Different Intelligent Approaches for Modeling the Style of Car Driving

Aguilar, José; Aguilar, Kristell; Chávez, Danilo; Cordero, Jorge; Puerto, Eduard

doi:10.5220/0006411902840291

Cited by 8 publications

(13 citation statements)

References 9 publications

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“…The second step determines if the created chronicles can be added to the database of unconfirmed chronicles. For this, we define two operators, as follows [3,15]:…”

Section: B Evaluation Of the Created Chroniclesmentioning

confidence: 99%

“…A generic chronicle is a chronicle with the same sequence of events, but with a temporal relationship between the events that includes a set of other chronicles [3]. Table 2 shows the procedure for the evaluation of a chronicle E:…”

Section: Definition 4: Inclusion (⸦) Between Chronicles a Chronicle mentioning

confidence: 99%

“…Our case study is a hierarchical pattern of the style of driving that has been proposed previously [3], which is composed of three levels, one to recognize the emotional states, another to recognize the driver states, and the last one the driving styles. The output of the third level (recognized emotions) feeds the second and first levels; and the status of the recognized driver is used by the first level.…”

Section: A Case Studymentioning

confidence: 99%

“…The output of the third level (recognized emotions) feeds the second and first levels; and the status of the recognized driver is used by the first level. Specifically, each level is described as follows (For more details, see [3]): The same situation (an emotion, a driving style, etc.) could be described by different chronicles, to express the diversity of contexts where the same situation can occur (for example, an aggressive behavior).…”

Section: A Case Studymentioning

confidence: 99%

“…In this section, our learning algorithm of chronicles is compared with two other schemes proposed in [3]. The first is called Ar2P (Recursive Pattern Recognition Algorithm), it is a pattern recognition algorithm inspired by the theory of pattern recognition of the mind [14].…”

Section: E Comparison With Other Workmentioning

confidence: 99%

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An approach for the structural learning of chronicles

Aguilar¹,

Aguilar²,

Mesa³

2018

ces

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The Chronicles are patterns characterized by observable events, with temporal relationships between them. In this work, we propose a model for the building of chronicles, through a learning strategy that allows defining its structure. Our approach discovers the events that compose a chronicle and the temporal relationships between them. These events are defined by changes in the descriptors/features of the modeled phenomena, according to the sequence in which they appear. We test our approach for modeling a hierarchical pattern of vehicle driving styles, which consists of three levels, one to describe the emotional states, another to describe the driver states and, finally, the last one to describe the driving styles. Finally, our approach is compared with other techniques, in classical benchmark classification problems.

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“…The second step determines if the created chronicles can be added to the database of unconfirmed chronicles. For this, we define two operators, as follows [3,15]:…”

Section: B Evaluation Of the Created Chroniclesmentioning

confidence: 99%

Section: Definition 4: Inclusion (⸦) Between Chronicles a Chronicle mentioning

confidence: 99%

Section: A Case Studymentioning

confidence: 99%

Section: A Case Studymentioning

confidence: 99%

Section: E Comparison With Other Workmentioning

confidence: 99%

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An approach for the structural learning of chronicles

Aguilar¹,

Aguilar²,

Mesa³

2018

ces

Self Cite

View full text Add to dashboard Cite

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Psychological Characteristics Estimation from On-Road Driving Data

Kimura,

Tanaka,

Yoshihara

et al. 2022

Social Computing and Social Media: Design, User Experience and Impact

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Driving assistance systems that support drivers by adapting individual psychological characteristics can provide appropriate feedback and prevent traffic accidents. As a first step toward implementing such adaptive assistance systems, this research aims to develop a model to estimate drivers' psychological characteristics, such as cognitive function, psychological driving style, and workload sensitivity, from on-road driving behavioral data using machine learning and deep learning techniques. We also investigated the relationship between driving behavior and various cognitive functions including the Trail Making test and Useful Field of View test through regression modeling. The proposed method focuses on road type information and captures various durations of time-series data observed from driving behaviors. First, we segment the driving time-series data into two road types, namely, arterial roads and intersections, to consider driving situations. Second, we further segment data into many sequences of various durations. Third, statistics are calculated from each sequence. Finally, these statistics are used as input features of machine learning models to predict psychological characteristics. The experimental results show that our model can predict a driver's cognitive function, namely, the Trail Making Test version B and Useful Field of View test scores, with Pearson correlation coefficients r of 0.579 and 0.557, respectively. Some characteristics, such as psychological driving style and workload sensitivity, are predicted with high accuracy, but whether various duration segmentation improves accuracy depends on the characteristics, and it is not effective for all characteristics. Additionally, we reveal important sensor and road types for the estimation of cognitive function.

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