A Complete Framework for a Behavioral Planner with Automated Vehicles: A Car-Sharing Fleet Relocation Approach

Currently, research on automated vehicles is strongly related to technological advances to achieve a safe, more comfortable driving process in different circumstances. The main achievements are focused mainly on highway and interurban scenarios. The urban environment remains a complex scenario due to the number of decisions to be made in a restrictive context. In this context, one of the main challenges is the automation of the relocation process of car-sharing in urban areas, where the management of the platooning and automatic parking and de-parking maneuvers needs a solution from the decision point of view. In this work, a novel behavioral planner framework based on a Finite State Machine (FSM) is proposed for car-sharing applications in urban environments. The approach considers four basic maneuvers: platoon following, parking, de-parking, and platoon joining. In addition, a basic V2V communication protocol is proposed to manage the platoon. Maneuver execution is achieved by implementing both classical (i.e., PID) and Model-based Predictive Control (i.e., MPC) for the longitudinal and lateral control problems. The proposed behavioral planner was implemented in an urban scenario with several vehicles using the Carla Simulator, demonstrating that the proposed planner can be helpful to solve the car-sharing fleet relocation problem in cities.This research was funded by the Goberment of the Basque Country (funding no. KK-2021/00123 and IT1726-22) and the European SHOW Project from the Horizon 2020 (funding no. 875530)

Entorno de simulación para vehículos automatizados con CARLA

[Resumen] El interés alrededor de los vehículos automatizados (VA) ha crecido considerablemente en los últimos años debido a la necesidad de conseguir un método de transporte más eficiente y seguro. Sin embargo, el desarrollo de esta tecnología es una tarea muy compleja, ya que es necesario validar e integrar una gran variedad de funcionalidades. Así mismo, el número de escenarios particulares que se requieren estudiar para asegurar una exitosa automatización hace que el testeo en carreteras reales no sea viable. Debido a esto, ha aumentado el interés por invertir en el desarrollo de entornos de validación virtuales, pudiendo encontrar tanto soluciones comerciales como de código abierto. En este trabajo se propone un entorno de simulación para aplicaciones de vehíıculos automatizados basado en CARLA, en el que se integran, por un lado, un mapa de una manzana de Bilbao y, por otro, el modelo de un Renault Twizy. De esta forma, se introducen las bases para validar futuros desarrollos en esta misma ubicación real.[Abstract] In recent years the interest in automated vehicles (AV) have increased due to the need of a safer and more efficient way of travelling. However, the validation of this technology is rather a complex task. Additionally, the amount of particular scenarios for which the technologies have to be tested makes the practical validation not a viable option. That is why recently the virtual testing environments are gaining a lot of popularity. In this work a testing environment is proposed using the open source simulator CARLA, in which a map of a part of Bilbao and a model of a Renault Twizy are integrated. Thanks to this work, future AV related work will be validated with real world data.Gobierno Vasco; ELKARTEK KK-2021/0012

A Complete Framework for a Behavioral Planner with Automated Vehicles: A Car-Sharing Fleet Relocation Approach

Currently, research on automated vehicles is strongly related to technological advances to achieve a safe, more comfortable driving process in different circumstances. The main achievements are focused mainly on highway and interurban scenarios. The urban environment remains a complex scenario due to the number of decisions to be made in a restrictive context. In this context, one of the main challenges is the automation of the relocation process of car-sharing in urban areas, where the management of the platooning and automatic parking and de-parking maneuvers needs a solution from the decision point of view. In this work, a novel behavioral planner framework based on a Finite State Machine (FSM) is proposed for car-sharing applications in urban environments. The approach considers four basic maneuvers: platoon following, parking, de-parking, and platoon joining. In addition, a basic V2V communication protocol is proposed to manage the platoon. Maneuver execution is achieved by implementing both classical (i.e., PID) and Model-based Predictive Control (i.e., MPC) for the longitudinal and lateral control problems. The proposed behavioral planner was implemented in an urban scenario with several vehicles using the Carla Simulator, demonstrating that the proposed planner can be helpful to solve the car-sharing fleet relocation problem in cities