosure provides pharmaceuticals for the treatment of such disorders by using the antibodies, polypeptides, polynucleotides, and vectors of the invention.

Abstract: The disclosure provides novel molecules related to growth and differentiation factor-8 (GDF8), in particular epitopes specific to GDF8 and other specific antagonists of GDF8 in particular anti-GDF8 antibodies or antigen binding protein or fragment thereof which may inhibit GDF8 activity and signal in vitro and/or in vivo. The disclosure also provides for an immunoassay used to detect and quantitate GDF8. The disclosure also provides methods for diagnosing, preventing, ameliorating, and treating GDF8-associated disorders, e.g., degenerative orders of muscle, bone, and insulin metabolism. Finally, the disclosure provides pharmaceuticals for the treatment of such disorders by using the antibodies, polypeptides, polynucleotides, and vectors of the invention.