Abstract: Among other things, a system provides speed behavior planning for vehicles with autonomous driving capabilities.

Abstract: Techniques for operation of a vehicle using machine learning with motion planning include storing, using one or more processors of a vehicle located within an environment, a plurality of constraints for operating the vehicle within the environment. One or more sensors of the vehicle receive sensor data describing the environment. The one or more processors extract a feature vector from the stored plurality of constraints and the received sensor data. The feature vector includes a first feature describing an object located within the environment. A machine learning circuit of the vehicle is used to generate a first motion segment based on the feature vector. A number of violations of the stored plurality of constraints is below a threshold. The one or more processors operate the vehicle in accordance with the generated first motion segment.

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: Techniques are provided for autonomous vehicle operation using linear temporal logic. The techniques include using one or more processors of a vehicle to store a linear temporal logic expression defining an operating constraint for operating the vehicle. The vehicle is located at a first spatiotemporal location. The one or more processors are used to receive a second spatiotemporal location for the vehicle. The one or more processors are used to identify a motion segment for operating the vehicle from the first spatiotemporal location to the second spatiotemporal location. The one or more processors are used to determine a value of the linear temporal logic expression based on the motion segment. The one or more processors are used to generate an operational metric for operating the vehicle in accordance with the motion segment based on the determined value of the linear temporal logic expression.

Abstract: Techniques are provided for autonomous vehicle operation using linear temporal logic. The techniques include using one or more processors of a vehicle to store a linear temporal logic expression defining an operating constraint for operating the vehicle. The vehicle is located at a first spatiotemporal location. The one or more processors are used to receive a second spatiotemporal location for the vehicle. The one or more processors are used to identify a motion segment for operating the vehicle from the first spatiotemporal location to the second spatiotemporal location. The one or more processors are used to determine a value of the linear temporal logic expression based on the motion segment. The one or more processors are used to generate an operational metric for operating the vehicle in accordance with the motion segment based on the determined value of the linear temporal logic expression.

Abstract: Techniques are provided for autonomous vehicle operation using linear temporal logic. The techniques include using one or more processors of a vehicle to store a linear temporal logic expression defining an operating constraint for operating the vehicle. The vehicle is located at a first spatiotemporal location. The one or more processors are used to receive a second spatiotemporal location for the vehicle. The one or more processors are used to identify a motion segment for operating the vehicle from the first spatiotemporal location to the second spatiotemporal location. The one or more processors are used to determine a value of the linear temporal logic expression based on the motion segment. The one or more processors are used to generate an operational metric for operating the vehicle in accordance with the motion segment based on the determined value of the linear temporal logic expression.

Abstract: Techniques for operation of a vehicle using machine learning with motion planning include storing, using one or more processors of a vehicle located within an environment, a plurality of constraints for operating the vehicle within the environment. One or more sensors of the vehicle receive sensor data describing the environment. The one or more processors extract a feature vector from the stored plurality of constraints and the received sensor data. The feature vector includes a first feature describing an object located within the environment. A machine learning circuit of the vehicle is used to generate a first motion segment based on the feature vector. A number of violations of the stored plurality of constraints is below a threshold. The one or more processors operate the vehicle in accordance with the generated first motion segment.

Abstract: Techniques are provided for operation of a vehicle using multiple motion constraints. The techniques include identifying an object using one or more processors of a vehicle. The vehicle has a likelihood of collision with the object that is greater than a threshold. The processors generate multiple motion constraints for operating the vehicle. At least one motion constraint includes a minimum speed of the vehicle greater than zero to avoid a collision of the vehicle with the object. The processors identify one or more motion constraints for operating the vehicle to avoid a collision of the vehicle with the object. The processors operate the vehicle in accordance with the identified motion constraints.

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: Techniques are provided for operation of a vehicle using multiple motion constraints. The techniques include identifying an object using one or more processors of a vehicle. The vehicle has a likelihood of collision with the object that is greater than a threshold. The processors generate multiple motion constraints for operating the vehicle. At least one motion constraint includes a minimum speed of the vehicle greater than zero to avoid a collision of the vehicle with the object. The processors identify one or more motion constraints for operating the vehicle to avoid a collision of the vehicle with the object. The processors operate the vehicle in accordance with the identified motion constraints.

Abstract: Techniques for operation of a vehicle using machine learning with motion planning include storing, using one or more processors of a vehicle located within an environment, a plurality of constraints for operating the vehicle within the environment. One or more sensors of the vehicle receive sensor data describing the environment. The one or more processors extract a feature vector from the stored plurality of constraints and the received sensor data. The feature vector includes a first feature describing an object located within the environment. A machine learning circuit of the vehicle is used to generate a first motion segment based on the feature vector. A number of violations of the stored plurality of constraints is below a threshold. The one or more processors operate the vehicle in accordance with the generated first motion segment.

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: Among other things, a system provides speed behavior planning for vehicles with autonomous driving capabilities.

Abstract: Among other things, a system provides speed behavior planning for vehicles with autonomous driving capabilities.

Abstract: Techniques for operation of a vehicle using machine learning with motion planning include storing, using one or more processors of a vehicle located within an environment, a plurality of constraints for operating the vehicle within the environment. One or more sensors of the vehicle receive sensor data describing the environment. The one or more processors extract a feature vector from the stored plurality of constraints and the received sensor data. The feature vector includes a first feature describing an object located within the environment. A machine learning circuit of the vehicle is used to generate a first motion segment based on the feature vector. A number of violations of the stored plurality of constraints is below a threshold. The one or more processors operate the vehicle in accordance with the generated first motion segment.

Abstract: Among other things, we describe techniques for redundancy in autonomous vehicles. For example, an autonomous vehicle can include two or more redundant autonomous vehicle operations subsystems.

Abstract: Techniques are provided for autonomous vehicle operation using linear temporal logic. The techniques include using one or more processors of a vehicle to store a linear temporal logic expression defining an operating constraint for operating the vehicle. The vehicle is located at a first spatiotemporal location. The one or more processors are used to receive a second spatiotemporal location for the vehicle. The one or more processors are used to identify a motion segment for operating the vehicle from the first spatiotemporal location to the second spatiotemporal location. The one or more processors are used to determine a value of the linear temporal logic expression based on the motion segment. The one or more processors are used to generate an operational metric for operating the vehicle in accordance with the motion segment based on the determined value of the linear temporal logic expression.