Vehicle Interaction Behavior Prediction with Self-Attention
Abstract
:1. Introduction
2. Related Works
3. Model and Proposed Methods
3.1. Intent Prediction Model
3.2. Self-Attention Encoder
3.3. Interactive Feature Extraction
3.4. Model Optimization
4. Experiments and Result Analysis
4.1. Datasets
4.2. Experimental Settings
4.2.1. Training Parameter Settings
4.2.2. Evaluation Parameter Settings
4.3. Results Analysis
5. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Feature Type (Interaction Information) | Related Parameters | Parameter Representation |
---|---|---|
Between the target vehicle and the ego-vehicle | Relative speed/(m/s) | |
Relative acceleration/(m/s2) | ||
Relative position/(m) | ||
Between surrounding vehicles and the target vehicle | Relative speed/(m/s) | |
Relative acceleration/(m/s2) | ||
Lateral distance/(m) | ||
Deceleration rate to avoid a crash/(m/s2) | ||
Vehicle cluster | Relative position/(m) | |
Gap in the lane/(m) |
Intention | Precision | Recall | F1 Score |
Cut-in | 0.907 | 0.932 | 0.921 |
Cut-out | 0.912 | 0.916 | 0.910 |
No-cut | 0.938 | 0.925 | 0.931 |
Method | VC-Attention | DBN [35] | HSS + LSTM [36] | A-LSTM [37] | BN [38] |
F1 Score | 0.924 | 0.795 | 0.868 | 0.802 | 0.61 |
Precision | 0.925 | 0.705 | 0.884 | 0.790 | 0.53 |
Recall | 0.924 | 0.94 | 0.862 | 0.814 | 0.72 |
Accuracy | 0.924 | 0.675 | 0.965 | 0.799 | 0.57 |
Time to event/s | 2.9 | 3.75 | 1.5 | 1.198 | 1.03 |
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Li, L.; Sui, X.; Lian, J.; Yu, F.; Zhou, Y. Vehicle Interaction Behavior Prediction with Self-Attention. Sensors 2022, 22, 429. https://doi.org/10.3390/s22020429
Li L, Sui X, Lian J, Yu F, Zhou Y. Vehicle Interaction Behavior Prediction with Self-Attention. Sensors. 2022; 22(2):429. https://doi.org/10.3390/s22020429
Chicago/Turabian StyleLi, Linhui, Xin Sui, Jing Lian, Fengning Yu, and Yafu Zhou. 2022. "Vehicle Interaction Behavior Prediction with Self-Attention" Sensors 22, no. 2: 429. https://doi.org/10.3390/s22020429