Exploring rounD Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction
This paper analyzes the rounD dataset to advance motion forecasting algorithms for autonomous vehicles navigating complex roundabout environments. We develop a trajectory prediction framework inspired by Gated Recurrent Unit (GRU) networks and graph-based modules to effectively model vehicle interac...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7538 |
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| author | Zikai Zhang |
| author_facet | Zikai Zhang |
| author_sort | Zikai Zhang |
| collection | DOAJ |
| description | This paper analyzes the rounD dataset to advance motion forecasting algorithms for autonomous vehicles navigating complex roundabout environments. We develop a trajectory prediction framework inspired by Gated Recurrent Unit (GRU) networks and graph-based modules to effectively model vehicle interactions. Our primary objective is to evaluate the generalizability of the proposed model across diverse training and testing datasets. Through extensive experiments, we investigate how varying data distributions—such as different road configurations and recording times—impact the model’s prediction accuracy and robustness. This study provides key insights into the challenges of domain generalization in autonomous vehicle trajectory prediction. |
| format | Article |
| id | doaj-art-5a23f185c98940ed95a69dbafc4eced6 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-5a23f185c98940ed95a69dbafc4eced62025-08-20T02:50:36ZengMDPI AGSensors1424-82202024-11-012423753810.3390/s24237538Exploring rounD Dataset for Domain Generalization in Autonomous Vehicle Trajectory PredictionZikai Zhang0Department of Computer Science, Durham University, Stockton Rd, Durham DH1 3LE, UKThis paper analyzes the rounD dataset to advance motion forecasting algorithms for autonomous vehicles navigating complex roundabout environments. We develop a trajectory prediction framework inspired by Gated Recurrent Unit (GRU) networks and graph-based modules to effectively model vehicle interactions. Our primary objective is to evaluate the generalizability of the proposed model across diverse training and testing datasets. Through extensive experiments, we investigate how varying data distributions—such as different road configurations and recording times—impact the model’s prediction accuracy and robustness. This study provides key insights into the challenges of domain generalization in autonomous vehicle trajectory prediction.https://www.mdpi.com/1424-8220/24/23/7538machine learningdomain generalizationdriving behaviormotion forecastingtrajectory prediction |
| spellingShingle | Zikai Zhang Exploring rounD Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction Sensors machine learning domain generalization driving behavior motion forecasting trajectory prediction |
| title | Exploring rounD Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction |
| title_full | Exploring rounD Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction |
| title_fullStr | Exploring rounD Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction |
| title_full_unstemmed | Exploring rounD Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction |
| title_short | Exploring rounD Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction |
| title_sort | exploring round dataset for domain generalization in autonomous vehicle trajectory prediction |
| topic | machine learning domain generalization driving behavior motion forecasting trajectory prediction |
| url | https://www.mdpi.com/1424-8220/24/23/7538 |
| work_keys_str_mv | AT zikaizhang exploringrounddatasetfordomaingeneralizationinautonomousvehicletrajectoryprediction |