Advanced Microsystems for Automotive Applications 2017

Smart Systems Transforming the Automobile
 
 
Springer (Verlag)
  • erschienen am 29. August 2017
  • |
  • XI, 247 Seiten
 
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
E-Book | PDF mit Wasserzeichen-DRM | Systemvoraussetzungen
978-3-319-66972-4 (ISBN)
 
This volume of the Lecture Notes in Mobility series contains papers written by speakers and poster presenters at the 21st International Forum on Advanced Microsystems for Automotive Applications (AMAA 2017) "Smart Systems Transforming the Automobile" that was held in Berlin, Germany in September 2017. The authors report about recent breakthroughs in electric and electronic components and systems, driver assistance and vehicle automation as well as safety and testing. Furthermore, legal aspects and impacts of connected and automated driving are covered. The target audience primarily comprises research experts and practitioners in industry and academia, but the book may also be beneficial for graduate students alike.
1st ed. 2018
  • Englisch
  • Cham
  • |
  • Schweiz
Springer International Publishing
  • 104
  • |
  • 12 s/w Abbildungen, 104 farbige Abbildungen
  • |
  • 12 schwarz-weiße und 104 farbige Abbildungen, Bibliographie
  • 8,93 MB
978-3-319-66972-4 (9783319669724)
3319669729 (3319669729)
10.1007/978-3-319-66972-4
weitere Ausgaben werden ermittelt
  • Intro
  • Preface
  • Organisation Committee
  • Funding Authority
  • Supporting Organisations
  • Organisers
  • Steering Committee
  • Conference Chair
  • Conference Organizing Team
  • Contents
  • Smart Sensors
  • 1 Smart Sensor Technology as the Foundation of the IoT: Optical Microsystems Enable Interactive Laser Projection
  • Abstract
  • 1 MEMS Sensors-The Hidden Champions
  • 1.1 Enablers for the Internet of Things
  • 1.2 Challenges and Barriers for IoT Sensors
  • 1.3 The Role of Smart Sensors in the IoT
  • 2 Interactive Laser Projection
  • 2.1 Making User Interfaces Simpler, More Flexible . and More Fun
  • 2.2 Interactive Projection in Practice
  • 2.3 A Window to the IoT
  • 2.4 Interactive Projection for the Automotive Industry
  • 2.4.1 Industry Teamwork
  • 2.5 Wearables and Beyond
  • 2.6 A Compact Module
  • 3 Conclusion
  • 2 Unit for Investigation of the Working Environment for Electronics in Harsh Environments, ESU
  • Abstract
  • 1 Introduction
  • 2 Monitoring Unit, ESU
  • 2.1 ESU Main Data
  • 2.1.1 Condensation Measurement
  • 2.1.2 Relative Humidity Measurement
  • 2.1.3 Vibration Measurement
  • 2.1.4 Temperature Measurement
  • 2.1.5 RTC
  • 2.1.6 User Interface
  • 2.2 Reliability of the ESU
  • 2.3 EMC Test
  • 3 Market Assessments
  • Acknowledgements
  • Reference
  • 3 Automotive Synthetic Aperture Radar System Based on 24 GHz Series Sensors
  • Abstract
  • 1 Introduction
  • 1.1 Automotive Radar Sensors
  • 1.2 Odometry
  • 2 Related Work
  • 3 SAR Algorithm
  • 4 Performance Estimation
  • 4.1 Azimuth Resolution
  • 4.2 Range Resolution
  • 4.3 Maximum Velocity
  • 5 Evaluation Environment
  • 6 Evaluation of Automotive Relevant SAR Properties
  • 6.1 Incorrect Trajectory Measurement
  • 6.2 Time-Based Sampling
  • 7 Simulation and Measurement
  • 7.1 Measurement
  • 7.2 Simulation
  • 8 Conclusion
  • Acknowledgements
  • References
  • 4 SPAD-Based Flash Lidar with High Background Light Suppression
  • Abstract
  • 1 Introduction
  • 2 Sensor Principle
  • 3 Technology and Measurements
  • 4 Summary
  • References
  • Driver Assistance and Vehicle Automation
  • 5 Enabling Robust Localization for Automated Guided Carts in Dynamic Environments
  • Abstract
  • 1 Introduction
  • 2 Related Work
  • 3 The MCL/MU Approach
  • 3.1 Map Update Control
  • 3.2 Map Update and Map Update Fusion
  • 4 Evaluation
  • 5 Conclusion
  • References
  • 6 Recognition of Lane Change Intentions Fusing Features of Driving Situation, Driver Behavior, and Vehicle Movement by Means of Neural Networks
  • Abstract
  • 1 Introduction
  • 2 Features Indicating Upcoming Lane Changes
  • 3 Implementation and Sensor Data
  • 4 Naturalistic Driving Study
  • 5 Neural Network for Feature Classification
  • 5.1 Artificial Neural Networks
  • 5.2 Network Design
  • 5.3 Network Parameterization
  • 6 Experimental Results
  • 7 Conclusion and Future Work
  • Acknowledgements
  • References
  • 7 Applications of Road Edge Information for Advanced Driver Assistance Systems and Autonomous Driving
  • Abstract
  • 1 Introduction
  • 2 Road Edge Detection
  • 2.1 Target Road Edge
  • 2.2 Road Edge Detection Result
  • 3 Application for Advanced Driver Assistance Systems
  • 3.1 Euro NCAP
  • 3.2 Integrated Lateral Assist System
  • 3.2.1 Overview of Virtual Lane Guide
  • 3.2.2 Target of VLG
  • 3.2.3 Coordination of EPS and ESC
  • 3.3 Experimental Result
  • 4 Application for Autonomous Driving
  • 4.1 Path Planning Algorithm
  • 4.1.1 Path Planner
  • 4.1.2 Path Selector
  • 4.2 Simulation Result
  • 4.3 Experimental Result
  • 5 Conclusion
  • References
  • 8 Robust and Numerically Efficient Estimation of Vehicle Mass and Road Grade
  • Abstract
  • 1 Introduction
  • 2 Methodology
  • 2.1 Test Vehicle and Test Tracks
  • 2.2 System Model
  • 2.3 Recursive Least Squares (RLS) Algorithm
  • 3 Sensitivity Analysis and Parameter Estimation
  • 3.1 Sensitivity Analysis
  • 3.2 Identification of Parameters and Validation of the Vehicle Model
  • 4 Results
  • 4.1 Validation with a Numerical Model
  • 4.2 Results in Real-World Driving Conditions
  • 5 Summary
  • References
  • 9 Fast and Accurate Vanishing Point Estimation on Structured Roads
  • Abstract
  • 1 Introduction
  • 2 Vanishing Point
  • 3 System Overview
  • 3.1 Double-Edge Detection
  • 3.2 Double-Edge Filtering
  • 3.3 Double-Edge Grouping to Lane Markings
  • 3.4 Lane Marking Filtering
  • 3.5 Lane Marking Simplification
  • 3.6 Vanishing Point Estimation
  • 4 Results
  • 5 Conclusion
  • References
  • 10 Energy-Efficient Driving in Dynamic Environment: Globally Optimal MPC-like Motion Planning Framework
  • Abstract
  • 1 Introduction
  • 2 Problem Definition
  • 2.1 Optimal Control Problem
  • 2.2 Computational Complexity
  • 3 Optimal Motion Planner
  • 3.1 Dynamic Programming
  • 3.2 Strategic Planning
  • 3.3 Situation-Dependent Replanning
  • 3.3.1 Prediction Horizon
  • 3.3.2 Replanning Triggering
  • 4 Simulation Results
  • 5 Conclusion
  • Acknowledgements
  • References
  • Data, Clouds and Machine learning
  • 11 Automated Data Generation for Training of Neural Networks by Recombining Previously Labeled Images
  • Abstract
  • 1 Introduction
  • 2 Related Work
  • 2.1 Available Public Datasets
  • 2.2 Image Manipulation and Recombination
  • 3 Semi-artificial Dataset Creation
  • 4 Evaluation
  • 5 Summary and Outlook
  • References
  • 12 Secure Wireless Automotive Software Updates Using Blockchains: A Proof of Concept
  • Abstract
  • 1 Introduction
  • 2 Background
  • 2.1 Wireless Automotive Software Updates
  • 2.2 Blockchains
  • 3 Architecture Enabling Wireless Software Updates
  • 3.1 Blockchain-Based Architecture Securing Wireless Software Updates
  • 3.2 Employing Our Architecture to Distribute New SW
  • 4 Proof of Concept
  • 5 Evaluation
  • 5.1 Overhead Due to the Use of Blockchains
  • 5.2 Latency Comparison: Local SW Update Versus SW Distribution Using BC
  • 5.3 Comparison of BC- and Certificate-Based Approaches
  • 6 Conclusion
  • References
  • 13 DEIS: Dependability Engineering Innovation for Industrial CPS
  • Abstract
  • 1 Introduction
  • 2 The Digital Dependability Identity (DDI) Concept
  • 3 The Four Industrial Use Cases in DEIS Project
  • 3.1 Automotive: Development of a Stand-Alone System for Intelligent Physiological Parameter Monitoring
  • 3.2 Automotive: Enhancement of an Advanced Driver Simulator for Evaluation of Automated Driving Functions
  • 3.3 Railway: Enabling Plug-and-Play Scenarios for Heterogeneous Railway Systems
  • 3.4 Health Care: Enhancement of Clinical Decision App for Oncology Professional
  • 4 Opportunities for DDI Applications
  • 5 Conclusions
  • References
  • Safety and Testing
  • 14 Smart Features Integrated for Prognostics Health Management Assure the Functional Safety of the Electronics Systems at the High Level Required in Fully Automated Vehicles
  • Abstract
  • 1 Introduction
  • 2 Prognostics Health Management
  • 3 PHM Strategy
  • 4 PHM Indicators and Parameters for the RUL Estimation
  • Acknowledgements
  • References
  • 15 Challenges for the Validation and Testing of Automated Driving Functions
  • Abstract
  • 1 Introduction
  • 2 Challenges for Validation and Testing
  • 2.1 Complexity of Automated Driving Functions
  • 2.2 Variation of Scenarios and Parameters
  • 2.3 Scenario Selection and Test Generation
  • 3 Current Methodologies/Technology Overview
  • 4 Validation-Global Approach
  • 5 Supporting Tools in the Validation Task
  • 6 Standardization
  • 7 Conclusion
  • Acknowledgements
  • References
  • 16 Automated Assessment and Evaluation of Digital Test Drives
  • Abstract
  • 1 Introduction
  • 2 State of the Art in Automotive Testing
  • 2.1 Test Processes and Methodologies
  • 2.2 Digital Test Drive
  • 3 Requirements and Constraints for Automated Assessment of Digital Test Drives
  • 4 Automated Assessment Concept
  • 4.1 HiL System
  • 4.2 Assessment Domain
  • 4.3 Visualization and Data Analytics Domain
  • 5 Application on Exemplary Driver-Assistance System
  • 6 Conclusion and Outlook
  • References
  • 17 HiFi Visual Target-Methods for Measuring Optical and Geometrical Characteristics of Soft Car Targets for ADAS and AD
  • Abstract
  • 1 Background
  • 2 Soft Car Targets
  • 3 Project Goals
  • 4 Initial Measurements and Results
  • 4.1 Measurement Setup
  • 4.1.1 Optical Measurement Setup
  • 4.1.2 Geometry Measurement Setup
  • 4.2 Preliminary Results
  • 4.2.1 Optical Measurement Results
  • 4.2.2 Geometry Variation Due to Assembly
  • 5 Conclusions and Future Work
  • Acknowledgments
  • References
  • Legal Framework and Impact
  • 18 Assessing the Impact of Connected and Automated Vehicles. A Freeway Scenario
  • Abstract
  • 1 Introduction
  • 2 Review of the Literature
  • 3 Case-Study Simulation
  • 3.1 The Traffic Model of Antwerp's Ring Road
  • 3.2 Human and CACC Drivers
  • 3.3 Assessment Metrics
  • 3.4 Simulation Scenarios
  • 4 Results
  • 4.1 Energy Consumption
  • 5 Conclusions
  • 19 Germany's New Road Traffic Law-Legal Risks and Ramifications for the Design of Human-Machine Interaction in Automated Vehicles
  • Abstract
  • 1 Introduction
  • 2 The Amendments to the Federal Road Traffic Act
  • 2.1 Levels of Automation Addressed
  • 2.2 Definition of "Driver"
  • 2.3 Interaction Between the Automation System and the Driver
  • 3 The Statutory Amendments from the Driver's Perspective
  • 3.1 Brief Overview of the Statutory Liability Regime for Drivers
  • 3.2 Ramifications of the Obligations Imposed on Automated System Users
  • 3.2.1 Obligation to Use the Automation System Properly
  • 3.2.2 Sharing of the Driving Task Between the Driver and the Automation System
  • 4 Liability Issues from the Manufacturer's Perspective
  • 4.1 Brief Overview of the Statutory Liability Regime for Manufacturers
  • 4.2 Product Liability Issues in Relation to Automated Vehicles
  • 4.2.1 Constructional Deficiencies
  • 4.2.2 Instructional Errors
  • 5 Summary
  • References
  • 20 Losing a Private Sphere? A Glance on the User Perspective on Privacy in Connected Cars
  • Abstract
  • 1 Introduction
  • 2 Literature Review
  • 2.1 Methodology
  • 2.2 Relevant Privacy Factors for the Adoption of Connected Services
  • 3 User Study
  • 3.1 Results
  • 3.2 Discussion
  • 4 Conclusion and Practical Implications

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