An Overview on Study of Identification of Driver Behavior Characteristics for Automotive Control

Abstract

Driver characteristics have been the research focus for automotive control. Study on identification of driver characteristics is provided in this paper in terms of its relevant research directions and key technologies involved. This paper discusses the driver characteristics based on driver’s operation behavior, or the driver behavior characteristics. Following the presentation of the fundamental of the driver behavior characteristics, the key technologies of the driver behavior characteristics are reviewed in detail, including classification and identification methods of the driver behavior characteristics, experimental design and data acquisition, and model adaptation. Moreover, this paper discusses applications of the identification of the driver behavior characteristics which has been applied to the intelligent driver advisory system, the driver safety warning system, and the vehicle dynamics control system. At last, some ideas about the future work are concluded.

Smartphone-based outlier detection: a complex event processing approach for driving behavior detection

“…Currently, approaches that evaluate driving behavior in general use models and techniques (e.g., Neural Networks, Fuzzy Theory, and Hidden Markov models) with good accuracy [58]. However, they were not designed for data stream processing [40], and according to Lin et al and Wang, Xi, and Chen [58,59], have low processing performances, require a long training phase, artificial assumptions, or prior knowledge to formulate rules.…”

Neural Network Based Approach for the Generation of Road Feel in a Steer-By-Wire System

“…Junaid et al proposed a cosine function neural network method by taking tire-road signal as an error to the system [17]. Na et al developed a neural network based system for the identification of driver behaviour characteristics [18]. by-wire system by training the neural network and compared with the genetic algorithm [19].…”

Detecção Móvel E Online De Anomalia Em Múltiplos Fluxos De Dados: Uma Abordagem Baseada Em Processamento De Eventos Complexos Para Detecção De Comportamento De Condução

“…As técnicas atualmente utilizadas para avaliação do estilo de condução, em sua grande maioria, utilizam modelos/técnicas (e.g., modelos baseados em redes neurais, teoria fuzzy e modelos ocultos de Markov) com uma boa acurácia [33].…”

“…Contudo, não foram projetados para o processamento de fluxo de dados [34], e de acordo com [33], [35], [36]: redes neurais possuem baixo desempenho de processamento em tempo real, impossibilita entender o comportamento físico por se tratar de uma "caixa-preta" e como possui método empírico de ajuste dos parâmetros (e.g., número de camadas), pode ter uma fase de treinamento longa.…”

“…Além disso, estes modelos/técnicas de identificação do comportamento do motorista são estáticos [33], ou seja, seus parâmetros são pré-fixados. Por exemplo, redes neurais requerem um número fixo de parâmetros de entrada [33], [36]. Contudo, as situações de condução (que são influenciadas pelo estado psicológico do motorista, por fatores do trânsito e clima)…”