Lane-Change Risk When the Subject Vehicle Is Faster Than the Following Vehicle: A Case Study on the Lane-Changing Warning Model Considering Different Driving Styles
Abstract
:1. Introduction
2. Methods
2.1. Participants and Test Routes
2.2. Apparatus
2.3. Extraction of Lane-Changing Events
3. Driving Style Classification in Lane-Changing Situations
3.1. Clustering Model Establishment
3.1.1. K-Means Algorithm
3.1.2. Gaussian Mixture Model with the Inputs of K-Means Algorithm
- Initialize centroids by first shuffling all data points and randomly selecting k data points as the cluster centers.
- Compute the sum of the squared distance between each data point and k centroids and assign each data point to the possible cluster based on the principle of the minimum distance.
- Update k centroids by iterating until there is no change to the centroids.
- Compute the proportion and the mean vector of the data points in each cluster determined by the k-means algorithm.
- Take the clustering result of the k-means algorithm as the initial input parameters of the GMM (i.e., mean vector and weight of a cluster and covariance matrix of clusters).
- Run the improved GMM algorithm to obtain the optimized clustering result.
3.2. Lane-Changing Safety Indicator Selection
3.3. Clustering Results and Verification
4. Lane-Changing Warning Model Considering Different Driving Styles
4.1. Lane-Changing Safety Distance Model
- (1)
- Ensure that the SV is always in front of the FV during lane changes
- (2)
- Ensure a safety distance between the SV and the FV after entering the adjacent lane
4.2. Lane-Changing Warning Model
4.3. Recognition Results of Lane-Changing Warning Model
5. Conclusions
- For different speed ranges of the SV, the time to collision was relatively stable under high relative speed conditions (<−15 km/h), while the time gap was stable under low relative speed conditions (≥−15 km/h).
- A significant difference existed in the lane-changing durations for the three types of drivers, and the peak frequencies of the lane-changing duration for aggressive drivers, calm drivers, and conservative drivers were 3–4 s, 4–5 s, and 5–6 s, respectively.
- The overall recognition accuracy of the lane-changing warning model considering driving styles was 81%, and the overall recognition accuracy of the model for aggressive drivers was relatively higher at 84% when compared with the other two types of drivers.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Driving Styles | Driver #s | Average Time Gap (s) | Average TTC_Min (s) |
---|---|---|---|
Aggressive | 6, 7, 16, 17, 20, 22, 23, 26, 28, 40, 43, 50 | 1.36 | 4.21 |
Calm | 3, 4, 5, 9, 12, 14, 18, 21, 30, 32, 34, 35, 39, 41, 44, 45, 46, 47 | 1.55 | 5.84 |
Conservative | 2, 8, 15, 24, 25, 37 | 1.83 | 7.62 |
Speed Ranges (km/h) | Lane-Changing Safety Distance Model |
---|---|
≤ 70 | |
≤ 90 | |
≤ 110 | |
> 110 |
Speed Ranges (km/h) | Lane-Changing Warning Model |
---|---|
≤ 70 | |
≤ 90 | |
≤ 110 | |
> 110 |
(a) vS ≤ 70 km/h | ||
Warning Area | Safety Zone | |
Hazardous data | 83 | 21 |
Potential conflict/Safe data | 26 | 309 |
(b) 70 km/h < v ≤ 90 km/h | ||
Warning Area | Safety Zone | |
Hazardous data | 96 | 28 |
Potential conflict/Safe data | 25 | 277 |
(c) 90 km/h < v ≤ 110 km/h | ||
Warning Area | Safety Zone | |
Hazardous data | 80 | 24 |
Potential conflict/Safe data | 11 | 225 |
(d) v > 110 km/h | ||
Warning Area | Safety Zone | |
Hazardous data | 24 | 6 |
Potential conflict/Safe data | 11 | 45 |
Warning Area | Safety Zone | |
---|---|---|
Hazardous data | 243 | 69 |
Potential conflict/Safe data | 57 | 692 |
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Liu, T.; Wang, C.; Fu, R.; Ma, Y.; Liu, Z.; Liu, T. Lane-Change Risk When the Subject Vehicle Is Faster Than the Following Vehicle: A Case Study on the Lane-Changing Warning Model Considering Different Driving Styles. Sustainability 2022, 14, 9938. https://doi.org/10.3390/su14169938
Liu T, Wang C, Fu R, Ma Y, Liu Z, Liu T. Lane-Change Risk When the Subject Vehicle Is Faster Than the Following Vehicle: A Case Study on the Lane-Changing Warning Model Considering Different Driving Styles. Sustainability. 2022; 14(16):9938. https://doi.org/10.3390/su14169938
Chicago/Turabian StyleLiu, Tong, Chang Wang, Rui Fu, Yong Ma, Zhuofan Liu, and Tangzhi Liu. 2022. "Lane-Change Risk When the Subject Vehicle Is Faster Than the Following Vehicle: A Case Study on the Lane-Changing Warning Model Considering Different Driving Styles" Sustainability 14, no. 16: 9938. https://doi.org/10.3390/su14169938