Browsing by Keyword "Vehicle dynamics"
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Item Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles(2019-02) Dendaluce Jahnke, Martin; Cosco, Francesco; Novickis, Rihards; Pérez Rastelli, Joshué; Gomez-Garay, Vicente; Tecnalia Research & Innovation; CCAMThe combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Item Real-Time Trajectory Planning Method Based On N-Order Curve Optimization(Institute of Electrical and Electronics Engineers Inc., 2020-10-08) Lattarulo, Ray; Gonzalez, Leonardo; Perez, Joshue; Barbulescu, Lucian-Florentin; CCAMIn recent years, many functionalities were developed for Automated Vehicles (AVs) and some of them with close-to-market prototypes. A required topic is the generation of continuous trajectories that reduces the amount of discrete and pre-coded instructions while leading the vehicle safely. Consequently, this work presents a novel real-time trajectory planning approach based on numerical optimization of n-order Bézier curves and lane-based information. The generation of a feasible trajectory considers the vehicle dimension while driving into a lane-corridor. The nonlinear optimization problem was solved with the Bound Optimization BY Quadratic Approximation method (BOBYQA), and it uses the passengers' comfort, safety, and vehicle dynamics as constraints of the problem. The solution is validated in a simulation environment using a bus with a length of 12 meters. Moreover, the validation considered the roundabouts due to its complexity, nevertheless, the solution is scalable to other scenarios.Item Validation of a Real-Time Capable Multibody Vehicle Dynamics Formulation for Automotive Testing Frameworks Based on Simulation(2020) Parra, Alberto; Rodriguez, Antonio J.; Zubizarreta, Asier; Perez, Joshue; Tecnalia Research & Innovation; CCAMThe growing functionalities implemented on vehicles have increased the importance of simulation in the design process. This complexity is mainly driven by the introduction of electrified powertrains, Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS). Additionally, the automotive industry must reduce development times and cost, while keeping flexible development capabilities and fulfilling demanding regulation standards for safety-critical systems. Existing testing frameworks based on simulation implement typically analytical models to ensure real-time performance, and provide limited flexibility to perform Hardware in the Loop (HiL) setup based tests. In this work a vehicle modelling approach which guarantees high accuracy and real-time capabilities is proposed. Moreover, the proposed approach is validated firstly with real vehicle data, demonstrating that it can fairly reproduce the behaviour of the vehicle tested; and secondly, in a HiL setup to demonstrate the real-time execution capabilities of the approach.