Uncertainty in the behaviours of vehicles surrounding a self- driving car introduces substantial computational complexity in autonomous driving. In this study 1 , a data-driven approach was used to extract probabilistic models of the behaviours of other cars and exploit them to support a driving system based on Monte Carlo Tree Search (MCTS). The model selection component of the architecture infers which models better explain the current behaviours of the other vehicles using maximum likelihood estimation for Bayesian model comparison. The inferred behaviours are then used for MCTS- based control to prevent rollouts on models that are not relevant in the current context. While the use of multiple models allows improved efficiency and higher flexibility, it also introduces identification-related issues, which were solved here using Bayesian machine learning. The results obtained from the performed simulations are presented comparing the proposed MCTS architecture when employing multiple models with a naive model of the other vehicles.
Catenacci Volpi, N., Wu, Y., & Ognibene, D. (2018). Towards Event-Based MCTS for Autonomous Cars. In Proceeding of the 9th Asia-Pacific Signal and Information Processing Association Annual Conference (APSIPA ASC 2017) (pp.420-427). IEEE.
|Citazione:||Catenacci Volpi, N., Wu, Y., & Ognibene, D. (2018). Towards Event-Based MCTS for Autonomous Cars. In Proceeding of the 9th Asia-Pacific Signal and Information Processing Association Annual Conference (APSIPA ASC 2017) (pp.420-427). IEEE.|
|Carattere della pubblicazione:||Scientifica|
|Presenza di un coautore afferente ad Istituzioni straniere:||Si|
|Titolo:||Towards Event-Based MCTS for Autonomous Cars|
|Autori:||Catenacci Volpi, N; Wu, Y; Ognibene, D|
OGNIBENE, DIMITRI (Ultimo)
|Data di pubblicazione:||2018|
|Nome del convegno:||9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 12-15 Dec|
|Appare nelle tipologie:||02 - Intervento a convegno|