Autonomous vehicle trajectory planning using neural network trained based on knowledge distillation

A method, comprising:

• in an electronic device:
• determining a set of updated values of a set of variables of an objective function for trajectory planning of an ego autonomous vehicle (AV), wherein
• the determination is based on a set of initial values of the set of variables and a set of gradients of the objective function;
• applying a first prediction network on an updated value of the set of updated values, a state of the ego AV over a past time interval, and a state of each AV of a set of AVs over the past time interval, wherein
• the updated value is indicative of a current state of the ego AV;
• determining, based on the application, an output that includes a state of the ego AV over a future time interval, and a state of each AV of the set of AVs over the future time interval;
• determining a set of optimal values for the set of variables based on the updated value and the determined output satisfying a safety constraint associated with the objective function;
• and
• controlling a trajectory of the ego AV based on the set of optimal values of the set of variables.

A non-transitory computer-readable medium having stored thereon, computer-executable instructions that when executed by an electronic device, causes the electronic device to execute operations, the operations comprising:

• determining a set of updated values of a set of variables of an objective function for trajectory planning of an ego autonomous vehicle (AV), wherein
• the determination is based on a set of initial values of the set of variables and a set of gradients of the objective function;
• applying a first prediction network on an updated value of the set of updated values, a state of the ego AV over a past time interval, and a state of each AV of a set of AVs over the past time interval, wherein
• the updated value is indicative of a current state of the ego AV;
• determining, based on the application, an output that includes a state of the ego AV over a future time interval, and a state of each AV of the set of AVs over the future time interval;
• determining a set of optimal values for the set of variables based on the updated value and the determined output satisfying a safety constraint associated with the objective function;
• and
• controlling a trajectory of the ego AV based on the set of optimal values of the set of variables.