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, 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: 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: Enclosed are embodiments of motion control operations in various traffic scenarios in consideration of the kinematic factor for trajectory planning. In some embodiments, a method includes: determining a danger rating for at least one object identified in an environment, wherein the danger rating represents a perceived risk associated with a respective object; evaluating a set of hierarchical factors with respect to a traffic scenario, wherein a metric is derived for trajectories of the traffic scenario that quantifies passenger ride comfort based on the danger rating and the set of hierarchical factors; determining a motion control operation in the traffic scenario to increase the passenger ride comfort based on the metric; and augmenting a route planner of an autonomous vehicle with motion control operations in different traffic scenarios to increase the passenger ride comfort.

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 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.