Context‐dependent reconfiguration of autonomous vehicles in mixed traffic. Issue 4 (4th December 2017) - Horcas, José Miguel; Monteil, Julien; Bouroche, Mélanie; Pinto, Mónica; Fuentes, Lidia; Clarke, Siobhán
Abstract: Human drivers naturally adapt their behaviour depending on the traffic conditions, such as the current weather and road type. Autonomous vehicles need to do the same, in a way that is both safe and efficient in traffic composed of both conventional and autonomous vehicles. In this paper, we demonstrate the applicability of a reconfigurable vehicle controller agent for autonomous vehicles that adapts the parameters of a used car‐following model at runtime, so as to maintain a high degree of traffic quality (efficiency and safety) under different weather conditions. We follow a dynamic software product line approach to model the variability of the car‐following model parameters, context changes and traffic quality, and generate specific configurations for each particular context. Under realistic conditions, autonomous vehicles have only a very local knowledge of other vehicles' variables. We investigate a distributed model predictive controller agent for autonomous vehicles to estimate their behavioural parameters at runtime, based on their available knowledge of the system. We show that autonomous vehicles with the proposed reconfigurable controller agent lead to behaviour similar to that achieved by human drivers, depending on the context. Abstract : – The variability of the autonomous vehicles' behaviour, traffic context and quality is modelled in a Dynamic Software Product Line. – The behaviour of the autonomous vehicles is adapted at runtime based on the current traffic context. – A model predictive controller agent for autonomous vehicles optimises their behavioural parameters based on available knowledge in the traffic network.