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Naji, M. |
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Motta, Antonella |
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Aletan, Dirar |
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Mohamed, Tarek |
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Ertürk, Emre |
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Taccardi, Nicola |
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Kononenko, Denys |
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Petrov, R. H. | Madrid |
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Alshaaer, Mazen | Brussels |
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Bih, L. |
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Casati, R. |
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Muller, Hermance |
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Kočí, Jan | Prague |
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Šuljagić, Marija |
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Kalteremidou, Kalliopi-Artemi | Brussels |
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Azam, Siraj |
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Ospanova, Alyiya |
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Blanpain, Bart |
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Ali, M. A. |
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Popa, V. |
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Rančić, M. |
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Ollier, Nadège |
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Azevedo, Nuno Monteiro |
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Landes, Michael |
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Rignanese, Gian-Marco |
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Wang, Xiao
Technical University of Munich
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (18/18 displayed)
- 2023Development of high-voltage and high-energy membrane-free nonaqueous lithium-based organic redox flow batteriescitations
- 2022Discovery of high-entropy oxide electrocatalysts: from thin-film material libraries to particlescitations
- 2021Redefining architectural effects in 3D printed scaffolds through rational design for optimal bone tissue regenerationcitations
- 2021Unraveling the formation mechanism of nanoparticles sputtered in ionic liquidcitations
- 2021Fe2Co2Nb2O9: a magnetoelectric honeycomb antiferromagnetcitations
- 2021Combining switchable phase‐change materials and phase‐transition materials for thermally regulated smart mid‐infrared modulatorscitations
- 2021Influence of low Bi contents on phase transformation properties of VO<sub>2</sub> studied in a VO<sub>2</sub>:Bi thin film librarycitations
- 2021Fe 2 Co 2 Nb 2 O 9 :A magnetoelectric honeycomb antiferromagnetcitations
- 2021Coupling Apollo with the CommonRoad Motion Planning Frameworkcitations
- 2020Structure Zone Investigation of Multiple Principle Element Alloy Thin Films as Optimization for Nanoindentation Measurementscitations
- 2020SAQEcitations
- 2020Influences of Cr content on the phase transformation properties and stress change in V-Cr-O thin-film librariescitations
- 2020Structure zone investigation of multiple principle element alloy thin films as optimization for nanoindentation measurements
- 2020High-throughput characterization of (Fe<sub><i>x</i></sub>Co<sub>1–<i>x</i></sub>)<sub>3</sub>O<sub>4</sub> thin-film composition spreadscitations
- 2018Application of an A-A′-A-Containing Acceptor Polymer in Sequentially Deposited All-Polymer Solar Cellscitations
- 2018Influences of W content on the phase transformation properties and the associated stress change in thin film substrate combinations studied by fabrication and characterization of thin film V1-xWxO2 materials librariescitations
- 2018Metallic contact between MoS2 and Ni via Au Nanogluecitations
- 2015Polymorphism of a polymer precursor: metastable glycolide polymorph recovered via large scale high-pressure experimentscitations
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document
Coupling Apollo with the CommonRoad Motion Planning Framework
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.