Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (1/1 displayed)

  • 2021Coupling Apollo with the CommonRoad Motion Planning Framework7citations

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Rettinger, Anna-Katharina
1 / 1 shared
Waez, Md Tawhid Bin
1 / 1 shared
Wang, Xiao
1 / 18 shared
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2021

Co-Authors (by relevance)

  • Rettinger, Anna-Katharina
  • Waez, Md Tawhid Bin
  • Wang, Xiao
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document

Coupling Apollo with the CommonRoad Motion Planning Framework

  • Rettinger, Anna-Katharina
  • Waez, Md Tawhid Bin
  • Althoff, Matthias
  • Wang, Xiao
Abstract

The development of autonomous vehicles requires extensive testing of software modules. Developing a reliable software platform which allows testing on a real vehicle is yet a challenging task. Therefore, open-source software platforms are becoming more important for researchers in the field of autonomous driving. For example, Baidu provides the open-source autonomous driving platform Apollo which aims to accelerate testing and deployment of autonomous vehicles. However, the complex software structure hinders an easy integration of developed software modules, especially the motion planning module. Moreover, Baidu's Apollo provides only one possibility to test one's own algorithms in simulation, namely to upload the algorithm in Baidu's cloud platform, which is unacceptable for most autonomous driving companies.In contrast, the open-source CommonRoad benchmark suites contain diverse testing scenarios, e.g., highway, urban, dense traffic, and interaction with bicyclists and pedestrians. In addition, CommonRoad provides a motion planning framework in Python which enables rapid planner prototyping, along with additional tools, e.g., efficient collision checker, map format converter, and interface with the traffic simulator SUMO.In this work, we introduce a Python API between the planning module of the Baidu Apollo platform and the CommonRoad software framework. The developed interface aims to bridge the gap between rapid prototyping for safe planning algorithms and real-time test drives. The API transfers perception and map information to the planner and then returns the planned trajectory. The users can either replace the Apollo planner with their own planner or integrate their planner as a fail-safe planner if the planned trajectory by Apollo is unsafe. With our interface, developers can first test their planners in diverse scenarios from the CommonRoad benchmark, and directly on a real vehicle afterwards using the Apollo platform. The latter can be performed without adapting their algorithms to Apollo software structures. Moreover, developers can record their test drives in CommonRoad format for offline analyses. We demonstrate our interface in several scenarios with increasing complexity.

Topics
  • impedance spectroscopy
  • simulation