Safety-related challenges and opportunities for GPUs in the automotive domain

GPUs have been shown to cover the computing performance needs of autonomous driving (AD) systems. However, since the GPUs used for AD build on designs for the mainstream market, they may lack fundamental properties for correct operation under automotive's safety regulations. In this paper, we analyze some of the main challenges in hardware and software design to embrace GPUs as the reference computing solution for AD, with the emphasis in ISO 26262 functional safety requirements.Authors would like to thank Guillem Bernat from Rapita Systems for his technical feedback on this work. The research leading to this work has received funding from the European Re-search Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773). This work has also been partially supported by the Spanish Ministry of Science and Innovation under grant TIN2015-65316-P and the HiPEAC Network of Excellence. Jaume Abella has been partially supported by the Ministry of Economy and Competitiveness under Ramon y Cajal postdoctoral fellowship number RYC-2013-14717. Carles Hernández is jointly funded by the Spanish Ministry of Economy and Competitiveness and FEDER funds through grant TIN2014-60404-JIN.Peer ReviewedPostprint (author's final draft

Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system

ENABLE-S3 – Advanced V&V technologies and methods combined with simulation and testing environments enable the safe and secure development of Autonomous Vehicles

Highly automated and autonomous transport is a technology field that enables safer and cleaner transport and unburdens the driver from boring and/or error prone driving task. The development of automated transport features and vehicles will or have already led to new business opportunities in many technology sectors, like sensor technologies, SW-development or mobility services to name just a few of them. The highly precise sensors and communication technologies as well as the necessary computing power and algorithms within the vehicle plus the digital infrastructure that are necessary to realize the autonomous transport are developing very fast. But this goes also along with new heavy-weight challenges in terms of safety and security aspects. Extensive verification and validation efforts are necessary to make automated systems at least as safe as human-operated systems are nowadays. The ENABLE-S3 project develops verification and validation technologies and methods that will help to tackle this challenge with reasonable efforts and high coverage of test-cases. 71 partners from different transport sectors (automotive, aerospace, rail, maritime, farming) and other industries are creating new knowledge in the areas of testing and simulation methods & technologies as well as the required testing platforms and environments. Research within ENABLE-S3 focuses on: - Test and simulation environments supporting open standards (e.g. Functional Mock-up Interface, OpenSimulationInterface) wherever possible in order to run tests for automated transport seamlessly in different virtual and semi-virtual environments. - Open standards for the definition, management and execution of test cases/testing scenarios like OpenScenario or OpenDrive and their relationship to other existing standards like ASAM-XiL. - Investigation of testing methodologies which are necessary to reduce the number of test cases tremendously, among them are DoE (design of experiments), combinatorial testing, FMEA analysis etc. - Development of sensor models as well as sensor stimuli (physical sensor signal generators). - Generation of test cases out of existing recorded real-world data. The developed methods are applied in different industrial use-cases. This paper will give an overview over the needed building blocks for testing AD functions, including scenario generation, test planning, and test execution and simulation that were already developed within the ENABLE-S3 project and will finally present a practical use case and the application of aforementioned methods to an ACC function of a vehicle. The results gained so far in the project will show that the verification and validation methods combined with simulation and testing technologies for automated vehicles in transport play a major role in reaching the high safety and security levels that end customers and legal authorities will demand for this important technology in order to get acceptance and in order to provide a great step forward in reducing road fatalities and at the same time also CO2 emissions

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

An Empirical Evaluation of Deep Learning on Highway Driving

Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical evaluations of recent deep learning advances. Computer vision, combined with deep learning, has the potential to bring about a relatively inexpensive, robust solution to autonomous driving. To prepare deep learning for industry uptake and practical applications, neural networks will require large data sets that represent all possible driving environments and scenarios. We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection. We show how existing convolutional neural networks (CNNs) can be used to perform lane and vehicle detection while running at frame rates required for a real-time system. Our results lend credence to the hypothesis that deep learning holds promise for autonomous driving.Comment: Added a video for lane detectio