Validating Autonomous Vehicle Simulation Scenarios
Validating a simulation scenario for use in training a machine learning model for an autonomous vehicle includes determining a simulation scenario; executing a simulation based on a simulation scenario; monitoring execution of the simulation and receiving messages from the execution of the simulation; determining, by a first simulation monitor, whether the messages satisfy a first incident; and validating the simulation scenario responsive to the messages satisfying the first incident to produce validated simulation data. A system and method may also include determining, by a first simulation validator, whether the messages satisfy a condition; and validating the simulation scenario responsive to the messages satisfying the condition to produce validated simulation data.
1 . A method comprising:
executing a simulation to simulate a behavior of an autonomous vehicle based on a simulation scenario;
receiving a first message including state information of the autonomous vehicle based on a simulated behavior of the autonomous vehicle from the simulation;
determining whether the first message including the state information of the autonomous vehicle satisfies a first condition;
generating, responsive to determining that the first message including the state information of the autonomous vehicle satisfies the first condition, a validated simulated behavior by validating the simulated behavior of the autonomous vehicle in the simulation scenario;
generating a training instance including the validated simulated behavior of the autonomous vehicle in the simulation scenario; and
training a machine learning model of the autonomous vehicle using the training instance including the validated simulated behavior of the autonomous vehicle in the simulation scenario.
2 . The method of claim 1 , further comprising:
generating a simulated output of the simulation scenario based on the simulation;
providing the validated simulated behavior of the autonomous vehicle in the simulation scenario as a training input to the machine learning model to generate a predicted output of the machine learning model; and
updating one or more weights in the machine learning model based on a difference between the predicted output and the simulated output of the simulation scenario.
3 . The method of claim 1 , further comprising:
receiving a second message including the state information of the autonomous vehicle based on the simulated behavior of the autonomous vehicle from the simulation;
determining whether the second message including the state information of the autonomous vehicle satisfies a second condition;
responsive to determining that the second message including the state information of the autonomous vehicle satisfies the second condition, determining that a logical combination of the first condition and the second condition is satisfied; and
wherein generating the validated simulated behavior by validating the simulated behavior of the autonomous vehicle in the simulation scenario is responsive to determining that the logical combination of the first condition and the second condition is satisfied.
4 . The method of claim 3 , further comprising:
determining that the logical combination of the first condition and the second condition corresponds to a termination of the simulation; and
signaling the termination of the simulation as it is executing responsive to determining that the logical combination of the first condition and the second condition is satisfied.
5 . The method of claim 3 , further comprising:
determining that the logical combination of the first condition and the second condition corresponds to a failure of the simulation; and
signaling the failure of the simulation responsive to determining that the logical combination of the first condition and the second condition is satisfied.
6 . The method of claim 3 , further comprising:
determining that the logical combination of the first condition and the second condition corresponds to an advisory warning message in the simulation; and
signaling the advisory warning message in the simulation responsive to determining that the logical combination of the first condition and the second condition is satisfied.
7 . The method of claim 1 , wherein the simulation scenario is a three-dimensional virtual scene simulating an encounter between the autonomous vehicle and an entity in a surrounding environment of the autonomous vehicle.
8 . The method of claim 3 , wherein the first message and the second message correspond to a time series of messages generated in real time during the simulation.
9 . The method of claim 3 , wherein:
the first condition evaluates a first aspect of the simulated behavior of the autonomous vehicle against a first threshold, and
the second condition evaluates a second aspect of the simulated behavior of the autonomous vehicle against a second threshold.
10 . The method of claim 1 , wherein the validated simulated behavior of the autonomous vehicle is exclusive of an unwanted behavior that may bias the machine learning model during the training.
11 . A system comprising one or more processors and memory operably coupled with the one or more processors, wherein the memory stores instructions that, in response to execution of the instructions by the one or more processors, cause the one or more processors to perform operations including:
executing a simulation to simulate a behavior of an autonomous vehicle based on a simulation scenario;
receiving a first message including state information of the autonomous vehicle based on a simulated behavior of the autonomous vehicle from the simulation;
determining whether the first message including the state information of the autonomous vehicle satisfies a first condition;
generating, responsive to determining that the first message including the state information of the autonomous vehicle satisfies the first condition, a validated simulated behavior by validating the simulated behavior of the autonomous vehicle in the simulation scenario;
generating a training instance including the validated simulated behavior of the autonomous vehicle in the simulation scenario; and
training a machine learning model of the autonomous vehicle using the training instance including the validated simulated behavior of the autonomous vehicle in the simulation scenario.
12 . The system of claim 11 , wherein the operations further comprise:
generating a simulated output of the simulation scenario based on the simulation;
providing the validated simulated behavior of the autonomous vehicle in the simulation scenario as a training input to the machine learning model to generate a predicted output of the machine learning model; and
updating one or more weights in the machine learning model based on a difference between the predicted output and the simulated output of the simulation scenario.
13 . The system of claim 11 , wherein the operations further comprise:
receiving a second message including the state information of the autonomous vehicle based on the simulated behavior of the autonomous vehicle from the simulation;
determining whether the second message including the state information of the autonomous vehicle satisfies a second condition;
responsive to determining that the second message including the state information of the autonomous vehicle satisfies the second condition, determining that a logical combination of the first condition and the second condition is satisfied; and
wherein generating the validated simulated behavior by validating the simulated behavior of the autonomous vehicle in the simulation scenario is responsive to determining that the logical combination of the first condition and the second condition is satisfied.
14 . The system of claim 13 , wherein the operations further comprise:
determining that the logical combination of the first condition and the second condition corresponds to a termination of the simulation; and
signaling the termination of the simulation as it is executing responsive to determining that the logical combination of the first condition and the second condition is satisfied.
15 . The system of claim 13 , wherein the operations further comprise:
determining that the logical combination of the first condition and the second condition corresponds to a failure of the simulation; and
signaling the failure of the simulation responsive to determining that the logical combination of the first condition and the second condition is satisfied.
16 . The system of claim 13 , wherein the operations further comprise:
determining that the logical combination of the first condition and the second condition corresponds to an advisory warning message in the simulation; and
signaling the advisory warning message in the simulation responsive to determining that the logical combination of the first condition and the second condition is satisfied.
17 . The system of claim 11 , wherein the simulation scenario is a three-dimensional virtual scene simulating an encounter between the autonomous vehicle and an entity in a surrounding environment of the autonomous vehicle.
18 . The system of claim 13 , wherein the first message and the second message correspond to a time series of messages generated in real time during the simulation.
19 . The system of claim 13 , wherein:
the first condition evaluates a first aspect of the simulated behavior of the autonomous vehicle against a first threshold, and
the second condition evaluates a second aspect of the simulated behavior of the autonomous vehicle against a second threshold.
20 . The system of claim 11 , wherein the validated simulated behavior of the autonomous vehicle is exclusive of an unwanted behavior that may bias the machine learning model during the training.