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 (DSPL) 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
