Browsing by Keyword "Automated driving"
Now showing 1 - 8 of 8
Results Per Page
Sort Options
Item Adaptable Emergency Braking Based on a Fuzzy Controller and a Predictive Model(IEEE, 2018-11) Alarcon, Leonardo Gonzalez; Vaca Recalde, Myriam Elizabeth; Marcano, Mauricio; Marti, Enrique; Tecnalia Research & Innovation; CCAMThis work presents the implementation of an adaptable emergency braking system for low speed collision avoidance, based on a frontal laser scanner for static obstacle detection, using a D-GPS system for positioning. A fuzzy logic decision process performs a criticality assessment that triggers the emergency braking system and modulates its behavior. This criticality is evaluated through the use of a predictive model based on a kinematic estimation, which modulates the decision to brake. Additionally a critical study is conducted in order to provide a benchmark for comparison, and evaluate the limits of the predictive model. The braking decision is based on a parameterizable braking model tuned for the target vehicle, that takes into account factors such as reaction time, distance to obstacles, vehicle velocity and maximum deceleration. Once activated, braking force is adapted to reduce vehicle occupants discomfort while ensuring safety throughout the process. The system was implemented on a real vehicle and proper operation is validated through extensive testing carried out at Tecnalia facilities.Item Caracterización de los sistemas de actuación para vehículos altamente automatizados(Universidade da Coruña, Servizo de Publicacións, 2019) Sarabia, Joseba; Matute-Peaspan, José A.; Zubizarreta, AsierLos modelos de simulación son importantes para la investigación y desarrollo de estrategias de decisión y control en vehículos automatizados. Las plataformas de pruebas reales se obtienen generalmente mediante la modificación de vehículos comerciales, donde robots o actuadores electromecánicos son fijados a los elementos de conducción del vehículo como el volante, pedales de freno y aceleración. Diferencias significativas durante el ajuste de las ganancias en el control de alto nivel entre simulaciones y dichas plataformas reales pueden deberse principalmente a un modelo impreciso de los actuadores. En este trabajo, se presenta una revisión del estado del arte sobre técnicas para el control de bajo nivel en diferentes plataformas utilizadas tanto en lo académico como en la industria. Adicionalmente, se muestra la metodología utilizada para el modelado de actuadores realizando pruebas a lazo abierto sobre una plataforma real para pruebas de conducción automatizada. Finalmente, se presenta una comparativa entre el comportamiento de los actuadores modelados y los reales durante un recorrido del vehículo en modo automático ejecutando una ruta predefinida, evidenciando la fiabilidad de los modelos propuestos y la metodología utilizada para su obtención.Item A Fail-Operational Control Architecture Approach and Dead-Reckoning Strategy in Case of Positioning Failures(2020-01-02) Matute-Peaspan, Jose Angel; Perez, Joshue; Zubizarreta, Asier; Tecnalia Research & Innovation; CCAMPresently, in the event of a failure in Automated Driving Systems, control architectures rely on hardware redundancies over software solutions to assure reliability or wait for human interaction in takeover requests to achieve a minimal risk condition. As user confidence and final acceptance of this novel technology are strongly related to enabling safe states, automated fall-back strategies must be assured as a response to failures while the system is performing a dynamic driving task. In this work, a fail-operational control architecture approach and dead-reckoning strategy in case of positioning failures are developed and presented. A fail-operational system is capable of detecting failures in the last available positioning source, warning the decision stage to set up a fall-back strategy and planning a new trajectory in real time. The surrounding objects and road borders are considered during the vehicle motion control after failure, to avoid collisions and lane-keeping purposes. A case study based on a realistic urban scenario is simulated for testing and system verification. It shows that the proposed approach always bears in mind both the passenger’s safety and comfort during the fall-back maneuvering execution.Item Fault injection method for safety and controllability evaluation of automated driving(IEEE, 2017-07-31) Uriagereka, Garazi Juez; Lattarulo, Ray; Rastelli, Joshue Perez; Calonge, Estibaliz Amparan; Ruiz Lopez, Alejandra; Espinoza Ortiz, Huascar; Tecnalia Research & Innovation; CCAM; CIBERSEC&DLT; QuantumAdvanced Driver Assistance Systems (ADAS) and automated vehicle applications based on embedded sensors have become a reality today. As road vehicles increase its autonomy and the driver shares his role in the control loop, novel challenges on their dependability assessment arise. One key issue is that the notion of controllability becomes more complex when validating the robustness of the automated vehicle in the presence of faults. This paper presents a simulation-based fault injection approach aimed at finding acceptable controllability properties for the model-based design of control systems. We focus on determining the best fault models inserting exceptional conditions to accelerate the identification of specific areas for testing. In our work we performed fault injection method to find the most appropriate safety concepts, controllability properties and fault handling strategies at early design phases of lateral control functions based on the error in the Differential GPS signal.Item From the Concept of Being “the Boss” to the Idea of Being “a Team”: The Adaptive Co-Pilot as the Enabler for a New Cooperative Framework: The adaptive co-pilot as the enabler for a new cooperative framework(2021-07-28) Marcano, Mauricio; Tango, Fabio; Sarabia, Joseba; Castellano, Andrea; Pérez, Joshué; Irigoyen, Eloy; Díaz, Sergio; CCAMThe “classical” SAE LoA for automated driving can present several drawbacks, and the SAE-L2 and SAE-L3, in particular, can lead to the so-called “irony of automation”, where the driver is substituted by the artificial system, but is still regarded as a “supervisor” or as a “fallback mechanism”. To overcome this problem, while taking advantage of the latest technology, we regard both human and machine as members of a unique team that share the driving task. Depending on the available resources (in terms of driver’s status, system state, and environment conditions) and considering that they are very dynamic, an adaptive assignment of authority for each member of the team is needed. This is achieved by designing a technology enabler, constituted by the intelligent and adaptive co-pilot. It comprises (1) a lateral shared controller based on NMPC, which applies the authority, (2) an arbitration module based on FIS, which calculates the authority, and (3) a visual HMI, as an enabler of trust in automation decisions and actions. The benefits of such a system are shown in this paper through a comparison of the shared control driving mode, with manual driving (as a baseline) and lane-keeping and lane-centering (as two commercial ADAS). Tests are performed in a use case where support for a distracted driver is given. Quantitative and qualitative results confirm the hypothesis that shared control offers the best balance between performance, safety, and comfort during the driving task.Item Lateral-acceleration-based vehicle-models-blending for automated driving controllers(2020-10-13) Matute-Peaspan, Jose A.; Marcano, Mauricio; Diaz, Sergio; Zubizarreta, Asier; Perez, Joshue; CCAMModel-based trajectory tracking has become a widely used technique for automated driving system applications. A critical design decision is the proper selection of a vehicle model that achieves the best trade-off between real-time capability and robustness. Blending different types of vehicle models is a recent practice to increase the operating range of model-based trajectory tracking control applications. However, current approaches focus on the use of longitudinal speed as the blending parameter, with a formal procedure to tune and select its parameters still lacking. This work presents a novel approach based on lateral accelerations, along with a formal procedure and criteria to tune and select blending parameters, for its use on model-based predictive controllers for autonomous driving. An electric passenger bus traveling at different speeds over urban routes is proposed as a case study. Results demonstrate that the lateral acceleration, which is proportional to the lateral forces that differentiate kinematic and dynamic models, is a more appropriate model-switching enabler than the currently used longitudinal velocity. Moreover, the advanced procedure to define blending parameters is shown to be effective. Finally, a smooth blending method offers better tracking results versus sudden model switching ones and non-blending techniques.Item Study of the lane change maneuver: automated driving use case(2017) Lattarulo, Ray; Perez, JoshueNowadays, the idea of a completely interconnected city with infrastructure, vehicles and even the humans on a connectivity loop is a reality.Item A Two-Stage Real-Time Path Planning: Application to the Overtaking Manuever: Application to the Overtaking Manuever(2020) Garrido, Fernando; Gonzalez, Leonardo; Milanes, Vicente; Perez, Joshue; Nashashibi, Fawzi; Rastelli, Joshue Perez; CCAMThis paper proposes a two-stage local path planning approach to deal with all kinds of scenarios (i.e. intersections, turns, roundabouts). The first stage carries out an off-line optimization, considering vehicle kinematics and road constraints. The second stage includes all dynamic obstacles in the scene, generating a continuous path in real-time. Human-like driving style is provided by evaluating the sharpness of the road bends and the available space among them, optimizing the drivable area. The proposed approach is validated on overtaking scenarios where real-time path planning generation plays a key role. Simulation and real results on an experimental automated platform provide encouraging results, generating real-time collision-free paths while maintaining the defined smoothness criteria.