Research on the Driving Behavior and Decision-Making of Autonomous Vehicles (AVs) in Mixed Traffic Flow by Integrating Bilayer-GRU-Att and GWO-XGBoost Models
Abstract
:1. Introduction
2. Model Framework
- The Bilayer-GRU-Att model aims to capture and predict the dynamic behavior of vehicles in complex traffic environments and simulate the nonlinear dynamic characteristics of vehicles during driving. The model consists of four parts: the input layer, the Bilayer-GRU network (encoder–decoder), the attention mechanism layer (located between the first GRU and the second GRU) [14], and the fully connected layer for trajectory output. Firstly, the input layer performs filtering and standardization on the vehicle trajectory data, and reconstructs feature vectors including vehicle coordinates, longitudinal speed, lateral speed, longitudinal acceleration, lateral acceleration, and heading angle. Then, the Bilayer-GRU captures the contextual information in the time sequence through a double-layer Gated Recurrent Unit (GRU) network and completes the encoding and decoding process. Here, the attention mechanism simulates the ability of human drivers to quickly focus on key target information, prevents the loss of high-value information to make up for the shortcomings of traditional encoders in micro-lane-changing features, and thus improves the accuracy of trajectory prediction. Finally, through the fully connected layer, the model generates trajectory prediction results for future time steps.
- First, the GWO-XGBoost model further processes the trajectory prediction results generated by the Bilayer-GRU-Att model to extract the key features. Then, the feature splicing module fuses vehicle trajectory data in different prediction time-domains to generate input feature sets. Furthermore, the eXtreme Gradient Boosting model (XGBoost) [15] is optimized by the Grey Wolf Optimization model (GWO) [16], which is used to decode and judge these feature sets, so as to achieve the accurate identification of vehicle lane-changing intention. GWO optimization is used to improve the effectiveness of feature selection and the optimization of XGBoost parameters, thus enhancing the accuracy and robustness of recognition.
3. Data Preprocessing and Fragment Extraction
3.1. Data Source and Preprocessing
3.2. Data Filtering
- The types of vehicles collected in the HighD data include cars and trucks. Because trucks are always on the right lane of the road during driving and the frequency of lane-changing is far less than that of cars, in order to truly reflect the lane-changing decision-making behavior of vehicles on the highway, the driving information of cars in the dataset is selected.
- A total of 4191 sets of vehicle trajectory data are screened from the HighD dataset, including 2123 sets of lane-changing trajectories and 2068 sets of non-lane-changing trajectories. The selected data are collated and the vehicle driving information is recorded as discrete points, where the ordinate direction is the same as the driving direction of the vehicle. Table 1 shows some processed vehicle trajectory data from the HighD dataset, for the fourth vehicle driving in the positive direction of the y-axis, which changed lanes from the middle to the right, starting at 9:20 a.m. on Monday, October 2017.
3.3. Data Fragment Extraction
4. Vehicle Trajectory Prediction Model
4.1. Model Structure
4.2. Bilayer-GRU-Att Model Mechanism
4.2.1. Coding Process
4.2.2. Att Model Mechanism
4.2.3. Decoding Process
4.2.4. Trajectory Output
5. Lane-Changing Intention Identification Model
5.1. Model Structure
5.2. Mechanism of GWO-XGBoost Model
6. Experiment and Analysis
6.1. Experimental Environment Configuration
6.2. Comparative Analysis of Models
6.2.1. Comparison of Trajectory Prediction Models
6.2.2. Comparison of Lane-Changing Decision Models
- Prediction of “turning left” driving intention: GWO-XGBoost also performed well in identifying “turning left” driving intentions. The precision rate, recall rate, F1 score improved by 31.69%, 4.48%, and 18.55%, respectively, compared with ELM. Compared with the BP model, these indexes are also significantly improved by 18.44%, 3.40%, and 11.17%, respectively.
- Prediction of “going straight” driving intention: GWO-XGBoost has excellent performance in “going straight” driving intention prediction. Compared with ELM, its precision rate is improved by 0.91%, recall rate is improved by 3.08%, and F1 score is improved by 1.99%. Compared with BP, the precision rate, recall rate, and F1 score also increased by 0.30%, 1.79%, and 1.05%, respectively.
- Prediction of “turning right” driving intention: The GWO-XGBoost also demonstrated excellent performance in “turing right” driving intent prediction. The precision rate, recall rate, and F1 score improved by 11.66%, 9.38%, and 10.52%, respectively, compared with ELM. Compared with BP, the improvement of these indicators also reached 6.53%, 3.46%, and 5.00%, respectively.
7. Summary
- The Bilayer-GRU-Att module proposed here exhibits a remarkable ability to capture and analyze the dynamic evolution of the traffic environment in real-time. This capability enables the system to accurately predict the driving state of the target vehicle across different tpred. The module demonstrates superior performance in trajectory prediction, achieving the best prediction error evaluation when compared to benchmarking models.
- The GWO-XGboost module significantly enhances the predictability and accuracy of lane-changing intention recognition. By incorporating information from the Bilayer-GRU-Att module, the GWO-XGboost model effectively decodes and judges feature sets, resulting in the accurate identification of vehicle lane-changing intentions. This integrated approach not only improves the effectiveness of feature selection but also optimizes XGBoost parameters, thereby enhancing the overall accuracy and robustness of the recognition system.
- The experimental results obtained using the real-world HighD dataset further validate the effectiveness of the proposed hybrid prediction model. The models’ performance in mixed human–machine traffic scenarios is particularly noteworthy, highlighting its potential for enhancing system safety in complex driving environments.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Frame | ID | X | Y | X Velocity | Y Velocity | X Acceleration | Y Acceleration | Theta |
---|---|---|---|---|---|---|---|---|
1352 | 219 | 3.800 | 25.1200 | 19.8800 | 0.1600 | −0.1100 | −0.0200 | 0.0080 |
1353 | 219 | 4.5900 | 25.1200 | 19.8700 | 0.1600 | −0.1100 | −0.0300 | 0.0080 |
1354 | 219 | 5.3800 | 25.1300 | 19.8700 | 0.1500 | −0.1100 | −0.0300 | 0.0075 |
1355 | 219 | 6.1800 | 25.1400 | 19.8600 | 0.1500 | −0.1200 | −0.0400 | 0.0075 |
1356 | 219 | 6.9800 | 25.1400 | 19.8600 | 0.1500 | −0.1200 | −0.0400 | 0.0075 |
1357 | 219 | 7.7800 | 25.1500 | 19.8600 | 0.1400 | −0.1300 | −0.0400 | 0.0070 |
1358 | 219 | 8.5700 | 25.1500 | 19.8500 | 0.1400 | −0.1400 | −0.0500 | 0.0070 |
1359 | 219 | 9.3800 | 25.1600 | 19.8500 | 0.1300 | −0.1400 | −0.0500 | 0.0065 |
1360 | 219 | 10.1700 | 25.1600 | 19.8400 | 0.1300 | −0.1500 | −0.0500 | 0.0065 |
1361 | 219 | 10.9700 | 25.1600 | 19.8300 | 0.1200 | −0.1600 | −0.0500 | 0.0060 |
Advance Prediction Time-Domain | Evaluation Index | Vehicle Trajectory Prediction Model | |||||
---|---|---|---|---|---|---|---|
Bilayer-GRU-Att | Bilayer-GRU | Single-GRU | Bi-GRU | Single-LSTM | Bilayer-LSTM | ||
tpred = 0.0 s | RMSE/m | 3.9535 | 4.6358 | 5.0859 | 5.1751 | 5.0907 | 5.6122 |
ADE/m | 3.0305 | 3.9683 | 4.4152 | 4.4672 | 4.3826 | 4.9689 | |
FDE/m | 5.5402 | 7.7539 | 9.4478 | 9.3142 | 8.5048 | 7.2470 | |
PTC/ms | 29.9526 | 27.6731 | 26.5046 | 30.3257 | 35.4763 | 38.5224 | |
tpred = 0.4 s | RMSE/m | 4.0622 | 4.7914 | 5.2472 | 5.3430 | 5.2632 | 5.9603 |
ADE/m | 3.1178 | 4.1332 | 4.5290 | 4.5997 | 4.4950 | 5.2644 | |
FDE/m | 5.7699 | 8.0921 | 9.8209 | 9.6874 | 8.8324 | 7.5189 | |
PTC/ms | 30.7508 | 28.5756 | 26.9144 | 31.3932 | 36.8574 | 40.4592 | |
tpred = 0.8 s | RMSE/m | 4.2005 | 4.9402 | 5.3714 | 5.4546 | 5.3908 | 6.1985 |
ADE/m | 3.2219 | 4.2901 | 4.6375 | 4.6807 | 4.5648 | 5.4640 | |
FDE/m | 5.9862 | 8.4817 | 10.0423 | 9.8875 | 8.9610 | 7.5816 | |
PTC/ms | 31.6852 | 29.3051 | 27.4945 | 32.7618 | 38.4371 | 42.7675 | |
tpred = 1.2 s | RMSE/m | 4.2705 | 5.0745 | 5.4794 | 5.5354 | 5.4966 | 6.3727 |
ADE/m | 3.2654 | 4.4302 | 4.7375 | 4.7297 | 4.6181 | 5.6077 | |
FDE/m | 6.1777 | 8.7548 | 10.2198 | 10.0186 | 9.0137 | 7.5324 | |
PTC/ms | 32.3931 | 30.1777 | 27.9324 | 33.6674 | 40.2769 | 45.0985 | |
tpred = 1.6 s | RMSE/m | 4.2795 | 5.1900 | 5.6276 | 5.6487 | 5.6461 | 6.5487 |
ADE/m | 3.2444 | 4.5537 | 4.8826 | 4.7959 | 4.7144 | 5.7618 | |
FDE/m | 6.3714 | 8.9751 | 10.4696 | 10.2172 | 9.1574 | 7.5723 | |
PTC/ms | 33.9952 | 30.9751 | 28.6077 | 34.4376 | 42.8436 | 48.2981 | |
tpred = 2.0 s | RMSE/m | 4.2965 | 5.2976 | 5.7988 | 5.7900 | 5.8240 | 6.7437 |
ADE/m | 3.2112 | 4.6644 | 5.0499 | 4.8803 | 4.8588 | 5.9391 | |
FDE/m | 6.5808 | 9.2402 | 10.7859 | 10.5039 | 9.3998 | 7.7354 | |
PTC/ms | 34.4371 | 31.7635 | 29.0248 | 35.1153 | 45.2921 | 51.0654 |
Predictive Intent | Turning Left | Going Straight | Turning Right | |||||||
---|---|---|---|---|---|---|---|---|---|---|
ELM | BP | GWO-XGBoost | ELM | BP | GWO-XGBoost | ELM | BP | GWO-XGBoost | ||
Real intention | Turning left | 5717 | 6357 | 7529 | 2312 | 1627 | 598 | 104 | 149 | 6 |
Going straight | 186 | 178 | 65 | 79,843 | 80,326 | 80,566 | 637 | 162 | 35 | |
Turning right | 122 | 95 | 0 | 795 | 462 | 45 | 7472 | 7832 | 8344 |
Precision Rate | Recall Rate | F1 Score | Accuracy Rate | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Evaluation Index | ELM | BP | GWO-XGBoost | ELM | BP | GWO-XGBoost | ELM | BP | GWO-XGBoost | ELM | BP | GWO-XGBoost |
Turning left | 0.7029 | 0.7816 | 0.9257 | 0.9489 | 0.9588 | 0.9914 | 0.8076 | 0.8612 | 0.9574 | 0.9572 | 0.9725 | 0.9923 |
Going straight | 0.9898 | 0.9958 | 0.9988 | 0.9625 | 0.9747 | 0.9921 | 0.9760 | 0.9851 | 0.9954 | |||
Turning right | 0.8907 | 0.9336 | 0.9946 | 0.9098 | 0.9618 | 0.9951 | 0.9001 | 0.9475 | 0.9948 |
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Wang, L.; Guan, Z.; Liu, J.; Zhao, J. Research on the Driving Behavior and Decision-Making of Autonomous Vehicles (AVs) in Mixed Traffic Flow by Integrating Bilayer-GRU-Att and GWO-XGBoost Models. World Electr. Veh. J. 2024, 15, 333. https://doi.org/10.3390/wevj15080333
Wang L, Guan Z, Liu J, Zhao J. Research on the Driving Behavior and Decision-Making of Autonomous Vehicles (AVs) in Mixed Traffic Flow by Integrating Bilayer-GRU-Att and GWO-XGBoost Models. World Electric Vehicle Journal. 2024; 15(8):333. https://doi.org/10.3390/wevj15080333
Chicago/Turabian StyleWang, Lei, Zhiwei Guan, Jian Liu, and Jianyou Zhao. 2024. "Research on the Driving Behavior and Decision-Making of Autonomous Vehicles (AVs) in Mixed Traffic Flow by Integrating Bilayer-GRU-Att and GWO-XGBoost Models" World Electric Vehicle Journal 15, no. 8: 333. https://doi.org/10.3390/wevj15080333