A Double-Layer LSTM Model Based on Driving Style and Adaptive Grid for Intention-Trajectory Prediction
In the evolution of autonomous vehicles (AVs), ensuring safety is of the utmost significance. Precise trajectory prediction is indispensable for augmenting vehicle safety and system performance in intricate environments. This study introduces a novel double-layer long short-term memory (LSTM) model...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/7/2059 |
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| author | Yikun Fan Wei Zhang Wenting Zhang Dejin Zhang Li He |
| author_facet | Yikun Fan Wei Zhang Wenting Zhang Dejin Zhang Li He |
| author_sort | Yikun Fan |
| collection | DOAJ |
| description | In the evolution of autonomous vehicles (AVs), ensuring safety is of the utmost significance. Precise trajectory prediction is indispensable for augmenting vehicle safety and system performance in intricate environments. This study introduces a novel double-layer long short-term memory (LSTM) model to surmount the limitations of conventional prediction methods, which frequently overlook predicted vehicle behavior and interactions. By incorporating driving-style category values and an improved adaptive grid generation method, this model achieves more accurate predictions of vehicle intentions and trajectories. The proposed approach fuses multi-sensor data collected by perception modules to extract vehicle trajectories. By leveraging historical trajectory coordinates and driving style, and by dynamically adjusting grid sizes according to vehicle dimensions and lane markings, this method significantly enhances the representation of vehicle motion features and interactions. The double-layer LSTM module, in conjunction with convolutional layers and a max-pooling layer, effectively extracts temporal and spatial features. Experiments conducted using the Next Generation Simulation (NGSIM) US-101 and I-80 datasets reveal that the proposed model outperforms existing benchmarks, with higher intention accuracy and lower root mean square error (RMSE) over 5 s. The impact of varying sliding window lengths and grid sizes is examined, thereby verifying the model’s stability and effectiveness. |
| format | Article |
| id | doaj-art-8cceb0bef02b4967b00bb7eab1cb7d0d |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-8cceb0bef02b4967b00bb7eab1cb7d0d2025-08-20T02:09:11ZengMDPI AGSensors1424-82202025-03-01257205910.3390/s25072059A Double-Layer LSTM Model Based on Driving Style and Adaptive Grid for Intention-Trajectory PredictionYikun Fan0Wei Zhang1Wenting Zhang2Dejin Zhang3Li He4College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518000, ChinaInstitute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area, Shenzhen Polytechnic University, Shenzhen 518000, ChinaCollege of Electronics and Information Engineering, Shenzhen University, Shenzhen 518000, ChinaSchool of Architecture & Urban Planning, Shenzhen University, Shenzhen 518000, ChinaCollege of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518000, ChinaIn the evolution of autonomous vehicles (AVs), ensuring safety is of the utmost significance. Precise trajectory prediction is indispensable for augmenting vehicle safety and system performance in intricate environments. This study introduces a novel double-layer long short-term memory (LSTM) model to surmount the limitations of conventional prediction methods, which frequently overlook predicted vehicle behavior and interactions. By incorporating driving-style category values and an improved adaptive grid generation method, this model achieves more accurate predictions of vehicle intentions and trajectories. The proposed approach fuses multi-sensor data collected by perception modules to extract vehicle trajectories. By leveraging historical trajectory coordinates and driving style, and by dynamically adjusting grid sizes according to vehicle dimensions and lane markings, this method significantly enhances the representation of vehicle motion features and interactions. The double-layer LSTM module, in conjunction with convolutional layers and a max-pooling layer, effectively extracts temporal and spatial features. Experiments conducted using the Next Generation Simulation (NGSIM) US-101 and I-80 datasets reveal that the proposed model outperforms existing benchmarks, with higher intention accuracy and lower root mean square error (RMSE) over 5 s. The impact of varying sliding window lengths and grid sizes is examined, thereby verifying the model’s stability and effectiveness.https://www.mdpi.com/1424-8220/25/7/2059trajectory predictionLSTMdriving stylegrids in interactionautonomous vehicle |
| spellingShingle | Yikun Fan Wei Zhang Wenting Zhang Dejin Zhang Li He A Double-Layer LSTM Model Based on Driving Style and Adaptive Grid for Intention-Trajectory Prediction Sensors trajectory prediction LSTM driving style grids in interaction autonomous vehicle |
| title | A Double-Layer LSTM Model Based on Driving Style and Adaptive Grid for Intention-Trajectory Prediction |
| title_full | A Double-Layer LSTM Model Based on Driving Style and Adaptive Grid for Intention-Trajectory Prediction |
| title_fullStr | A Double-Layer LSTM Model Based on Driving Style and Adaptive Grid for Intention-Trajectory Prediction |
| title_full_unstemmed | A Double-Layer LSTM Model Based on Driving Style and Adaptive Grid for Intention-Trajectory Prediction |
| title_short | A Double-Layer LSTM Model Based on Driving Style and Adaptive Grid for Intention-Trajectory Prediction |
| title_sort | double layer lstm model based on driving style and adaptive grid for intention trajectory prediction |
| topic | trajectory prediction LSTM driving style grids in interaction autonomous vehicle |
| url | https://www.mdpi.com/1424-8220/25/7/2059 |
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