• Corpus ID: 233024765

Autonomous Driving Data Chain & Interfaces

@article{Kahl2021AutonomousDD,
  title={Autonomous Driving Data Chain \& Interfaces},
  author={Benjamin Kahl},
  journal={ArXiv},
  year={2021},
  volume={abs/2104.01252}
}
Recent developments in autonomous driving technology have proven that map data may be used, not only for general routing purposes, but also for to enhance and complement common sensor data. This document reviews the most commonly used interfaces and formats at each step of a self-healing map data chain. 

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References

SHOWING 1-9 OF 9 REFERENCES
How Does a Self-Driving Car See?
  • Nvidia Blogs. URL: https://blogs.nvidia.com/blog/2019/04/
  • 2019
ADASIS and SENSORIS platforms and their impact Automotive Ecosystem" slides, IAEC
  • 2018
Introduction to ERTICO & platforms" slides
  • 2018
Map Line Interface for Autonomous Driving
TLDR
It is shown that the usage of mean values and covariance matrices approximate the true distributions rather accurately, and therefore are both from an accuracy point of view and from a bandwidth points of view the way to represent map lines in interfaces.
Automated Driving Data Chain Challenges" slides, Automated Vehicle Symposium
  • 2017
Evaluation of 3D-distance measurement accuracy of stereo-vision systems
TLDR
An accuracy evaluation method of two stereo-vision systems, which use a coordinate measuring machine (CMM) and a reference block, has been presented, and the results have been presented for systems using infra red cameras and webcams.
SENSORIS Version 1.0.0 official public specification
  • 2017
Know Your Limits: Accuracy of Long Range Stereoscopic Object Measurements in Practice
TLDR
A comprehensive statistical evaluation of selected state-of-the-art stereo matching approaches on an extensive dataset is presented and reference values for the precision limits actually achievable in practice are established.
RADAR, camera, LiDAR and V2X for autonomous cars
  • 2011