Learning-Based Modeling of Human-Autonomous Vehicle Interaction for Improved Safety in Mixed-Vehicle Platooning Control
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866913303133945856 |
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| author | Wang, Jie Pant, Yash Vardhan Jiang, Zhihao |
| author_facet | Wang, Jie Pant, Yash Vardhan Jiang, Zhihao |
| contents | The rising presence of autonomous vehicles (AVs) on public roads necessitates the development of advanced control strategies that account for the unpredictable nature of human-driven vehicles (HVs). This study introduces a learning-based method for modeling HV behavior, combining a traditional first-principles approach with a Gaussian process (GP) learning component. This hybrid model enhances the accuracy of velocity predictions and provides measurable uncertainty estimates. We leverage this model to develop a GP-based model predictive control (GP-MPC) strategy to improve safety in mixed vehicle platoons by integrating uncertainty assessments into distance constraints. Comparative simulations between our GP-MPC approach and a conventional model predictive control (MPC) strategy reveal that the GP-MPC ensures safer distancing and more efficient travel within the mixed platoon. By incorporating sparse GP modeling for HVs and a dynamic GP prediction in MPC, we significantly reduce the computation time of GP-MPC, making it only marginally longer than standard MPC and approximately 100 times faster than previous models not employing these techniques. Our findings underscore the effectiveness of learning-based HV modeling in enhancing safety and efficiency in mixed-traffic environments involving AV and HV interactions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_09452 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning-Based Modeling of Human-Autonomous Vehicle Interaction for Improved Safety in Mixed-Vehicle Platooning Control Wang, Jie Pant, Yash Vardhan Jiang, Zhihao Robotics The rising presence of autonomous vehicles (AVs) on public roads necessitates the development of advanced control strategies that account for the unpredictable nature of human-driven vehicles (HVs). This study introduces a learning-based method for modeling HV behavior, combining a traditional first-principles approach with a Gaussian process (GP) learning component. This hybrid model enhances the accuracy of velocity predictions and provides measurable uncertainty estimates. We leverage this model to develop a GP-based model predictive control (GP-MPC) strategy to improve safety in mixed vehicle platoons by integrating uncertainty assessments into distance constraints. Comparative simulations between our GP-MPC approach and a conventional model predictive control (MPC) strategy reveal that the GP-MPC ensures safer distancing and more efficient travel within the mixed platoon. By incorporating sparse GP modeling for HVs and a dynamic GP prediction in MPC, we significantly reduce the computation time of GP-MPC, making it only marginally longer than standard MPC and approximately 100 times faster than previous models not employing these techniques. Our findings underscore the effectiveness of learning-based HV modeling in enhancing safety and efficiency in mixed-traffic environments involving AV and HV interactions. |
| title | Learning-Based Modeling of Human-Autonomous Vehicle Interaction for Improved Safety in Mixed-Vehicle Platooning Control |
| topic | Robotics |
| url | https://arxiv.org/abs/2303.09452 |