Back to Search
Start Over
Learning-based modeling of human-autonomous vehicle interaction for improved safety in mixed-vehicle platooning control.
- Source :
-
Transportation Research Part C: Emerging Technologies . May2024, Vol. 162, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- 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. • A Gaussian process (GP) based model corrects predictions and estimates uncertainty. • An uncertainty-aware controller uses GP models to enhance vehicle interaction safety. • The controller is 100× faster than previous methods, ensuring real-time control. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0968090X
- Volume :
- 162
- Database :
- Academic Search Index
- Journal :
- Transportation Research Part C: Emerging Technologies
- Publication Type :
- Academic Journal
- Accession number :
- 176867264
- Full Text :
- https://doi.org/10.1016/j.trc.2024.104600