Complex environment model, cognitive system and cognitive method for self-driving cars based on complex networks
A technology for autonomous driving, complex environments, applied in motor vehicles, control/regulation systems, non-electric variable control, etc., can solve problems such as complexity
Active Publication Date: 2022-06-21
JIANGSU UNIV
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[0004] In order to solve the above-mentioned technical problems, the present invention provides a complex network-based self-driving car complex environment model, a cognitive system and a cognitive method. For the complexity problem, the driving style is identified based on the driving characteristic parameters used to represent the aggressiveness of driving manipulation and the mode transfer preference; secondly, based on the group behavior characteristics of the moving subjects in the environment, on the basis of the driving style identification, based on the complex network, With the moving subject as the node and the road as the constraint, a time-varying complex dynamic network is established as the complex environment model of the autonomous vehicle; finally, the nodes in the complex environment model are parametrically expressed to realize the node differential cognition of the complex environment, Use the agglomerative algorithm to stratify the nodes in the complex environment model, realize the hierarchical cognition of the complex environment, establish the disorder degree measurement method of the complex environment model, and realize the global risk situation cognition of the complex environment
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[0063] The present invention will be further described below in conjunction with the accompanying drawings.
[0064] like figure 1 Figure 1 shows the structural flow of the driving style recognition module. First, the longitudinal driving characteristic parameters, the lateral driving characteristic parameters and the mode transition characteristic parameters are extracted. The longitudinal driving characteristic parameters refer to the longitudinal acceleration a within a limited time window. + , with the relaxation time d time , the lateral driving characteristic parameter refers to the lateral acceleration root mean square RMS (a _ ), the standard deviation SD(r) of the yaw angular velocity, the left lane-changing state transition probability P(l) in the limited time window of the mode transition characteristic parameter c ) and the right lane-changing state transition probability P(r c ); then, construct the driving style feature matrix C J , the driving sty...
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The invention discloses a complex environment model, a cognitive system and a cognitive method for an autonomous driving vehicle based on a complex network. The driving characteristic parameters that represent the aggressiveness of driving manipulation and mode transfer preference are used to identify the driving style. The road is a constraint, and a time-varying complex dynamic network is established as the complex environment model of the self-driving car; finally, the nodes in the complex environment model are parametrically expressed to realize the differentiated cognition of the nodes in the complex environment, and the agglomerative algorithm is used to analyze the complex environment model The node layering in the system realizes the hierarchical cognition of the complex environment, establishes the disorder degree measurement method of the complex environment model, and realizes the global risk situation cognition of the complex environment.
Description
technical field [0001] The invention relates to the technical field of automatic driving vehicles, in particular to a complex environment model, a cognitive system and a cognitive method of an automatic driving vehicle based on a complex network. Background technique [0002] A complex network is a network that presents a high degree of complexity and is an abstraction of a complex system. It generally has some or all of the properties of self-organization, self-similarity, attractor, small world, and scale-free. The characteristic of complex network is complexity, which is embodied in: large network scale, complex connection structure, node complexity (such as node dynamics complexity and node diversity), complex spatiotemporal evolution process of network, sparseness of network connection, and more. Complexity fusion, etc. The research methods of the complexity of complex networks, such as: node complexity, connection structure complexity and network spatiotemporal evolut...
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IPC IPC(8): G05D1/02
CPCG05D1/0221B60W40/09B60W30/182B60W50/082B60W40/107B60W2520/105B60W2540/30
Inventor 蔡英凤滕成龙熊晓夏王海孙晓东刘擎超
Owner JIANGSU UNIV