A method that distinguishes between urban road environment types, based on traffic sign (TS) and crossroad (CR) data is presented in this paper. The types and the along-the-route locations of the TSs and the CRs ‒ encountered during car trips ‒ are recorded either by a human data entry assistant, or by an advanced driver assistance subsystem that has been enhanced for the purpose. A feed-forward artificial neural network (ANN) ‒ trained in a supervised manner ‒ carries out the classification tasks. ANNs with different topologies and training regimes are considered and tested for the purpose. These ANNs are characterized by different degrees of modularity ranging from fully modular to non-modular networks. The fully modular ANN consists of three functional modules. Two of these three were trained initially as standalone ANNs, to infer the actual road environment type solely from the TS and the CR data, respectively. The outputs of these two modules are combined via the third module. Further synapses supplement the module-level connections in the less modular ANNs. During the training of the full ANN, the TS and the CR modules are kept relatively intact, while the weights and the biases within the merger module can evolve. Test results for the considered ANNs are provided and compared.
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