# Data Anomaly Detection for Internet of Vehicles Based on Traffic Cellular Automata and Driving Style

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Relative Works

#### 2.1. Traffic Flow Model

#### 2.2. Data Anomaly Detection

## 3. Model and Description

#### 3.1. Traffic Cellular Automata

#### 3.2. Rule Set of Traffic Cellular Automata

#### 3.2.1. Accelerate Rule

#### 3.2.2. Overtaking/Lane-Changing Rule

#### 3.2.3. Mandatory Deceleration Rule

#### 3.2.4. Random Slowing Rule

#### 3.3. Driving Style Quantization Model

## 4. Add Algorithm: Anomaly Detection Based on Driving Style

#### 4.1. Gaussian Mixed Model (GMM)

#### 4.2. Add Algorithm

## 5. Experiment and Analysis

#### 5.1. Experimental Results and Analysis

#### 5.1.1. Experimental Results Analysis of the First Situation

#### 5.1.2. Experimental Results Analysis of the Second Situation

#### 5.1.3. Experimental Results Analysis of the Third Situation

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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Driving Type | Condition (km/h) | Safety Distance |
---|---|---|

high speed driving | $v>100$ | $s\ge 100$ m |

fast speed driving | $70<v\le 100$ | $s\ge 80$ m |

medium speed driving | $40<v\le 70$ | $s\ge 60$ m |

low speed driving | $20<v\le 40$ | $s\ge 30$ m |

turtle speed driving | $v\le 20$ | $s\ge 10$ m |

${\mathit{R}}_{\mathit{de}}$ | Driving Style | Coefficient e |
---|---|---|

${R}_{de}<Nor{m}_{threshold}$ | Cautious (C) | 1 |

$Norm\le {R}_{de}\le Agg$ | Normal (N) | 2 |

${R}_{de}>Ag{g}_{threshold}$ | Aggressive (A) | 3 |

(a) Precision | |||

ID | HTM | LSTM | ADD |

14 | 0.76 | 0.89 | 0.90 |

233 | 0.67 | 0.86 | 0.90 |

999 | 0.60 | 0.81 | 0.88 |

2333 | 0.74 | 0.89 | 0.91 |

AVG | 0.69 | 0.86 | 0.90 |

(b) Recall | |||

ID | HTM | LSTM | ADD |

14 | 0.36 | 0.81 | 0.94 |

233 | 0.39 | 0.87 | 0.95 |

999 | 0.43 | 0.89 | 0.86 |

2333 | 0.44 | 0.95 | 0.95 |

AVG | 0.40 | 0.88 | 0.95 |

(c) F1 score | |||

ID | HTM | LSTM | ADD |

14 | 0.49 | 0.84 | 0.92 |

233 | 0.49 | 0.86 | 0.92 |

999 | 0.50 | 0.85 | 0.92 |

2333 | 0.55 | 0.92 | 0.93 |

AVG | 0.51 | 0.87 | 0.92 |

pre | rec | f1 | |
---|---|---|---|

HTM | 0.75 | 0.36 | 0.49 |

GMM | 0.79 | 0.64 | 0.71 |

ADD | 0.83 | 0.74 | 0.78 |

(a) Precision | |||

ID | HTM | LSTM | ADD |

28 | 0.31 | 0.77 | 0.84 |

78 | 0.30 | 0.81 | 0.82 |

AVG | 0.30 | 0.79 | 0.83 |

(b) Recall | |||

ID | HTM | LSTM | ADD |

28 | 0.16 | 0.67 | 0.87 |

78 | 0.13 | 0.72 | 0.91 |

AVG | 0.14 | 0.69 | 0.89 |

(c) F1 score | |||

ID | HTM | LSTM | ADD |

28 | 0.21 | 0.72 | 0.86 |

78 | 0.18 | 0.76 | 0.86 |

AVG | 0.19 | 0.74 | 0.86 |

(a) Precision | |||

ID | HTM | LSTM | ADD |

59 | 0.95 | 0.89 | 0.91 |

1202 | 0.91 | 0.82 | 0.86 |

AVG | 0.93 | 0.86 | 0.88 |

(b) Recall | |||

ID | HTM | LSTM | ADD |

59 | 0.92 | 0.95 | 0.95 |

1202 | 0.91 | 0.92 | 0.94 |

AVG | 0.91 | 0.93 | 0.94 |

(c) F1 score | |||

ID | HTM | LSTM | ADD |

59 | 0.93 | 0.92 | 0.93 |

1202 | 0.91 | 0.87 | 0.89 |

AVG | 0.91 | 0.89 | 0.91 |

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**MDPI and ACS Style**

Ding, N.; Ma, H.; Zhao, C.; Ma, Y.; Ge, H.
Data Anomaly Detection for Internet of Vehicles Based on Traffic Cellular Automata and Driving Style. *Sensors* **2019**, *19*, 4926.
https://doi.org/10.3390/s19224926

**AMA Style**

Ding N, Ma H, Zhao C, Ma Y, Ge H.
Data Anomaly Detection for Internet of Vehicles Based on Traffic Cellular Automata and Driving Style. *Sensors*. 2019; 19(22):4926.
https://doi.org/10.3390/s19224926

**Chicago/Turabian Style**

Ding, Nan, Haoxuan Ma, Chuanguo Zhao, Yanhua Ma, and Hongwei Ge.
2019. "Data Anomaly Detection for Internet of Vehicles Based on Traffic Cellular Automata and Driving Style" *Sensors* 19, no. 22: 4926.
https://doi.org/10.3390/s19224926