Probabilistic Integration of GNSS for Safety-Critical Driving Functions and Automated Driving-the NAVENTIK Project

被引：1

作者：

Streiter, Robin ^{[1
]}

Hiltscher, Johannes ^{[2
]}

Bauer, Sven ^{[2
]}

Juettner, Michael ^{[2
]}

机构：

[1] Tech Univ Chemnitz, Reichenhainer Str 70, D-09126 Chemnitz, Germany

[2] NAVENTIK GmbH, Reichenhainer Str 70, Chemnitz, Germany

来源：

ADVANCED MICROSYSTEMS FOR AUTOMOTIVE APPLICATIONS 2016: SMART SYSTEMS FOR THE AUTOMOBILE OF THE FUTURE | 2016年

关键词：

GNSS; Localization; Automated driving; Safety requirements; Functional safety;

D O I：

10.1007/978-3-319-44766-7_2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The NAVENTIK project will develop an automotive platform for computational demanding applications in the field of sensor data fusion and software defined radio. Based on this platform, the first component launched will be an automotive-grade GNSS (Global Navigation Satellite System) receiver that integrates state-of-the-art signal processing for lane level accurate navigation and that guarantees bounded false alarm rates. This is possible, thanks to a software-defined approach and the probabilistic integration of GNSS signal tracking algorithms on radio level. The explicit modelling of GNSS error sources and local signal degradation provide the basis for the proper Bayesian integration. The project will enable the first mass-market GNSS receiver based on a software-defined approach that is able to meet safety-critical requirements as it copes with false alarm specifications and safety related requirements.

引用

页码：19 / 29

页数：11

共 50 条

[31] Barrier-Enhanced Parallel Homotopic Trajectory Optimization for Safety-Critical Autonomous Driving
Zheng, Lei
Yang, Rui
Wang, Michael Yu
Ma, Jun
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2025, 26 (02) : 2169 - 2186
[32] KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients
Hanselmann, Niklas
Renz, Katrin
Chitta, Kashyap
Bhattacharyya, Apratim
Geiger, Andreas
COMPUTER VISION, ECCV 2022, PT XXXVIII, 2022, 13698 : 335 - 352
[33] An Architecture Pattern for Safety Critical Automated Driving Applications: Design and Analysis
Luo, Yaping
Saberi, Arash Khabbaz
Bijlsma, Tjerk
Lukkien, Johan J.
van den Brand, Mark
2017 11TH ANNUAL IEEE INTERNATIONAL SYSTEMS CONFERENCE (SYSCON), 2017, : 261 - 267
[34] Confidence-driven weighted retraining for predicting safety-critical failures in autonomous driving systems
Stocco, Andrea
Tonella, Paolo
JOURNAL OF SOFTWARE-EVOLUTION AND PROCESS, 2022, 34 (10)
[35] nn-dependability-kit: Engineering Neural Networks for Safety-Critical Autonomous Driving Systems
Cheng, Chih-Hong
Huang, Chung-Hao
Nuehrenberg, Georg
2019 IEEE/ACM INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN (ICCAD), 2019,
[36] Automatic Generation of Critical Test Cases for the Development of Highly Automated Driving Functions
Baumann, Daniel
Pfeffer, Raphael
Sax, Eric
2021 IEEE 93RD VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-SPRING), 2021,
[37] Real World Scenarios for the Safety Validation and Development of Highly Automated Driving Functions
Moers T.
Klas C.
Zlocki A.
Vater L.
VDI Berichte, 2022, 2022 (2394): : 121 - 130
[38] "The Dresden Method": A toolbox for the holistic evaluation of active safety and automated driving functions
Mai M.
Bäumler M.
Lehmann M.
Siebke C.
Blenz K.
Prokop G.
Bönninger J.
Höpping K.
VDI Berichte, 2022, 2022 (2387): : 419 - 434
[39] Modeling driver behavior in critical traffic scenarios for the safety assessment of automated driving
Fries, Alexandra
Lemberg, Ludwig
Fahrenkrog, Felix
Mai, Marcus
Das, Arun
TRAFFIC INJURY PREVENTION, 2023, 24 : S105 - S110
[40] Automatically Learning Fallback Strategies with Model-Free Reinforcement Learning in Safety-Critical Driving Scenarios
Lecerf, Ugo U. L.
Yemdji-Tchassi, Christelle C. Y.
Aubert, Sebastien S. A.
Michiardi, Pietro P. M.
PROCEEDINGS OF 2022 7TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING TECHNOLOGIES, ICMLT 2022, 2022, : 209 - 215

← 1 2 3 4 5 →