Vehicle trajectory tree structure including learned trajectories
Techniques for generating a tree structure based on multiple machine-learned trajectories are described herein. A planning component (“ML system”) within a vehicle may receive and encode various types of sensor and/or vehicle data. The ML system can provide the encoded data as input to multiple machine-learning models (“ML models”), each of which may be trained to output a unique candidate trajectory for the vehicle follow. In some examples, each ML model may be trained to output a unique type of learned trajectory that causes the vehicle to perform a certain type of action. Using the learned candidate trajectories, the ML system may generate a tree structure that includes some or all of the candidate trajectories. The vehicle may determine a control trajectory based on the generation and traversal of the tree structure using a tree search algorithm, and may follow the control trajectory within the environment.
1 . A system comprising:
one or more processors; and
one or more non-transitory computer-readable media storing computer-executable instructions that, when executed, cause the one or more processors to perform operations comprising:
receiving, from a sensor associated with an autonomous vehicle, sensor data;
determining, using a first machine learned model and based at least in part on the sensor data, an encoding;
inputting the encoding into a second machine-learning model;
receiving, from the second machine-learning model, a first candidate trajectory of a first type;
receiving, from the second machine-learning model, a second candidate trajectory having a second type that is different from the first type;
determining that both the first candidate trajectory and the second candidate trajectory meet or exceed a probability threshold;
generating, based at least in part on the first candidate trajectory and the second candidate trajectory meeting or exceeding the probability threshold, a tree structure;
determining a control trajectory for the autonomous vehicle, based at least in part on the tree structure; and
controlling the autonomous vehicle based at least in part on the control trajectory.
2 . The system of claim 1 , wherein determining the encoding is further based at least in part on:
a state of the autonomous vehicle,
a characteristic of an object within an environment, or
a feature of a surface of a road.
3 . The system of claim 1 , the operations further comprising:
determining, based on a heuristic, a third candidate trajectory,
wherein generating the tree structure is further based at least in part on the third candidate trajectory.
4 . The system of claim 1 , wherein the second machine-learning model is trained such that a difference between a first output trajectory and a second output trajectory meets or exceeds a threshold difference.
5 . The system of claim 1 , wherein the tree structure includes a plurality of nodes that are associated with the first candidate trajectory or the second candidate trajectory.
6 . One or more non-transitory computer-readable media storing instructions executable by one or more processors, wherein the instructions, when executed, cause the one or more processors to perform operations comprising:
receiving, from a sensor associated with a vehicle, sensor data;
determining, based at least in part on the sensor data, a representation of an environment;
inputting the representation into a machine-learning model;
determining, using the machine-learning model and based at least in part on the representation, a first candidate trajectory for the vehicle;
determining, using the machine-learning model and based at least in part on the representation, a second candidate trajectory for the vehicle, wherein the second candidate trajectory differs from the first candidate trajectory by at least a threshold difference;
determining that both the first candidate trajectory and the second candidate trajectory meet or exceed a probability threshold;
generating, based at least in part on the first candidate trajectory and the second candidate trajectory meeting or exceeding the probability threshold, a tree structure; and
determining a control trajectory for the vehicle, based at least in part on the tree structure.
7 . The one or more non-transitory computer-readable media of claim 6 , wherein determining the representation is further based at least in part on one or more of:
a state of the vehicle,
a characteristic of an object within the environment, or
a feature of a surface of a road.
8 . The one or more non-transitory computer-readable media of claim 6 , the operations further comprising:
determining, based at least in part on a heuristic, a third candidate trajectory,
wherein generating the tree structure is further based at least in part on the third candidate trajectory.
9 . The one or more non-transitory computer-readable media of claim 6 , wherein the first candidate trajectory is of a first type and the second candidate trajectory is of a second type, and wherein one or more of the first type or the second type are associated with one or more of:
a lane change,
a modified velocity,
a modified acceleration,
a modified pose within a current lane, or
remaining in the current lane.
10 . The one or more non-transitory computer-readable media of claim 6 , wherein the control trajectory comprises of a first portion of the first candidate trajectory and a second portion of the second candidate trajectory.
11 . The one or more non-transitory computer-readable media of claim 6 , wherein the operations further comprising:
controlling the vehicle based at least in part on the control trajectory.
12 . The one or more non-transitory computer-readable media of claim 6 , wherein determining that the first candidate trajectory differs from the second candidate trajectory is based at least in part on comparing one or more of:
a first steering angle of the first candidate trajectory and a second steering angle of the second candidate trajectory,
a first velocity of the first candidate trajectory and a second velocity of the second candidate trajectory, or
a first acceleration of the first candidate trajectory and a second acceleration of the second candidate trajectory.
13 . The one or more non-transitory computer-readable media of claim 6 , wherein the tree structure includes a plurality of nodes that are associated with the first candidate trajectory or the second candidate trajectory.
14 . A method comprising:
receiving, from a sensor associated with a vehicle, sensor data;
determining, based at least in part on the sensor data, a representation of an environment;
inputting the representation into a machine-learning model;
determining, using the machine-learning model and based at least in part on the representation, a first candidate trajectory for the vehicle;
determining, using the machine-learning model and based at least in part on the representation, a second candidate trajectory for the vehicle, wherein the second candidate trajectory differs from the first candidate trajectory by at least a threshold difference;
determining that both the first candidate trajectory and the second candidate trajectory meet or exceed a probability threshold;
generating, based at least in part on the first candidate trajectory and the second candidate trajectory meeting or exceeding the probability threshold, a tree structure; and
determining a control trajectory for the vehicle, based at least in part on the tree structure.
15 . The method of claim 14 , wherein determining the representation is further based at least in part on one or more of:
a state of the vehicle,
a characteristic of an object within the environment, or
a feature of a surface of a road.
16 . The method of claim 14 , further comprising:
determining, based at least in part on a heuristic, a third candidate trajectory,
wherein generating the tree structure is further based at least in part on the third candidate trajectory.
17 . The method of claim 14 , wherein the first candidate trajectory is associated with a first type and the second candidate trajectory is associated with a second type, and wherein one or more of the first type or the second type are associated with one or more of:
a lane change,
a modified velocity,
a modified acceleration,
a modified pose within a current lane, or
remaining in the current lane.
18 . The method of claim 14 , wherein the control trajectory comprises of a first portion of the first candidate trajectory and a second portion of the second candidate trajectory.
19 . The method of claim 14 , wherein determining that the first candidate trajectory differs from the second candidate trajectory is based at least in part on comparing one or more of:
a first steering angle of the first candidate trajectory and a second steering angle of the second candidate trajectory,
a first velocity of the first candidate trajectory and a second velocity of the second candidate trajectory, or
a first acceleration of the first candidate trajectory and a second acceleration of the second candidate trajectory.
20 . The method of claim 14 , wherein the tree structure includes a plurality of nodes that are associated with the first candidate trajectory or the second candidate trajectory.