Cooperative controllers for highways based on human experience

The AUTOPIA program has been working on the development of intelligent autonomous vehicles for the last 10 years. Its latest advances have focused on the development of cooperative manœuvres based on communications involving several vehicles. However, so far, these manœuvres have been tested only on private tracks that emulate urban environments. The first experiments with autonomous vehicles on real highways, in the framework of the grand cooperative driving challenge (GCDC) where several vehicles had to cooperate in order to perform cooperative adaptive cruise control (CACC), are described. In this context, the main challenge was to translate, through fuzzy controllers, human driver experience to these scenarios. This communication describes the experiences deriving from this competition, specifically that concerning the controller and the system implemented in a Citröen C3

Energy dissipation based longitudinal and lateral coupling control for intelligent vehicles

This paper proposes a combined longitudinal and lateral control approach for an intelligent vehicle system based on energy dissipation. The vehicle system dynamics resembles a series of mass/spring/damper systems that are dissipative, i.e., the energy of the system decays to zero eventually. Thus, the nonlinear-optimal longitudinal and lateral coupling control problem of the intelligent vehicle system is transformed into a dissipative control design based on an energy storage function. To satisfy the γ-performance, with respect to the quadratic supply rate, the storage function is developed by using a back-stepping based Lyapunov method and a step-by-step improvement of performance bounds. A dissipative feedback control law is formulated by successive approximation based on the step-by-step reduction of the value of γ. The results of the adaptive vehicle control simulations and test-bed experiments are provided and verified by the respective comparison of energy consumption on different values of γ and speed adaption under different road geometries.In part by the National Key Research and Development Program (2016YFB0100903), the National Natural Science Foundation of China (61503284, 51505475 and 51408417) and Yingcai Project of CUMT (YC170001).https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5117645hj2018Electrical, Electronic and Computer Engineerin

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments

Design and validation of decision and control systems in automated driving

xxvi, 148 p.En la última década ha surgido una tendencia creciente hacia la automatización de los vehículos, generando un cambio significativo en la movilidad, que afectará profundamente el modo de vida de las personas, la logística de mercancías y otros sectores dependientes del transporte. En el desarrollo de la conducción automatizada en entornos estructurados, la seguridad y el confort, como parte de las nuevas funcionalidades de la conducción, aún no se describen de forma estandarizada. Dado que los métodos de prueba utilizan cada vez más las técnicas de simulación, los desarrollos existentes deben adaptarse a este proceso. Por ejemplo, dado que las tecnologías de seguimiento de trayectorias son habilitadores esenciales, se deben aplicar verificaciones exhaustivas en aplicaciones relacionadas como el control de movimiento del vehículo y la estimación de parámetros. Además, las tecnologías en el vehículo deben ser lo suficientemente robustas para cumplir con los requisitos de seguridad, mejorando la redundancia y respaldar una operación a prueba de fallos. Considerando las premisas mencionadas, esta Tesis Doctoral tiene como objetivo el diseño y la implementación de un marco para lograr Sistemas de Conducción Automatizados (ADS) considerando aspectos cruciales, como la ejecución en tiempo real, la robustez, el rango operativo y el ajuste sencillo de parámetros. Para desarrollar las aportaciones relacionadas con este trabajo, se lleva a cabo un estudio del estado del arte actual en tecnologías de alta automatización de conducción. Luego, se propone un método de dos pasos que aborda la validación de ambos modelos de vehículos de simulación y ADS. Se introducen nuevas formulaciones predictivas basadas en modelos para mejorar la seguridad y el confort en el proceso de seguimiento de trayectorias. Por último, se evalúan escenarios de mal funcionamiento para mejorar la seguridad en entornos urbanos, proponiendo una estrategia alternativa de estimación de posicionamiento para minimizar las condiciones de riesgo

Cooperative controllers for highways based on human experience

The AUTOPIA program has been working on the development of intelligent autonomous vehicles for the last 10 years. Its latest advances have focused on the development of cooperative manœuvres based on communications involving several vehicles. However, so far, these manœuvres have been tested only on private tracks that emulate urban environments. The first experiments with autonomous vehicles on real highways, in the framework of the grand cooperative driving challenge (GCDC) where several vehicles had to cooperate in order to perform cooperative adaptive cruise control (CACC), are described. In this context, the main challenge was to translate, through fuzzy controllers, human driver experience to these scenarios. This communication describes the experiences deriving from this competition, specifically that concerning the controller and the system implemented in a Citröen C3. © 2012 Elsevier Ltd. All rights reserved.CYCIT (Spain); Plan Nacional (Spain) from the GUIADE, P9/08 ; TRANSITO , TRA2008-06602-C03-01Peer Reviewe

Actas de las XXXIV Jornadas de Automática

Postprint (published version