Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles

The 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.Some of the results presented in this work are related to activities within the 3Ccar project, which has received funding from ECSEL Joint Undertaking under grant agreement No. 662192. This Joint Undertaking received support from the European Union’s Horizon 2020 research and innovation programme and Germany, Austria, Czech Republic, Romania, Belgium, United Kingdom, France, Netherlands, Latvia, Finland, Spain, Italy, Lithuania. This work was also partly supported by the project ENABLES3, which received funding from ECSEL Joint Undertaking under grant agreement No. 692455-2

Development and experimental validation of high performance embedded intelligence and fail-operational urban surround perception solutions of the PRYSTINE project

Automated Driving Systems (ADSs) commend a substantial reduction of human-caused road accidents while simultaneously lowering emissions, mitigating congestion, decreasing energy consumption and increasing overall productivity. However, achieving higher SAE levels of driving automation and complying with ISO26262 C and D Automotive Safety Integrity Levels (ASILs) is a multi-disciplinary challenge that requires insights into safety-critical architectures, multi-modal perception and real-time control. This paper presents an assorted effort carried out in the European H2020 ECSEL project—PRYSTINE. In this paper, we (1) investigate Simplex, 1oo2d and hybrid fail-operational computing architectures, (2) devise a multi-modal perception system with fail-safety mechanisms, (3) present a passenger vehicle-based demonstrator for low-speed autonomy and (4) suggest a trust-based fusion approach validated on a heavy-duty truck.</p

Modulární jednotka pro zpracování signálů v aplikacích řízení pohybu založená na System-on-Chip a FPGA

Článek se zabývá průmyslovými systémy řízení pohybu (Motion Control), které jsou implementovány implementace distribuovaným způsobem, tj. kde je více vstupních/výstupních zařízení propojeno s nadřazeným řídicím systémem pomocí rychlé digitální komunikace. Tento článek představuje návrh řešení modulární jednotky pro připojení více vstupních a výstupních signálů z více snímačů a aktuátorů do průmyslové komunikační sítě s protokolem EtherCAT. Do této řídicí jednotky je možné vložit uživatelem definované algoritmy pro předzpracování signálů, fúzi více vstupních signálů nebo uzavření velmi rychlé lokální regulační smyčky. Tato skutečnost je hlavním přínosem ve srovnání s obdobnými běžně dostupnými řešeními. Efektivní návrh hardwarového řešení je založen na jediném hlavním čipu (SoC), který integruje část programovatelného hradlového pole (FPGA) a několik standardních výpočetních CPU jader s ARM architekturou. Zpracování dat může být realizováno kompletně v FPGA, čímž lze dosáhnout velmi nízkého zpoždění signálů. Zároveň může být využito výkonných CPU jader pro složitější algoritmy, které na FPGA nelze realizovat, a využít přitom těsné vazby FPGA a CPU části.Motion control systems with distributed architecture where multiple input/output devices are connected to the upper layer controller by fast digital communication (fieldbus) became an industrial standard. This paper presents design of a modular input/output device which can process signals from multiple sensors, drive multiple actuators and act as a Slave or Master node in EtherCAT fieldbus network. User-defined algorithms can be easily implemented to preprocess input signals, combine multiple signals or close local control loops with extremely high sampling rates which makes the difference to standard off-the-shelf solutions. To meet these requirements and simplify hardware design, our device is based on System-on-Chip with both programmable logic (FPGA) and classic processor (CPU) ARM cores. Data processing including user algorithms can be done entirely in FPGA which provides very low latency and no jitter, and also on CPU for more complex computations with advantage of tight integration between FPGA and CPU. In this paper we provide description of hardware design, system architecture and typical applications

An Approach of Feed-Forward Neural Network Throughput-Optimized Implementation in FPGA

Artificial Neural Networks (ANNs) have become an accepted approach for a wide range of challenges. Meanwhile, the advancement of chip manufacturing processes is approaching saturation which calls for new computing solutions. This work presents a novel approach of an FPGA-based accelerator development for fully connected feed-forward neural networks (FFNNs). A specialized tool was developed to facilitate different implementations, which splits FFNN into elementary layers, allocates computational resources and generates high-level C++ description for high-level synthesis (HLS) tools. Various topologies are implemented and benchmarked, and a comparison with related work is provided. The proposed methodology is applied for the implementation of high-throughput virtual sensor

Mathematical Model and Synthetic Data Generation for Infra-Red Sensors

A key challenge in further improving infrared (IR) sensor capabilities is the development of efficient data pre-processing algorithms. This paper addresses this challenge by providing a mathematical model and synthetic data generation framework for an uncooled IR sensor. The developed model is capable of generating synthetic data for the design of data pre-processing algorithms of uncooled IR sensors. The mathematical model accounts for the physical characteristics of the focal plane array, bolometer readout, optics and the environment. The framework permits the sensor simulation with a range of sensor configurations, pixel defectiveness, non-uniformity and noise parameters

Free–Form 3–D Current Loop Magnetic Field Calculations

This paper is aimed to describe well known mathematical approach implementation for numerical calculations of magnetic field induced by free–form current loop

Development and Experimental Validation of High Performance Embedded Intelligence and Fail-Operational Urban Surround Perception Solutions of the PRYSTINE Project

Automated Driving Systems (ADSs) commend a substantial reduction of human-caused road accidents while simultaneously lowering emissions, mitigating congestion, decreasing energy consumption and increasing overall productivity. However, achieving higher SAE levels of driving automation and complying with ISO26262 C and D Automotive Safety Integrity Levels (ASILs) is a multi-disciplinary challenge that requires insights into safety-critical architectures, multi-modal perception and real-time control. This paper presents an assorted effort carried out in the European H2020 ECSEL project&mdash;PRYSTINE. In this paper, we (1) investigate Simplex, 1oo2d and hybrid fail-operational computing architectures, (2) devise a multi-modal perception system with fail-safety mechanisms, (3) present a passenger vehicle-based demonstrator for low-speed autonomy and (4) suggest a trust-based fusion approach validated on a heavy-duty truck

Programmable Systems for Intelligence in Automobiles (PRYSTINE): Technical Progress after Year 2

As originally submitted and published there was an error in this document. The authors subsequently provided the following text: "The article is a co-development of many authors from many organizations. Only the first author affiliation was provided on the article PDF. The following additional author affiliations are noted: Kaspars Ozols (Institute of Electronics and Computer Science, Latvia); Rihards Novickis (Institute of Electronics and Computer Science, Latvia); Aleksandrs Levinskis (Institute of Electronics and Computer Science, Latvia)." The original article PDF remains unchanged.Autonomous driving has the potential to disruptively change the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations by its own, which currently is not reached with state-of-the-art approaches.The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI controlled vehicle demonstrators) achieved until year 2.Peer reviewe

Programmable Systems for Intelligence in Automobiles (PRYSTINE): Final results after Year 3

Autonomous driving is disrupting the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations on its own, which currently is not reached with state-of-the-art approaches.The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key exploitable results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI-controlled vehicle demonstrators) achieved until its final year 3