Context-dependent reconfiguration of autonomous vehicles in mixed traffic
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 demonstr...
| Autores: | , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión enviada para evaluación y publicación |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/111454 |
| Acceso en línea: | https://hdl.handle.net/11441/111454 https://doi.org/10.1002/smr.1926 |
| Access Level: | acceso abierto |
| Palabra clave: | Autonomous vehicles Car-following model Dynamic Software Product Lines Reconfiguration Traffic quality |
| Sumario: | 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. |
|---|