Methods and systems for multiple object classification tracking in autonomous vehicles

An autonomous vehicle, comprising:

• at least one processor;
• and
• at least one memory storing instructions, which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
• receiving, at a computing device of an autonomous vehicle, first sensor data from a network of one or more sensors;
• generating a first prediction, based at least in part on the received first sensor data and based at least in part on probability determined in accordance with the received first sensor data for an object to have properties corresponding to a particular sub-class, by the computing device, for an object being associated with a first state with a first confidence value;
• receiving, at the computing device of an autonomous vehicle, second sensor data from the network of one or more sensors, the second sensor data received after elapsing of time t after receiving the first sensor data;
• generating a second prediction, based at least in part on the received second sensor data and based at least in part on the probability determined in accordance with the received second sensor data for the object to have properties corresponding to another particular sub-class, by the computing device, for the object being associated with a second state with a second confidence value, generating the second prediction further comprising:
• determining the second confidence value based on an exponentially decaying model described by γk=exp (−δt)γk-1, where δt represents time difference between time k and k−1 corresponding to receiving the second sensor data and receiving the first sensor data, respectively, and γk and γk-1 represent the second confidence value and the first confidence value, respectively;
• updating, by the computing device, a state of the object in a hierarchical representation tree to be the first state or the second state in accordance with a higher confidence value between the first confidence value and the second confidence value;
• and
• controlling operation of the autonomous vehicle based on the updated state of the object.

A non-transitory computer-readable medium (CRM) embodying programmed instructions which, when executed by at least one processor of an autonomous vehicle, cause the at least one processor to perform operations comprising:

• receiving, at a computing device of an autonomous vehicle, first sensor data from a network of one or more sensors;
• generating a first prediction, based at least in part on the received first sensor data and based at least in part on probability determined in accordance with the received first sensor data for an object to have properties corresponding to a particular sub-class, by the computing device, for an object being associated with a first state with a first confidence value;
• receiving, at the computing device of an autonomous vehicle, second sensor data from the network of one or more sensors, the second sensor data received after elapsing of time t after receiving the first sensor data;
• generating a second prediction, based at least in part on the received second sensor data and based at least in part on the probability determined in accordance with the received second sensor data for the object to have properties corresponding to another particular sub-class, by the computing device, for the object being associated with a second state with a second confidence value, generating the second prediction further comprising:
• determining the second confidence value based on an exponentially decaying model described by γk=exp(−δt)γk-1, where δt represents time difference between time k and k−1 corresponding to receiving the second sensor data and receiving the first sensor data, respectively, and γk and γk-1 represent the second confidence value and the first confidence value, respectively;
• updating, by the computing device, a state of the object in a hierarchical representation tree to be the first state or the second state in accordance with a higher confidence value between the first confidence value and the second confidence value;
• and
• controlling operation of the autonomous vehicle based on the updated state of the object.