Browsing by Keyword "Advanced Driver Assistance Systems"
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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 Low Speed Longitudinal Control Algorithms for Automated Vehicles in Simulation and Real Platforms(2018) Marcano, Mauricio; Matute, José A.; Lattarulo, Ray; Martí, Enrique; Pérez, Joshué; CCAM; Tecnalia Research & InnovationAdvanced Driver Assistance Systems (ADAS) acting over throttle and brake are already available in level 2 automated vehicles. In order to increase the level of automation new systems need to be tested in an extensive set of complex scenarios, ensuring safety under all circumstances. Validation of these systems using real vehicles presents important drawbacks: the time needed to drive millions of kilometers, the risk associated with some situations, and the high cost involved. Simulation platforms emerge as a feasible solution.Therefore, robust and reliable virtual environments to test automated driving maneuvers and control techniques are needed. In that sense, this paper presents a use case where three longitudinal low speed control techniques are designed, tuned, and validated using an in-house simulation framework and later applied in a real vehicle. Control algorithms include a classical PID, an adaptive network fuzzy inference system (ANFIS), and a Model Predictive Control (MPC). The simulated dynamics are calculated using a multibody vehicle model. In addition, longitudinal actuators of a Renault Twizy are characterized through empirical tests. A comparative analysis of results between simulated and real platform shows the effectiveness of the proposed framework for designing and validating longitudinal controllers for real automated vehicles.