340 research outputs found
Analysis and Mitigation of Shared Resource Contention on Heterogeneous Multicore: An Industrial Case Study
In this paper, we address the industrial challenge put forth by ARM in ECRTS
2022. We systematically analyze the effect of shared resource contention to an
augmented reality head-up display (AR-HUD) case-study application of the
industrial challenge on a heterogeneous multicore platform, NVIDIA Jetson Nano.
We configure the AR-HUD application such that it can process incoming image
frames in real-time at 20Hz on the platform. We use micro-architectural
denial-of-service (DoS) attacks as aggressor tasks of the challenge and show
that they can dramatically impact the latency and accuracy of the AR-HUD
application, which results in significant deviations of the estimated
trajectories from the ground truth, despite our best effort to mitigate their
influence by using cache partitioning and real-time scheduling of the AR-HUD
application. We show that dynamic LLC (or DRAM depending on the aggressor)
bandwidth throttling of the aggressor tasks is an effective mean to ensure
real-time performance of the AR-HUD application without resorting to
over-provisioning the system
A systematic review of perception system and simulators for autonomous vehicles research
This paper presents a systematic review of the perception systems and simulators for autonomous vehicles (AV). This work has been divided into three parts. In the first part, perception systems are categorized as environment perception systems and positioning estimation systems. The paper presents the physical fundamentals, principle functioning, and electromagnetic spectrum used to operate the most common sensors used in perception systems (ultrasonic, RADAR, LiDAR, cameras, IMU, GNSS, RTK, etc.). Furthermore, their strengths and weaknesses are shown, and the quantification of their features using spider charts will allow proper selection of different sensors depending on 11 features. In the second part, the main elements to be taken into account in the simulation of a perception system of an AV are presented. For this purpose, the paper describes
simulators for model-based development, the main game engines that can be used for simulation, simulators from the robotics field, and lastly simulators used specifically for AV. Finally, the current state of regulations that are being applied in different countries around the world on issues concerning the implementation of autonomous vehicles is presented.This work was partially supported by DGT (ref. SPIP2017-02286) and GenoVision (ref. BFU2017-88300-C2-2-R) Spanish Government projects, and the “Research Programme for Groups of Scientific Excellence in the Region of Murcia" of the Seneca Foundation (Agency for Science and Technology in the Region of Murcia – 19895/GERM/15)
On driver behavior recognition for increased safety:A roadmap
Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced
CiThruS2 : Open-source Photorealistic 3D Framework for Driving and Traffic Simulation in Real Time
The automotive and transport sector is undergoing a paradigm shift from manual to highly automated driving. This transition is driven by a proliferation of advanced driver assistance systems (ADAS) that seek to provide vehicle occupants with a safe, efficient, and comfortable driving experience. However, increasing the level of automation makes exhaustive physical testing of ADAS technologies impractical. Therefore, the automotive industry is increasingly turning to virtual simulation platforms to speed up time-to-market. This paper introduces the second version of our open-source See-Through Sight (CiThruS) simulation framework that provides a novel photorealistic virtual environment for vision-based ADAS development. Our 3D urban scene supports realistic traffic infrastructure and driving conditions with a plurality of time-of-day, weather, and lighting effects. Different traffic scenarios can be generated with practically any number of autonomous vehicles and pedestrians that can be made to comply with dedicated traffic regulations. All implemented features have been carefully optimized and the performance of our lightweight simulator exceeds 4K (3840 × 2160) rendering speed of 60 frames per second when run on NVIDIA GTX 1060 graphics card or equivalent consumer-grade hardware. Photorealistic graphics rendering and real-time simulation speed make our proposal suitable for a broad range of applications, including interactive driving simulators, visual traffic data collection, virtual prototyping, and traffic flow management.acceptedVersionPeer reviewe
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
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
Advances in Intelligent Vehicle Control
This book is a printed edition of the Special Issue Advances in Intelligent Vehicle Control that was published in the journal Sensors. It presents a collection of eleven papers that covers a range of topics, such as the development of intelligent control algorithms for active safety systems, smart sensors, and intelligent and efficient driving. The contributions presented in these papers can serve as useful tools for researchers who are interested in new vehicle technology and in the improvement of vehicle control systems
Autopilot simulator prototype for autonomous driving based on SimTwo
The main objective of this work was to develop a control system for an autonomous vehicle
that provides autonomous driving. For this, a simulation software, named "SimTwo" was
used, where the actuation and sensing model was developed.
At the end of the work, a control and 3D visualization system was obtained for an
autonomous vehicle capable of driving on a road, avoiding obstacles, alerting in case
of danger, among others. The work was developed in a simulation environment and
includes a 3D model of a road, with several real scenarios, where the vehicle moves. There
are objects on the circuit that can obstruct the passage of the car, creating situations
of imminent danger. This system alerts the driver in the event of danger and reacts
by deflecting or stopping. This control system uses image sensors and LiDAR (Light
Detection And Ranging) as inputs data sources.O principal objetivo deste trabalho foi desenvolver um sistema de controlo de um veículo
autónomo que o dote de condução autónoma. Para tal, foi utilizado um software de
simulação, SimTwo, onde o modelo de atuação e sensorização foi desenvolvido.
No final do trabalho, obteve-se um sistema de controlo e visualização 3D de um veículo
autónomo capaz de conduzir numa estrada, desviar de obstáculos, alertar no caso de
perigo, entre outros. O trabalho foi desenvolvido num ambiente de simulação e contempla
um modelo 3D de uma estrada, com vários cenários reais, onde o veículo se desloca.
Existem objetos nas bermas que podem obstruir a passagem do carro, criando situações
de perigo eminente. Este alerta no caso de perigo e reage, desviando ou parando. Este
sistema de controlo utiliza sensores de imagem e LiDAR (da sigla inglesa "Light Detection
And Ranging"), como fontes de informação
FESTA. D2.4 Data analysis and modelling
The chapter of the handbook and the deliverable on data analysis will provide guidance and
general principles for
- pre-testing to check the usability of the system and the feasibility of the evaluation process,
- controlling the consistency of the chain and the precision with different sampling schemes,
- modelling the impact for each indicators and for an integrated evaluation including a
systemic and multidisciplinary interpretation of the effects,
- integrating and controlling the quality of space-time data from various sources (numerical,
video, questionnaires),
- selecting the appropriate statistical techniques for data processing, PI estimation and
hypothesis testing in accordance to the list of indicators and experimental design,
- scaling up from experimental data and identified models to population and network level.
Experimentalists stress the role and importance of a preliminary field test in FOT. Three main
objectives have been defined to make a preliminary diagnosis of usability of the systems and
to check the relevance and feasibility of the evaluation process. These preliminary tests are
very important for the practical deployment of the FOT as well as for the overall scientific
evaluation process.
Recommendations about the monitoring of local and global consistency of the chain of
operations from the database extraction to the hypothesis testing are given, especially to
ensure the validation of the calculation of the Performance indicators.
Integration of the outputs of the different analysis and hypothesis testing requires a kind of
meta-model and the competences of a multidisciplinary evaluation team, specially for
interpretation of the system impact and secondary effects (behavioural adaptation, learning
process, long-term retroaction, …).
In cooperation with WP2.2, methods for data quality control have been defined. Four types of
checks have been defined to complement the information of the data base in order to prepare
the data for the analysis.
Statistical methods have been described for three steps of the chain: data processing, PI
calculation and hypothesis testing. They belong either to exploratory data analysis or to
inferential analysis. Special attention has been given to the precision of the estimates of the
effects or impacts of the system on the Performance indicators by stressing the importance of
controlled randomisation and application of mixed regression models.
Scaling-up relies upon the potential to extrapolate from the PIs to estimates of the impact at
an aggregated level. Three approaches have been defined to carry out the scaling up process
from direct estimations to simulation models with the related assumptions. Models and methodologies for scaling up results on traffic flow, environmental effects (e.g. PM10, CO2,
Noise emissions in db) and traffic safety have been collected
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