Guaranteeing the safety of an autonomous vehicle (AV) is a challenging task, especially if the perceived environment is highly uncertain and other road users deviate from their expected trajectories. In this paper, we propose a probabilistic overall strategy for risk assessment and management of AV in highway through a Sequential Level Bayesian Decision Network (SLBDN) and an appropriate analytical formalization of criteria for anomaly detection based on a Dynamic Predicted Inter-Distance Profile (DPIDP) between vehicles. Accordingly, the proposed system is designed to take the suitable maneuver decision, have a safety retrospection and verification over the current maneuver risk and take appropriate evasive action autonomously from moving obstacles. Moreover, this probabilistic framework accounts for measurements uncertainty through an Extended Kalman Filter (EKF) and for vehicles' maximum capacities. Since the proposed strategy has a short response time, integrating safety verification in the decision-making process makes real time evasive decisions possible. Several simulation results show the good performance of the overall proposed control architecture, mainly in terms of efficiency to handle probabilistic decision-making even for risky scenarios.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Reliable Risk Management for Autonomous Vehicles based on Sequential Bayesian Decision Networks and Dynamic Inter-Vehicular Assessment


    Contributors:


    Publication date :

    2019-06-01


    Size :

    1167790 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    RELIABLE RISK MANAGEMENT FOR AUTONOMOUS VEHICLES BASED ON SEQUENTIAL BAYESIAN DECISION NETWORKS AND DYNAMIC INTER-VEHICULAR ASSESSMENT

    Iberraken, Dimia / Adouane, Lounis / Denis, Dieumet | British Library Conference Proceedings | 2019


    Reliable Decision-Making in Autonomous Vehicles

    Alves, Gleifer Vaz / Dennis, Louise / Fernandes, Lucas et al. | Springer Verlag | 2019