Driver Intention Recognition for Mine Transport Vehicle Based on Cross-Modal Knowledge Distillation
Abstract
:1. Introduction
- (1)
- An experimental framework was designed to collect driving intention signals from mine transport vehicles. Driving videos were recorded from two coal mine transport vehicles, processed into video data for intention analysis, and used as stimuli to gather EEG signals related to driving intentions. Additionally, a simulation environment for mine transport vehicle operation was created, allowing multiple subjects to view the videos and generate corresponding EEG data.
- (2)
- A driving intention recognition model for mine transport vehicles was developed using CMKD. An EEG-based intention recognition model was first created to decode driver intentions. This model served as a teacher, providing intention information to train a video-based student model. Guided by the teacher model, the student model achieved effective intention recognition even in the absence of direct EEG data.
2. Materials and Methods
2.1. Experimental Personnel and Stimulus Materials
2.2. Experimental Platform and Procedure
2.3. Data Preprocessing
2.4. Driving Intention Characteristics Analysis
2.5. EEG Feature Extraction
2.6. Construction of a Driving Intention Model Based on Cross-Modal Knowledge Distillation
3. Result and Discussion
3.1. Driving Intention Recognition Analysis Based on Video Data
3.2. Driving Intention Recognition Analysis Based on EEG Features
3.3. Driving Intention Recognition Analysis Based on Cross-Modal Knowledge Distillation
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Subject/Driver Intention | Left | Right | Normal | Avoidance | Scale (μV) |
---|---|---|---|---|---|
Subject 1 | |||||
Subject 2 | |||||
Subject 3 |
Algorithm | Classes | Avg. Precision (%) | Avg. Recall (%) | Avg. F1 (%) | Avg. Accuracy (%) | Model File Size (MB) |
---|---|---|---|---|---|---|
R3D | left | 74.29 | 25.00 | 33.13 | 45.63 ± 5.23 | 129.753 |
right | 76.91 | 35.00 | 40.22 | |||
normal | 39.65 | 95.00 | 54.40 | |||
avoidance | 78.57 | 27.50 | 38.67 | |||
R(2+1)D | left | 79.33 | 27.50 | 38.93 | 53.13 ± 4.94 | 129.862 |
right | 63.79 | 52.50 | 56.18 | |||
normal | 70.70 | 57.50 | 57.71 | |||
avoidance | 45.92 | 75.00 | 51.71 | |||
LRCN_18 | left | 61.24 | 40.00 | 45.70 | 59.38 ± 3.83 | 57.097 |
right | 59.09 | 45.00 | 47.34 | |||
normal | 56.25 | 95.00 | 68.26 | |||
avoidance | 94.00 | 57.50 | 66.99 | |||
LRCN_34 | left | 23.33 | 20.00 | 21.43 | 47.50 ± 5.13 | 96.646 |
right | 45.67 | 47.50 | 44.17 | |||
normal | 55.12 | 75.00 | 59.83 | |||
avoidance | 74.17 | 47.50 | 46.52 | |||
SlowFast_32 | left | 45.33 | 25.00 | 31.74 | 57.50 ± 5.23 | 84.450 |
right | 50.16 | 47.50 | 47.14 | |||
normal | 66.62 | 77.50 | 68.97 | |||
avoidance | 73.33 | 80.00 | 70.95 | |||
SlowFast_50 | left | 13.33 | 7.50 | 9.52 | 46.25 ± 8.67 | 131.569 |
right | 43.89 | 35.00 | 37.75 | |||
normal | 54.71 | 80.00 | 61.80 | |||
avoidance | 57.06 | 62.50 | 54.85 |
Algorithm | Classes | Avg. Precision (%) | Avg. Recall (%) | Avg. F1 (%) | Avg. Accuracy (%) |
---|---|---|---|---|---|
CNN | left | 66.97 | 69.84 | 68.34 | 69.28 ± 0.62 |
right | 72.22 | 64.57 | 68.17 | ||
normal | 74.24 | 69.18 | 71.60 | ||
avoidance | 64.91 | 73.73 | 69.02 | ||
LSTM | left | 63.17 | 68.80 | 65.81 | 67.86 ± 0.53 |
right | 71.23 | 64.10 | 67.42 | ||
normal | 70.78 | 71.79 | 71.27 | ||
avoidance | 66.73 | 66.87 | 66.79 | ||
CNN_LSTM | left | 56.87 | 60.73 | 58.73 | 59.38 ± 1.55 |
right | 60.12 | 52.86 | 56.48 | ||
normal | 63.21 | 61.74 | 62.43 | ||
avoidance | 57.21 | 62.45 | 59.75 | ||
CNN_BiGRU | left | 58.34 | 58.95 | 58.62 | 60.08 ± 1.68 |
right | 59.65 | 59.14 | 59.37 | ||
normal | 63.10 | 60.58 | 61.81 | ||
avoidance | 59.40 | 61.57 | 60.45 | ||
Transformer | left | 63.51 | 67.44 | 65.39 | 67.83 ± 1.01 |
right | 67.34 | 66.10 | 66.66 | ||
normal | 72.43 | 69.56 | 70.87 | ||
avoidance | 68.55 | 68.24 | 68.33 | ||
CNN_Attention | left | 53.01 | 55.39 | 54.16 | 56.75 ± 1.36 |
right | 58.56 | 54.86 | 56.63 | ||
normal | 59.62 | 59.23 | 59.40 | ||
avoidance | 55.83 | 57.45 | 56.60 | ||
CNN_Transformer | left | 56.20 | 58.32 | 57.11 | 59.09 ± 2.28 |
right | 60.66 | 55.53 | 57.95 | ||
normal | 63.46 | 58.36 | 60.63 | ||
avoidance | 57.04 | 64.22 | 60.39 | ||
LSTM_Attention | left | 61.95 | 66.39 | 64.09 | 67.49 ± 0.68 |
right | 72.46 | 64.29 | 68.12 | ||
normal | 70.37 | 70.12 | 70.54 | ||
avoidance | 65.66 | 68.53 | 67.06 | ||
CNN_TCN | left | 63.05 | 63.35 | 63.09 | 65.71 ± 0.67 |
right | 67.69 | 65.53 | 66.42 | ||
normal | 67.92 | 68.79 | 68.23 | ||
avoidance | 64.81 | 65.00 | 64.86 | ||
TCN_CNN | left | 69.86 | 70.37 | 70.11 | 71.55 ± 0.51 |
right | 72.55 | 68.95 | 70.68 | ||
normal | 73.18 | 76.91 | 74.94 | ||
avoidance | 70.75 | 69.90 | 70.29 | ||
TCN_Attention | left | 61.51 | 63.87 | 62.66 | 64.78 ± 1.02 |
right | 67.03 | 63.43 | 65.05 | ||
normal | 68.40 | 68.50 | 68.44 | ||
avoidance | 62.71 | 63.24 | 62.81 | ||
TCN_LSTM | left | 57.38 | 59.68 | 58.46 | 59.61 ± 0.57 |
right | 61.23 | 57.52 | 59.25 | ||
normal | 62.03 | 61.74 | 61.84 | ||
avoidance | 58.05 | 59.51 | 58.76 |
Algorithm | Classes | Avg. Precision (%) | Avg. Recall (%) | Avg. F1 (%) | Avg. Accuracy (%) |
---|---|---|---|---|---|
CNN | left | 72.37 | 64.04 | 67.84 | 64.76 ± 0.66 |
right | 65.01 | 60.58 | 62.68 | ||
normal | 63.40 | 71.15 | 66.97 | ||
avoidance | 60.11 | 63.27 | 61.60 | ||
LSTM | left | 69.94 | 66.73 | 68.17 | 66.08 ± 0.60 |
right | 69.65 | 63.08 | 65.66 | ||
normal | 67.49 | 69.04 | 68.07 | ||
avoidance | 59.95 | 65.48 | 62.54 | ||
CNN_LSTM | left | 46.72 | 49.90 | 48.25 | 49.06 ± 2.24 |
right | 44.59 | 43.17 | 43.87 | ||
normal | 56.08 | 52.60 | 54.24 | ||
avoidance | 49.41 | 50.30 | 49.95 | ||
CNN_BiGRU | left | 52.84 | 54.14 | 53.39 | 51.75 ± 1.72 |
right | 48.14 | 49.13 | 48.59 | ||
normal | 55.58 | 55.96 | 55.73 | ||
avoidance | 50.63 | 47.79 | 49.01 | ||
Transformer | left | 68.55 | 68.46 | 68.48 | 66.64 ± 1.12 |
right | 71.57 | 59.71 | 64.79 | ||
normal | 66.27 | 70.96 | 68.51 | ||
avoidance | 62.39 | 67.40 | 64.56 | ||
CNN_Attention | left | 46.76 | 52.02 | 49.22 | 49.11 ± 2.27 |
right | 44.49 | 46.06 | 45.24 | ||
normal | 56.33 | 52.79 | 54.47 | ||
avoidance | 50.27 | 45.58 | 47.68 | ||
CNN_Transformer | left | 50.20 | 55.48 | 52.56 | 51.28 ± 3.06 |
right | 48.60 | 47.40 | 47.92 | ||
normal | 54.36 | 54.23 | 54.25 | ||
avoidance | 52.41 | 47.98 | 50.01 | ||
LSTM_Attention | left | 61.66 | 63.37 | 62.47 | 61.59 ± 0.97 |
right | 57.78 | 58.56 | 58.16 | ||
normal | 64.93 | 67.60 | 66.22 | ||
avoidance | 61.96 | 56.83 | 59.28 | ||
CNN_TCN | left | 59.15 | 61.44 | 60.19 | 58.70 ± 0.72 |
right | 54.96 | 54.04 | 54.47 | ||
normal | 63.88 | 63.17 | 63.43 | ||
avoidance | 56.98 | 56.16 | 56.51 | ||
TCN_CNN | left | 72.62 | 69.71 | 71.10 | 67.31 ± 0.93 |
right | 73.53 | 60.00 | 66.02 | ||
normal | 65.03 | 69.52 | 67.04 | ||
avoidance | 60.97 | 70.00 | 65.08 | ||
TCN_Attention | left | 56.96 | 59.52 | 58.12 | 55.87 ± 1.53 |
right | 51.59 | 52.69 | 52.04 | ||
normal | 58.30 | 59.62 | 58.94 | ||
avoidance | 56.89 | 51.63 | 54.08 | ||
TCN_LSTM | left | 49.52 | 54.42 | 51.85 | 52.86 ± 1.06 |
right | 48.66 | 50.67 | 49.62 | ||
normal | 61.91 | 54.71 | 58.07 | ||
avoidance | 53.05 | 51.63 | 52.32 |
Parameter | λ = 0.25 | λ = 0.5 | λ = 0.75 | λ = 1 | ||||
---|---|---|---|---|---|---|---|---|
T | Avg. (%) | Max (%) | Avg. (%) | Max (%) | Avg. (%) | Max (%) | Avg. (%) | Max (%) |
1 | 59.38 ± 5.41 | 68.75 | 51.25 ± 10.73 | 68.75 | 56.87 ± 8.67 | 71.88 | 67.50 ± 5.68 | 75.00 |
2 | 58.13 ± 4.19 | 62.50 | 64.37 ± 1.71 | 65.62 | 58.13 ± 3.57 | 62.50 | 49.38 ± 6.78 | 59.38 |
5 | 58.13 ± 4.74 | 65.62 | 55.00 ± 10.03 | 71.88 | 59.38 ± 6.25 | 68.75 | 60.00 ± 8.95 | 71.88 |
10 | 55.62 ± 6.01 | 65.62 | 67.50 ± 2.80 | 71.88 | 67.50 ± 4.74 | 71.88 | 57.50 ± 8.73 | 71.88 |
20 | 53.75 ± 10.46 | 71.88 | 65.62 ± 5.41 | 75.00 | 60.63 ± 10.74 | 75.00 | 75.63 ± 5.59 | 84.38 |
50 | 49.38 ± 11.69 | 68.75 | 65.62 ± 5.85 | 75.00 | 61.87 ± 9.48 | 78.12 | 73.12 ± 1.71 | 75.00 |
100 | 56.25 ± 7.97 | 68.75 | 67.50 ± 6.09 | 78.12 | 71.25 ± 6.78 | 81.25 | 67.50 ± 9.27 | 78.12 |
Parameter Combinations (λ-T) | Classes | Avg. Precision (%) | Avg. Recall (%) | Avg. F1 (%) | Avg. Accuracy (%) |
---|---|---|---|---|---|
0.75–100 | left | 76.29 | 62.50 | 67.95 | 71.25 ± 6.78 |
right | 73.31 | 65.00 | 64.57 | ||
normal | 67.28 | 100.00 | 79.49 | ||
avoidance | 82.79 | 57.50 | 66.48 | ||
1–20 | left | 91.56 | 62.50 | 72.21 | 75.63 ± 5.59 |
right | 76.64 | 77.50 | 72.14 | ||
normal | 68.27 | 100.00 | 80.44 | ||
avoidance | 95.00 | 62.50 | 73.82 | ||
1–50 | left | 87.79 | 62.50 | 71.19 | 73.12 ± 1.71 |
right | 79.01 | 62.50 | 68.07 | ||
normal | 61.64 | 100.00 | 75.68 | ||
avoidance | 88.81 | 67.50 | 75.76 |
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Zhang, Y.; Guo, Y.; You, X.; Guo, L.; Miao, B.; Li, H. Driver Intention Recognition for Mine Transport Vehicle Based on Cross-Modal Knowledge Distillation. Appl. Sci. 2025, 15, 6814. https://doi.org/10.3390/app15126814
Zhang Y, Guo Y, You X, Guo L, Miao B, Li H. Driver Intention Recognition for Mine Transport Vehicle Based on Cross-Modal Knowledge Distillation. Applied Sciences. 2025; 15(12):6814. https://doi.org/10.3390/app15126814
Chicago/Turabian StyleZhang, Yizhe, Yinan Guo, Xiusong You, Lunfeng Guo, Bing Miao, and Hao Li. 2025. "Driver Intention Recognition for Mine Transport Vehicle Based on Cross-Modal Knowledge Distillation" Applied Sciences 15, no. 12: 6814. https://doi.org/10.3390/app15126814
APA StyleZhang, Y., Guo, Y., You, X., Guo, L., Miao, B., & Li, H. (2025). Driver Intention Recognition for Mine Transport Vehicle Based on Cross-Modal Knowledge Distillation. Applied Sciences, 15(12), 6814. https://doi.org/10.3390/app15126814