Operation of a vehicle using motion planning with machine learning

An autonomous vehicle comprising:

• one or more computer processors;
• and
• one or more non-transitory storage media storing instructions which, when executed by the one or more computer processors, cause the one or more computer processors to:
• accessing a plurality of constraints for operating the autonomous vehicle within an environment;
• receive, using one or more sensors of the autonomous vehicle, sensor data describing the environment;
• receive, using one or more additional sensors of the autonomous vehicle, additional sensor data describing one or more physical characteristics of a passenger of the autonomous vehicle other than a driver of the autonomous vehicle;
• extract a feature vector from the stored plurality of constraints, the received sensor data, and the received additional sensor data, wherein the feature vector comprises:
• a first feature describing an object located within the environment, and
• a second feature describing the one or more physical characteristics of the passenger of the autonomous vehicle, including a comfort level of the passenger, as a function of a speed of the autonomous vehicle;
• generate, using a machine learning circuit of the autonomous vehicle, a first motion segment based on the feature vector including the first feature and the second feature, such that a number of violations of the stored plurality of constraints is below a threshold, wherein the generated first motion segment comprises at least one of:
• a trajectory between two spatiotemporal locations of the environment, or
• a speed of the autonomous vehicle to avoid a collision of the autonomous vehicle with the object;
• and
• causing the autonomous vehicle to autonomously traverse to a destination in accordance with the generated first motion segment.

One or more non-transitory storage media storing instructions which, when executed by one or more computing devices, cause the one or more computing devices to:

• accessing a plurality of constraints for operating an autonomous vehicle within an environment;
• receive, using one or more sensors of the autonomous vehicle, sensor data describing the environment;
• receive, using one or more additional sensors of the autonomous vehicle, additional sensor data describing one or more physical characteristics of a passenger of the autonomous vehicle other than a driver of the autonomous vehicle;
• extract a feature vector from the stored plurality of constraints, the received sensor data, and the received additional sensor data, wherein the feature vector comprises:
• a first feature describing an object located within the environment, and
• a second feature describing the one or more physical characteristics of the passenger of the autonomous vehicle, including a comfort level of the passenger, as a function of a speed of the autonomous vehicle;
• generate, using a machine learning circuit of the autonomous vehicle, a first motion segment based on the feature vector including the first feature and the second feature, such that a number of violations of the stored plurality of constraints is below a threshold, wherein the generated first motion segment comprises at least one of:
• a trajectory between two spatiotemporal locations of the environment, or
• a speed of the autonomous vehicle to avoid a collision of the autonomous vehicle with the object;
• and
• causing the autonomous vehicle to autonomously traverse to a destination in accordance with the generated first motion segment.