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 Practice2.3 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

AcknowledgementsReference; 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 SuppressionAbstract; 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

Abstract1 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