US 2024/0190442 A1Published application

Complex network-based complex environment model, cognition system, and cognition method of autonomous vehicle

AssigneeJiangsu University

InventorsYingfeng CAI, Chenglong TENG, Xiaoxia XIONG, Hai WANG, Xiaodong SUN, Qingchao LIU

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Abstract

Based on a perception of an external environment of an autonomous vehicle, a driving style is recognized according to driving characteristic parameters indicating a driving aggressiveness degree and a mode shift preference, in response to a complexity of an individual driving behavior cognition. After the driving style is recognized, in accordance with group behavior characteristics of the motion bodies in the environment, a time-varying complex dynamical network is established based on a complex network with the motion bodies as nodes and roads as constraints, to serve as a complex environment model of the autonomous vehicle. Finally, the nodes in the complex environment model are parametrically represented to realize the node difference cognition of the complex environment. The nodes in the complex environment model are hierarchized by using an agglomerative algorithm to realize the hierarchical cognition of the complex environment.

Claims16

Dependent claims

A complex network-based complex environment model of an autonomous vehicle, comprising a time-varying complex dynamical network G constructed as a complex environment model by using motion bodies as nodes:

G=(V,B,X,P,Θ)
wherein G is the time-varying complex dynamical network;
V is a set of the nodes in the time-varying complex dynamical network G;
B is a set of edges in the time-varying complex dynamical network G, and represents inter-node connection lines;
X is a state vector of a node in the time-varying complex dynamical network G;
P is an intensity function of an edge in the time-varying complex dynamical network G, and represents an inter-node coupling relationship;
Θ is an area function of the time-varying complex dynamical network G, and represents a dynamic constraint for the time-varying complex dynamical network G;
the time-varying complex dynamical network G is equated to a continuous-time dynamical system with N nodes;
assuming that a state variant of an i-th node is xi, a kinetic equation of the i-th node is:
x
•
i
=
f
⁡
(
x
i
)
+
ξ
⁢
∑
j
=
1
N
p
i
⁢
j
(
t
)
⁢
H
⁡
(
x
j
)
,
(
i
=
1
,
2
,
…
,
N
)
wherein ƒ(xi) is an argument function of the state variant of the i-th node;
ξ>0 is a strength coefficient of a common connection relation;
pij(t) is a coupling coefficient between the i-th node and a j-th node;
H(xj) is an inter-node inline function, and is a function about a driving style and a node distance;
defining X=[x1, x2, . . . , xN]T, F(X)=[ƒ(x1), ƒ(x2), . . . , ƒ(xN)]T, P(t)=[(pij(t))]ΣRN×N, and H(X)=[H(x1), H(x2), . . . , H(xN)]T, a node system kinetic equation of the time-varying complex dynamical network G is as follows:
{dot over (X)}=F(X)+ξP(t)H(X)
wherein X is the state vector of the node in the time-varying complex dynamical network G;
F(X) is a dynamical equation vector of the node in the time-varying complex dynamical network G;
P(t) is a coupling matrix of the nodes in the time-varying complex dynamical network G;
H(X) is a node inline vector in the time-varying complex dynamical network G;
and
in the complex environment model, with a movement of the nodes and a change of an environment, positions and states of the nodes change dynamically, and there are nodes entering and flowing out of the time-varying complex dynamical network, the inter-node coupling relationship and the area function of the time-varying complex dynamical network change accordingly, and a complex network system continuously evolves over time.

62 more elements

A cognition method using a complex network-based cognition system of an autonomous vehicle, comprising the following steps:

step 1):
extracting a longitudinal driving characteristic parameter, a lateral driving characteristic parameter, and a mode shift characteristic parameter, constructing a driving style characteristic matrix CJ, generating a random forest classifier Rf, inputting the driving style characteristic matrix CJ into the random forest classifier Rf, outputting a driving style category Kdrive through the random forest classifier Rr, and recognizing a driving style as an aggressive category, a peaceful category, or a conservative category;
step 2):
constructing a time-varying complex dynamical network G as a complex environment model, to describe overall correlation characteristics of a complex environment;
further establishing a node kinetic equation in the complex environment model;
then combining a dynamical equation vector F(X) of all the nodes in the time-varying complex dynamical network G, a coupling matrix P(t) of the nodes in the time-varying complex dynamical network G, and a node inline vector H(X), to establish a node system kinetic equation of the time-varying complex dynamical network G to describe dynamic characteristics of the complex environment;
step 3):
constructing four parameters of the nodes in the complex environment model:
measure gi, degree ki, node weight si, and importance I(i), and performing a differentiated analysis on the nodes by using a normal distribution graph, to implement a differentiated cognition of the nodes;
step 4):
hierarchizing the nodes in the complex environment model by using an agglomerative algorithm, to implement a hierarchal, stepped cognition of the complex environment of the autonomous vehicle;
and
step 5):
measuring a disorder degree of the complex environment model by using system entropy and an entropy change according to a basic idea of an entropy theory, and describing an overall risk and a changing trend, to implement a global common state cognition.

11 more elements

Dependencies are inferred from the claim text.