Analysis of Driving Behavior Based on Dynamic Changes of Personality States
Abstract
:1. Introduction
- In the risk-free scenario, personality baselines were firstly measured by the NEO-FFI questionnaire. We aimed to establish the correspondence between driving indicators and the “Big Five” personality traits in a quantitative manner using the K-means clustering method.
- In the risk scenarios, the objective was to analyze the influence of specific driving situations and time on the personality states of different drivers from a dynamic perspective, combined with the thresholds of each indicator.
2. Methods
2.1. Sample
2.2. NEO-FFI
2.3. The Theory of K-Means Clustering
- There is no or minimum number of data objects reassigned to different clusters;
- There is no or minimum number of clustering centers changing again;
- The square of the local error is the smallest.
2.4. Analysis of Driving Behavior
3. Design of the Experiments
3.1. Design of Typical Driving Scenarios
3.1.1. Risk-Free Scenario
3.1.2. Risk Scenarios
3.2. Experimental Procedure
4. Discussion
4.1. Risk-Free Driving Scenario
4.1.1. Analysis of Driving Characteristics Based on K-Means Clustering Algorithm
4.1.2. Analysis of Dynamic Personality Based on Personality Baseline
4.2. Risky Driving Scenarios
4.2.1. The Traditional Approach to Personality in High- and Low-Risk Scenarios
4.2.2. Analysis of Dynamic Changes of Personality States in High- and Low-Risk Scenarios
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Personality | Characteristics |
---|---|
Neuroticism | Anxiety, hostility, and impulsiveness [44] |
Extroversion | Excitement seeking, activity, and warmth [44] |
Openness | Fantasy, actions, and ideas [44] |
Conscientiousness | Order, dutifulness, and self-discipline [45] |
Agreeableness | Trust, altruism, and compliance [45] |
Index | Symbolic Representation | Description |
---|---|---|
Response time | RT | This index was used to represent the driver’s reaction ability. The reaction time specifically refers to the time interval experienced by the driver from the time of the start of the stimulation to the time when he makes response. For example, the reaction time can refer to the time interval from the time when the brake light of front vehicle is on to when the driver steps on the brake pedal. |
Standard deviation of speed | SDS | This indicator was used to characterize the degree of speed fluctuations in each segment, specifically the standard deviation. |
Difference of average velocity | This indicator was used to characterize the difference in mean velocity between each segment and the entire driving process. |
K-Mean | Center of the Cluster | Minimum/Maximum of the Indexes | ||||
---|---|---|---|---|---|---|
Personality | RT | SDS | RT | SDS | ||
Conscientiousness | 2.570 | 2.342 | −1.947 | 2.15/3.91 | 0/6.787 | −4.941/4.749 |
Extroversion | 2.043 | 3.862 | 8.962 | 0.6/2.81 | 0.625/8.956 | 4.876/16.189 |
Agreeableness | 2.987 | 2.797 | −9.107 | 3.45/5.1 | 0.842/14.031 | −15.457/−5.007 |
Neuroticism | 2.525 | 12.543 | 11.392 | 0.8/3.03 | 7.672/15.497 | −4.032/18.607 |
Openness | 3.314 | 10.945 | −1.529 | 2.8/5.44 | 6.579/12.864 | −9.762/4.897 |
Segment | Description |
---|---|
1 | The tested driver starts to drive with speed limit of 50 km/h |
2 | Maintain or adjust speed |
3 | ① When the driver reaches 105 m, the pedestrian starts to cross the street ② The interaction stage between driver and pedestrian. The driver adjusts driving behavior to Avoid collision ③ The interaction is completed, and the driver continuously slows down or maintains the original behavior |
4 | Return to acceleration |
5 | Continue driving |
6 | Continue driving |
Index | Segment | Extroversion | Conscientiousness | Agreeableness | Neuroticism | Openness | |||||
---|---|---|---|---|---|---|---|---|---|---|---|
Risk Scenario | Risk Scenario | Risk Scenario | Risk Scenario | Risk Scenario | |||||||
Low | High | Low | High | Low | High | Low | High | Low | High | ||
Difference of average velocity () | 2 | 9.89 | 11.774 | 5.111 | 9.565 | 15.742 | 14.117 | 12.475 | 12.913 | 12.45 | 15.742 |
3 | −1.874 | −9.734 | −4.083 | 0.358 | −5.759 | −8.607 | −7.324 | −10.332 | −3.9 | −9.794 | |
4 | −12.597 | 2.221 | −0.542 | −11.67 | −4.43 | 2.65 | −2.009 | 0.843 | −8.328 | −2.192 | |
5 | 7.695 | 15.208 | 6.123 | 1.032 | 5.964 | 13.488 | 11.981 | 17.24 | 7.579 | 15.832 | |
Standard deviation of speed (SDS) | 2 | 0.741 | 0.657 | 2.082 | 0.589 | 0.594 | 1.356 | 0.301 | 4.669 | 0.76 | 0.829 |
3 | 4.483 | 5.669 | 1.722 | 2.403 | 4.28 | 5.166 | 4.86 | 5.035 | 4.413 | 6.452 | |
4 | 9.254 | 8.073 | 2.542 | 6.566 | 6.085 | 8.135 | 8.638 | 8.501 | 8.576 | 7.12 | |
5 | 1.77 | 1.062 | 2.331 | 2.982 | 1.534 | 1.941 | 1.574 | 1.867 | 1.996 | 2.529 |
Scenario | Segment | Distance | Time | Personality State | SDS | Average Speed | Max. Speed | Min. Speed | |
---|---|---|---|---|---|---|---|---|---|
High-risk scenario | 2 | 100 | 3.5–7.05 | Extroversion | 0.565 | 51.475 | 52.402 | 50.571 | |
3 | ② | 138 | 9.70 | Extroversion | 0.366 | 52.227 | 52.593 | 51.231 | |
145 | 10.25 | Conscientiousness | 3.819 | 45.100 | 50.410 | 39.037 | |||
③ | 150 | 10.77 | Agreeableness | 4.522 | 30.544 | 37.247 | 23.750 | ||
4 | 200 | 17.60 | Agreeableness | 9.306 | 26.815 | 43.303 | 13.210 | ||
5 | 250 | 21.45 | Conscientiousness | 1.960 | 47.052 | 50.385 | 43.509 | ||
Low-risk scenario | 2 | 100 | 3.57–7.25 | Extroversion | 1.858 | 48.358 | 49.426 | 42.766 | |
3 | ② | 131 | 10.25 | Extroversion | 1.598 | 38.307 | 42.498 | 36.860 | |
145 | 15.35 | Agreeableness | 9.217 | 9.178 | 36.161 | 0.000 | |||
③ | 150 | 16.85 | Agreeableness | 1.137 | 12.355 | 14.248 | 10.451 | ||
4 | 200 | 23.85 | Conscientiousness | 6.679 | 25.719 | 34.637 | 14.389 | ||
5 | 250 | 28.57 | Extroversion | 1.826 | 38.339 | 40.839 | 34.720 |
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Wang, F.; Zhang, J.; Wang, S.; Li, S.; Hou, W. Analysis of Driving Behavior Based on Dynamic Changes of Personality States. Int. J. Environ. Res. Public Health 2020, 17, 430. https://doi.org/10.3390/ijerph17020430
Wang F, Zhang J, Wang S, Li S, Hou W. Analysis of Driving Behavior Based on Dynamic Changes of Personality States. International Journal of Environmental Research and Public Health. 2020; 17(2):430. https://doi.org/10.3390/ijerph17020430
Chicago/Turabian StyleWang, Fanyu, Junyou Zhang, Shufeng Wang, Sixian Li, and Wenlan Hou. 2020. "Analysis of Driving Behavior Based on Dynamic Changes of Personality States" International Journal of Environmental Research and Public Health 17, no. 2: 430. https://doi.org/10.3390/ijerph17020430