Interpretable Kalman filter comprising neural network component(s) for autonomous vehicles

A system comprising:

• one or more processors;
• and
• a memory storing processor-executable instructions that, when executed by the one or more processors, cause the system to perform operations comprising:
• receiving sensor data indicating a measurement;
• determining, by a first neural network and based at least in part on the measurement, a bias output, the first neural network being trained under at least a first stage and a second stage, wherein:
• the first stage comprises training the first neural network using simulated sensor data that has zero bias or a first bias less than a threshold bias, the first neural network being trained to limit an output of the first neural network to zero or a value that is less than the threshold bias, and
• the second stage comprises training the first neural network using real-world sensor data and removing an output limit enforced during the first stage;
• modifying the measurement, as an updated measurement, based at least in part on the bias output;
• determining, by a motion model and based at least in part on a prior predicted state, a predicted state associated with an object;
• determining, by a measurement model based at least in part on the prior predicted state, a predicted measurement;
• determining a first difference between the updated measurement and the predicted measurement and a second difference between the prior predicted state and the predicted state associated with the object;
• determining, by a second neural network and based at least in part on the first difference and the second difference, a gain;
• determining, based at least in part on the first difference, the gain, and the predicted state associated with the object, an estimated state associated with the object;
• and
• controlling an autonomous vehicle based at least in part on the estimated state.

A non-transitory computer-readable medium storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• receiving sensor data indicating a measurement;
• determining, by a first neural network of a Kalman filter and based at least in part on the measurement, a bias output, the first neural network being trained under at least a first stage and a second stage, wherein:
• the first stage comprises training the first neural network using simulated sensor data that has zero bias or a first bias less than a threshold bias, the first neural network being trained to limit an output of the first neural network to zero or a value that is less than the threshold bias;
• and
• the second stage comprises training the first neural network using real-world sensor data and removing an output limit enforced during the first stage;
• modifying the measurement, as an updated measurement, based at least in part on the bias output;
• determining, by a motion model of the Kalman filter and based at least in part on a prior predicted state, a predicted state associated with an object;
• determining, by a measurement model of the Kalman filter based at least in part on the prior predicted state, a predicted measurement;
• determining a first difference between the updated measurement and the predicted measurement and a second difference between the prior predicted state and the predicted state associated with the object;
• determining, by a second neural network of the Kalman filter and based at least in part on the first difference and the second difference, a gain;
• determining, based at least in part on the first difference, the gain, and the predicted state associated with the object, an estimated state;
• and
• controlling an autonomous vehicle based at least in part on the estimated state.