SYSTEMS AND METHODS FOR GENERATING A TRAINING SET FOR A NEURAL NETWORK CONFIGURED TO GENERATE CANDIDATE TRAJECTORIES FOR AN AUTONOMOUS VEHICLE
This disclosure provides methods and systems for generating a training set for a neural network configured to generate candidate trajectories for an autonomous vehicle, comprising: receiving a set of sensor data representative of one or more portions of a plurality of objects in the environment of the autonomous vehicle; for each object, calculating a representative box enclosing the object, the representative box having portions comprising corners, edges, and planes; for each representative box, calculating at least one vector into the representative box from a position on the autonomous vehicle; for each vector, calculating a first and second corner position of the representative box, an edge of the representative box, and a plane of the representative box; determining the highest confidence corners, edge, and plane of each representative based on calculation from the at least one vector; and generating a training set including the highest confidence corners, edge, and plane of each representative box.
1 . A method for generating a training set for a neural network configured to generate candidate trajectories for an autonomous vehicle, comprising:
receiving a set of sensor data representative of one or more portions of a plurality of objects in the environment of the autonomous vehicle;
for each object, calculating a representative box enclosing the object, the representative box having portions comprising corners, edges, and planes;
for each representative box, calculating at least one vector into the representative box from a position on the autonomous vehicle;
for each vector, calculating a first and second corner position of the representative box, an edge of the representative box, and a plane of the representative box;
determining the highest confidence corners, edge, and plane of each representative based on calculation from the at least one vector; and
generating a training set including the highest confidence corners, edge, and plane of each representative box.
2 . The method of claim 1 , wherein the highest confidence corners, edge, and plane of the representative box are the nearest corners, edge, and plane of the representative box to the autonomous vehicle.
3 . The method of claim 1 , wherein the representative box comprises the nearest corners, edge, and plane of the object to the autonomous vehicle.
4 . The method of claim 1 , wherein the at least one vector comprises a plurality of vectors.
5 . The method of claim 1 , comprising for each object, calculating a representative box enclosing the object, the representative box having portions comprising the center of the object; for each representative box, calculating the at least one vector into the representative box from a position on the autonomous vehicle; for each vector, calculating the center of the representative box; and determining the highest confidence center of the representative box from calculation from the at least one vector.
6 . The method of claim 1 , comprising: for each object, calculating a representative box enclosing the object, the representative box having portions comprising a point along the longitudinal centerline of the object; for each representative box, calculating the at least one vector into the representative box from a position on the autonomous vehicle; for each vector, calculating a point along the longitudinal centerline of the representative box; and determining the highest confidence point along the longitudinal centerline of the representative box from calculation from the at least one vector.
7 . The method of claim 1 , comprising determining features of the highest confidence corners, edge, and plane of each representative box.
8 . The method of claim 2 , comprising determining features of the nearest corners, edge, and plane of each representative box.
9 . The method of claim 1 , wherein the object is a vehicle in the environment of the autonomous vehicle.
10 . The method of claim 9 , wherein the neural network comprises a convolutional neural network (CNN).
11 . A system for generating a training set for a neural network configured to generate candidate trajectories for an autonomous vehicle, comprising:
at least one sensor, configured to receive sensor data representative of one or more portions of an object in the environment of the autonomous vehicle; and
a processor, configured to:
for each object, calculate a representative box enclosing the object, the representative box having portions comprising corners, edges, and planes;
for each representative box, calculate at least one vector into the representative box from a position on the autonomous vehicle;
for each vector, calculate a first and second corner position of the representative box, an edge of the representative box, and a plane of the representative box;
determine the highest confidence corners, edge, and plane of each representative based on calculation from the at least one vector; and
generate a training set including the highest confidence corners, edge, and plane of each representative box.
12 . The system of claim 11 , wherein the highest confidence corners, edge, and plane of the representative box are the nearest corners, edge, and plane of the representative box to the autonomous vehicle.
13 . The system of claim 11 , wherein the representative box comprises the nearest corners, edge, and plane of the object to the autonomous vehicle.
14 . The system of claim 11 , wherein the at least one vector comprises a plurality of vectors.
15 . The method of claim 11 , wherein the processor is configured to: for each object, calculate a representative box enclosing the object, the representative box having portions comprising the center of the object; for each representative box, calculate the at least one vector into the representative box from a position on the autonomous vehicle; for each vector, calculate the center of the representative box; and determine the highest confidence center of the representative box from calculation from the at least one vector.
16 . The system of claim 11 , wherein the processor is configured to: for each object, calculate a representative box enclosing the object, the representative box having portions comprising a point along the longitudinal centerline of the object; for each representative box, calculate the at least one vector into the representative box from a position on the autonomous vehicle; for each vector, calculate a point along the longitudinal centerline of the representative box; and determine the highest confidence point along the longitudinal centerline of the representative box from calculation from the at least one vector.
17 . The system of claim 11 , wherein the processor is configured to determine features of the highest confidence corners, edge, and plane of each representative box.
18 . The system of claim 12 , wherein the processor is configured to determine features of the nearest corners, edge, and plane of each representative box.
19 . The system of claim 11 , wherein the object is a vehicle in the environment of the autonomous vehicle.
20 . The system of claim 19 , wherein the neural network comprises a convolutional neural network (CNN).