Abstract: Techniques for top-down scene discrimination are discussed. A system receives scene data associated with an environment proximate a vehicle. The scene data is input to a convolutional neural network (CNN) discriminator trained using a generator and a classification of the output of the CNN discriminator. The CNN discriminator generates an indication of whether the scene data is a generated scene or a captured scene. If the scene data is data generated scene, the system generates a caution notification indicating that a current environmental situation is different from any previous situations. Additionally, the caution notification is communicated to at least one of a vehicle system or a remote vehicle monitoring system.

Abstract: Techniques for top-down scene generation are discussed. A generator component may receive multi-dimensional input data associated with an environment. The generator component may generate, based at least in part on the multi-dimensional input data, a generated top-down scene. A discriminator component receives the generated top-down scene and a real top-down scene. The discriminator component generates binary classification data indicating whether an individual scene in the scene data is classified as generated or classified as real. The binary classification data is provided as a loss to the generator component and the discriminator component.

Abstract: Techniques are discussed herein for determining truncated simulation regions within a parameter space of simulation scenarios, such as driving scenarios used to analyze and evaluate the responses of autonomous vehicle controllers. Using non-sampling-based parameter selection techniques, parameterized scenarios may be executed as simulations to determine the truncated simulation region. Sampling-based parameter selection techniques may be used to determine additional parameterized scenarios, which may be compared to the truncated simulation region. Parameterized scenarios within the truncated simulation region may be executed as simulations and scenarios outside of the truncated simulation region may be excluded, and the aggregated results may be analyzed to determine scenario/vehicle performance metrics across the scenario parameter space.

Abstract: Techniques for determining an error model based on vehicle data and ground truth data are discussed herein. To determine whether a complex system (which may be not capable of being inspected) is able to operate safely, various operating regimes (scenarios) can be identified based on operating data. To provide safe operation of such a system, an error model can be determined that can provide a probability associated with perception data and a vehicle can determine a trajectory based on the probability of an error associated with the perception data.

Abstract: Techniques associated with generating simulation scenarios for simulating a vehicle controller are discussed herein. Log data may include sensor data captured by sensors of a vehicle. The log data may represent objects in an environment. Objects may be associated with a region of a discretized representation of the environment relative to the vehicle. Specific states of objects (relative position in a region type, velocity, classification, size, etc.) may represent an instance of an occupation. Log data can be aggregated based on similar region type and/or object state. A statistical model over object states can be determined for each region type and can later be sampled to determine simulation parameters. A simulation scenario can be generated based on the simulation parameters, and a vehicle controller can be evaluated based on the simulation scenario.

Abstract: Techniques for determining a safety metric associated with a vehicle controller are discussed herein. To determine whether a complex system (which may be uninspectable) is able to operate safely, various operating regimes (scenarios) can be identified based on operating data and associated with a scenario parameter to be adjusted. To validate safe operation of such a system, a scenario may be identified for inspection. Error metrics of a subsystem of the system can be quantified. The error metrics, in addition to stochastic errors of other systems/subsystems can be introduced to the scenario. The scenario parameter may also be perturbed. Any multitude of such perturbations can be instantiated in a simulation to test, for example, a vehicle controller. A safety metric associated with the vehicle controller can be determined based on the simulation, as well as causes for any failures.

Abstract: Techniques for analyzing a parameter space are discussed. Techniques may include receiving policy data for evaluating a vehicle controller. The techniques may further include determining, using a Bayesian optimization and based at least in part on the vehicle controller, parameter sets associated with adverse events. The adverse events may be associated with a violation of the policy data. The techniques may associate, based on exposure data, parameter bounds of the adverse events and probabilities of the adverse events in a driving environment. A safety metric may be determined based on the Bayesian optimization. The techniques may also include weighting an impact of an adverse event based on the safety metric.

Abstract: Techniques for top-down scene generation are discussed. A generator component may receive multi-dimensional input data associated with an environment. The generator component may generate, based at least in part on the multi-dimensional input data, a generated top-down scene. A discriminator component receives the generated top-down scene and a real top-down scene. The discriminator component generates binary classification data indicating whether an individual scene in the scene data is classified as generated or classified as real. The binary classification data is provided as a loss to the generator component and the discriminator component.

Abstract: Techniques for top-down scene discrimination are discussed. A system receives scene data associated with an environment proximate a vehicle. The scene data is input to a convolutional neural network (CNN) discriminator trained using a generator and a classification of the output of the CNN discriminator. The CNN discriminator generates an indication of whether the scene data is a generated scene or a captured scene. If the scene data is data generated scene, the system generates a caution notification indicating that a current environmental situation is different from any previous situations. Additionally, the caution notification is communicated to at least one of a vehicle system or a remote vehicle monitoring system.

Abstract: Techniques for determining an error model associated with a system/subsystem of vehicle controller are discussed herein. To determine whether a complex system (which may be uninspectable) is able to operate safely, errors can be introduced into operating regimes (scenarios) to validate the safe operation of such a system. By comparing captured and/or generated vehicle data with ground truth data, an error of the system can be statistically quantified and modeled. The statistical model can be used to introduce errors to the scenario to perturb the scenario to test, for example, a vehicle controller. Based on a simulation of the vehicle controlled in the perturbed scenario, a safety metric associated with the vehicle controller can be determined, as well as causes for any failures.

Abstract: Simulating realistic movement of an object, such as a vehicle or pedestrian, that accounts for unusual behavior may comprise generating an agent behavior model based at least in part on output of a perception component of an autonomous vehicle and determining a difference between the output and log data that includes indications of an actual maneuver of location of an object. Simulating movement of an object may comprise determining predicted motion of the object using the perception component and modifying the predicted motion based at least in part on the agent behavior model.

Abstract: Simulating realistic movement of an object, such as a vehicle or pedestrian, that accounts for unusual behavior may comprise generating an agent behavior model based at least in part on output of a perception component of an autonomous vehicle and determining a difference between the output and log data that includes indications of an actual maneuver of location of an object. Simulating movement of an object may comprise determining predicted motion of the object using the perception component and modifying the predicted motion based at least in part on the agent behavior model.

Abstract: Techniques associated with generating simulation scenarios for simulating a vehicle controller are discussed herein. Log data may include sensor data captured by sensors of a vehicle. The log data may represent objects in an environment. Objects may be associated with a region of a discretized representation of the environment relative to the vehicle. Specific states of objects (relative position in a region type, velocity, classification, size, etc.) may represent an instance of an occupation. Log data can be aggregated based on similar region type and/or object state. A statistical model over object states can be determined for each region type and can later be sampled to determine simulation parameters. A simulation scenario can be generated based on the simulation parameters, and a vehicle controller can be evaluated based on the simulation scenario.

Abstract: Techniques for determining an error model based on vehicle data and ground truth data are discussed herein. To determine whether a complex system (which may be not capable of being inspected) is able to operate safely, various operating regimes (scenarios) can be identified based on operating data. To provide safe operation of such a system, an error model can be determined that can provide a probability associated with perception data and a vehicle can determine a trajectory based on the probability of an error associated with the perception data.

Abstract: Techniques for determining a safety metric associated with a vehicle controller are discussed herein. To determine whether a complex system (which may be uninspectable) is able to operate safely, various operating regimes (scenarios) can be identified based on operating data and associated with a scenario parameter to be adjusted. To validate safe operation of such a system, a scenario may be identified for inspection. Error metrics of a subsystem of the system can be quantified. The error metrics, in addition to stochastic errors of other systems/subsystems can be introduced to the scenario. The scenario parameter may also be perturbed. Any multitude of such perturbations can be instantiated in a simulation to test, for example, a vehicle controller. A safety metric associated with the vehicle controller can be determined based on the simulation, as well as causes for any failures.

Abstract: Techniques for determining an error model associated with a system/subsystem of vehicle controller are discussed herein. To determine whether a complex system (which may be uninspectable) is able to operate safely, errors can be introduced into operating regimes (scenarios) to validate the safe operation of such a system. By comparing captured and/or generated vehicle data with ground truth data, an error of the system can be statistically quantified and modeled. The statistical model can be used to introduce errors to the scenario to perturb the scenario to test, for example, a vehicle controller. Based on a simulation of the vehicle controlled in the perturbed scenario, a safety metric associated with the vehicle controller can be determined, as well as causes for any failures.