# Revealing Driver’s Natural Behavior—A GUHA Data Mining Approach

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## Abstract

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## 1. Introduction

## 2. The GUHA Method Briefly

ψ | ¬ψ | ||

φ | a | b | r = a + b |

¬φ | c | d | s = c + d |

k = a + c | l = b + d | m = a + b + c + d |

- a is the number of objects satisfying both φ and ψ,
- b is the number of objects satisfying φ but not ψ,
- c is the number of objects not satisfying φ, satisfying ψ,
- d is the number of objects not satisfying φ nor ψ.

## 3. Presentation of the Analyzed Data

- Drivers A, …, J, (10 in all),
- Acceleration Speed Lateral,
- Acceleration Speed Longitudinal,
- Acceleration Pedal Value,
- Acceleration Pedal Value (RN),
- Fuel Consumption,
- Master Cylinder Pressure,
- Cylinder Pressure (RN),
- Steering Wheel Angle,
- Steering Wheel Angle (RN),
- Vehicle Speed,
- Vehicle Speed (RN).

## 4. Data Preprocessing

- Acceleration Speed Lateral (extra low),
- Steering Wheel-Angel (higher),
- Vehicle Speed (lower),
- Driver (D).

- φ stands for Driver(D),
- ψ stands for Steering Wheel Angle(higher) & Vehicle Speed(lower).

## 5. Analytical Questions and the Most Relevant Answers to Them

#### 5.1. The First Analytic Question: Which Hypotheses Distinguish Each Driver from All Other Drivers

AccelPedal | Value | & | Accel | Pedal | ValueRN | & | Master | Cylinder | PressureRN | & | SteerWheSpeedRN |

AccelPedal | Value | & | Accel | Pedal | ValueRN | & | Master | Cylinder | PressureRN | & | SteerWheSpeedRN |

#### 5.2. The Second Analytic Question: Comparing One Driver Separately in Pairs with Each of the Other Drivers (up to 3), Which Are the Most Distinguishing Attributes

#### 5.3. The Third Analytic Question: Comparing One Driver Separately in Pairs with Each of the Other Drivers, Which Attribute (up to 3) Values Show Significant Differences between Drivers

## 6. Observations

## 7. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 3.**LISp-Miner answers to the analytic question ‘Which hypotheses distinguish each driver from all other drivers?

**Figure 4.**Bayesian theory-based interpretation of the statistical significance of the hypothesis G: model.

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Turunen, E.; Dolos, K.
Revealing Driver’s Natural Behavior—A GUHA Data Mining Approach. *Mathematics* **2021**, *9*, 1818.
https://doi.org/10.3390/math9151818

**AMA Style**

Turunen E, Dolos K.
Revealing Driver’s Natural Behavior—A GUHA Data Mining Approach. *Mathematics*. 2021; 9(15):1818.
https://doi.org/10.3390/math9151818

**Chicago/Turabian Style**

Turunen, Esko, and Klara Dolos.
2021. "Revealing Driver’s Natural Behavior—A GUHA Data Mining Approach" *Mathematics* 9, no. 15: 1818.
https://doi.org/10.3390/math9151818