Abstract: Among other things, techniques are described for improving the trajectory estimates for an object in an environment. The technique includes receiving, by at least one processor, information indicating a presence of an object operating in an environment; determining, by the at least one processor, a trajectory of the object, the trajectory including at least a position, a speed, and a direction of travel of the object; determining, by the at least one processor, an expected route of the object, wherein the expected route is pre-planned and includes an expected future position of the object at a future time; comparing the trajectory of the object to the expected route of the object; and in accordance with the comparison that the trajectory of the object is consistent with the expected route of the object, updating the trajectory of the object based on the expected route of the object.

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, techniques are described for self-cleaning door handles. A door handle for a vehicle includes a housing including a hand grip; a hygienic film that is formed around a portion of a circumference of the hand grip; a plurality of cylindrical rollers rotatably connected to the housing and are configured to rotationally guide the hygienic film along the portion of the circumference of the hand grip; and a light that is configured to emit electromagnetic radiation in an ultraviolent spectrum and configured to irradiate at least one portion of the hygienic film to reduce a quantity of bacteria on the at least one portion of the hygienic film.

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 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, techniques are described for improving the trajectory estimates for an object in an environment. The technique includes receiving, by at least one processor, information indicating a presence of an object operating in an environment; determining, by the at least one processor, a trajectory of the object, the trajectory including at least a position, a speed, and a direction of travel of the object; determining, by the at least one processor, an expected route of the object, wherein the expected route is pre-planned and includes an expected future position of the object at a future time; comparing the trajectory of the object to the expected route of the object; and in accordance with the comparison that the trajectory of the object is consistent with the expected route of the object, updating the trajectory of the object based on the expected route of the object.

Abstract: Among other things, techniques are described for self-cleaning door handles. A door handle for a vehicle includes a housing including a hand grip; a hygienic film that is formed around a portion of a circumference of the hand grip; a plurality of cylindrical rollers rotatably connected to the housing and are configured to rotationally guide the hygienic film along the portion of the circumference of the hand grip; and a light that is configured to emit electromagnetic radiation in an ultraviolent spectrum and configured to irradiate at least one portion of the hygienic film to reduce a quantity of bacteria on the at least one portion of the hygienic film.

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 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.