INTELLIGENT DRIVING DECISION-MAKING METHOD, VEHICLE TRAVELING CONTROL METHOD AND APPARATUS, AND VEHICLE
This application relates to intelligent driving technologies, and provides an intelligent driving decision-making method, including: first, obtaining a game object of an ego vehicle; then from a plurality of strategy spaces of the ego vehicle and the game object, performing a plurality of times of release of the plurality of strategy spaces; and determining a strategy feasible region of the ego vehicle and the game object based on each released strategy space, and determining a traveling decision-making result of the ego vehicle based on the strategy feasible region. The decision-making result is an executable behavior action of the ego vehicle. As described above, by releasing the strategy spaces for a plurality of times, while decision-making precision is ensured, the decision-making result may be obtained when as fewer strategy spaces are released as possible. This reduces a computing amount and lowers a requirement for hardware computing power.
1 . A method for intelligent driving decision-making, comprising:
obtaining a game object of an ego vehicle; and
from a plurality of strategy spaces of both the ego vehicle and the game object, performing a plurality of times of release of the plurality of strategy spaces; and
after performing one of the plurality of times of release, determining a strategy feasible region of both the ego vehicle and the game object based on each released strategy space, and determining a traveling decision-making result of the ego vehicle based on the strategy feasible region.
2 . The method according to claim 1 , wherein
a dimension of the plurality of strategy spaces comprises at least one of the following: a longitudinal sampling dimension, a lateral sampling dimension, or a temporal sampling dimension.
3 . The method according to claim 2 , wherein the performing a plurality of times of release of the plurality of strategy spaces comprises performing a release in a sequence of the following dimensions: the longitudinal sampling dimension, the lateral sampling dimension, and the temporal sampling dimension.
4 . The method according to claim 1 , wherein when the strategy feasible region of both the ego vehicle and the game object is determined, a total cost value of a behavior-action pair in the strategy feasible region is determined based on one or more of the following:
a safety cost value, a right-of-way cost value, a lateral offset cost value, a passability cost value, a comfort cost value, an inter-frame association cost value, and a risk area cost value of the ego vehicle or the game object.
5 . The method according to claim 4 , wherein when the total cost value of the behavior-action pair is determined based on two or more cost values, each of the two or more cost values has a different weight.
6 . The method according to claim 1 , wherein when there are two or more game objects, the traveling decision-making result of the ego vehicle is determined based on each strategy feasible region of both the ego vehicle and a respective game object.
7 . The method according to claim 1 , further comprising:
obtaining a non-game object of the ego vehicle;
determining a strategy feasible region of both the ego vehicle and the non-game object; and
determining the traveling decision-making result of the ego vehicle based on at least the strategy feasible region of both the ego vehicle and the non-game object.
8 . The method according to claim 6 , wherein:
a strategy feasible region of the traveling decision-making result of the ego vehicle is determined based on an intersection of each strategy feasible region of both the ego vehicle and a respective game object; or
a strategy feasible region of the traveling decision-making result of the ego vehicle is determined based on an intersection of each strategy feasible region of both the ego vehicle and a respective game object and each strategy feasible region of both the ego vehicle and a respective non-game object.
9 . The method according to claim 2 , further comprising:
obtaining a non-game object of the ego vehicle; and
based on a motion status of the non-game object, constraining a longitudinal sampling strategy space corresponding to the ego vehicle, or constraining a lateral sampling strategy space corresponding to the ego vehicle.
10 . The method according to claim 2 , further comprising:
obtaining a non-game object of the game object of the ego vehicle; and
based on a motion status of the non-game object, constraining a longitudinal sampling strategy space corresponding to the game object of the ego vehicle, or constraining a lateral sampling strategy space corresponding to the game object of the ego vehicle.
11 . The method according to claim 8 , wherein when the intersection is an empty set, a conservative traveling decision of the ego vehicle is performed; and the conservative traveling decision comprises an action of making the ego vehicle safely stop or an action of making the ego vehicle safely decelerate for traveling.
12 . The method according to claim 1 , wherein the game object or a non-game object is determined by attention.
13 . The method according to claim 1 , further comprising: displaying at least one of the following through a human-computer interaction interface:
the traveling decision-making result of the ego vehicle, the strategy feasible region of the traveling decision-making result, a traveling trajectory of the ego vehicle corresponding to the traveling decision-making result of the ego vehicle, or a traveling trajectory of the game object corresponding to the traveling decision-making result of the ego vehicle.
14 . An apparatus for intelligent driving decision-making, comprising:
at least one processor; and
one or more memories coupled to the at least one processor and storing programming instructions for execution by the at least one processor to perform the following operations:
obtaining a game object of an ego vehicle; and
from a plurality of strategy spaces of both the ego vehicle and the game object, performing a plurality of times of release of the plurality of strategy spaces; and
after performing one of the plurality of times of release, determining a strategy feasible region of both the ego vehicle and the game object based on each released strategy space, and determining a traveling decision-making result of the ego vehicle based on the strategy feasible region.
15 . The apparatus according to claim 14 , wherein
a dimension of the plurality of strategy spaces comprises at least one of the following: a longitudinal sampling dimension, a lateral sampling dimension, or a temporal sampling dimension.
16 . The apparatus according to claim 15 , wherein the performing a plurality of times of release of the plurality of strategy spaces comprises performing a release in a sequence of the following dimensions: the longitudinal sampling dimension, the lateral sampling dimension, and the temporal sampling dimension.
17 . The apparatus according to claim 14 , wherein when the strategy feasible region of both the ego vehicle and the game object is determined, a total cost value of a behavior-action pair in the strategy feasible region is determined based on one or more of the following:
a safety cost value, a right-of-way cost value, a lateral offset cost value, a passability cost value, a comfort cost value, an inter-frame association cost value, and a risk area cost value of the ego vehicle or the game object.
18 . The apparatus according to claim 17 , wherein when the total cost value of the behavior-action pair is determined based on two or more cost values, each of the two or more cost values has a different weight.
19 . The apparatus according to claim 14 , wherein when there are two or more game objects, the traveling decision-making result of the ego vehicle is determined based on each strategy feasible region of both the ego vehicle and a respective game object.
20 . A non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores programming instructions for execution by at least one processor to:
obtain a game object of an ego vehicle;
from a plurality of strategy spaces of both the ego vehicle and the game object, perform a plurality of times of release of the plurality of strategy spaces; and
after performing one of the plurality of times of release, determine a strategy feasible region of both the ego vehicle and the game object based on each released strategy space, and determining a traveling decision-making result of the ego vehicle based on the strategy feasible region.