Autonomous Reply is partner for the
software and system integration of Autonomous Things (AuT). The experts advise and support companies in the industrial, automotive and New Mobility sector, from sensor technology to infrastructure. Autonomous Reply's services include consulting, software development and the integration of autonomous solutions. Including computer vision and deep learning the experts apply cutting-edge technologies and methods from the field of artificial intelligence (AI).
Including all facets of software development, Autonomous Reply has a special focus on the integration of subsystems and platforms. Classic software development, model-based software and ROS applications as well as edge computing are the core topics. In addition, the team works with the world's leading providers in the area of cloud infrastructure. Autonomous Reply accompanies companies along the entire path to an autonomous future, from the idea to a proof of concept to series software. Depending on the use case, in software development Autonomous Reply uses classic methods and processes such as the V-Model or A-Spice as well as agile approaches. In this way, the experts support companies in defining the right requirements, building the corresponding software architecture and the final testing of the software code.
We are always looking for enthusiasts who want to question the existing, try out new ideas and achieve exciting goals together.
Autonomous Reply develops embedded software in C/C++ for autonomous systems, with a special focus on real-time capability, maximum performance and optimal memory utilisation. For this purpose, various application frameworks and platforms are used. CODESYS is an example of an application implementation in an industrial environment, and AUTOSAR in the automotive sector.
For special subject areas such as navigation localisation solutions and deep learning approaches, the specialists use ROS/ROS2 and NVIDIA ISAAC. However, for this a powerful edge device is required.
Model-based function controlAutonomous Reply develops software and functions tailored to needs, requirements and available budget. With model-based function development, the developed software components can be tested on prototype ECUs at an early stage of development, which minimises time-to-market. Using automatic code generators such as TargetLink, the models developed in Matlab, Simulink and Stateflow can be generated as production code. This allows early verification through simulation and testing.
In addition, Autonomous Reply supports both the development-accompanying and the final virtual testing of the functions and algorithms. The portfolio ranges from initial model checks to MiL and SiL tests to embedding the software in virtual environments such as CarMaker with subsequent simulations and their evaluations.
In summary, the focus for rule algorithms is on the following topics:
Autonomous Reply develops innovative software solutions for autonomous systems with a focus on edge computing and software-defined systems in Python, Java C/C++. Especially in the areas of deep learning, navigation and localisation, where highly complex algorithms and powerful controllers with maximum performance are required, the experts rely on software frameworks such as ROS/ROS2 and NVIDIA ISAAC.
Another focus in this topic area is cloud application development, which is characterised by cloud-based software development and a direct connection to the edge device. By means of code creation and a CI/CD functionality for direct deployment on the edge device, the autonomous vehicle can be kept up-to-date via software updates. This creates a flexible balance between applications on the edge device and applications in the cloud, enabling high-performance hybrid solutions for HD Maps, navigation and fleet management, to name a few.
ROS applicationsAutonomous Reply offers knowledge and expertise for the conception, design, implementation and support of customised solutions in the areas of perception, navigation, control and simulation.
NVIDIA ISAAC applicationsIn the field of NVIDIA's ISAAC robotics platform, Autonomous Reply provides all the steps in the development of a software application for autonomous things. This includes the integration of existing ISAAC components, for example for localisation and mapping (SLAM), trajectory planning or obstacle avoidance, as well as the development of new ISAAC components. Beyond this, the experts create new ISAAC sim scenarios and use them to realistically test use cases or to generate training data for Deep Learning based computer vision algorithms (for object classification, 2D/3D object detection or semantic segmentation).
The most important elements of autonomously moving things are object recognition and classification capabilities as well as tracking and prediction of the future pose/position of objects.
Autonomous Reply therefore offers a comprehensive service especially in the areas of computer vision and deep learning. This includes data identification and selection for a representative dataset, identification of a suitable architecture for and training of the neural network as well as optimisation of the models with regard to accuracy and inference times. Last but not least, Autonomous Reply also takes care of the integration of the elements on the target platform.
The experts develop and train efficient deep learning models for this implementation. In doing so, Autonomous Reply uses both native cloud services and its own neural networks. In addition, the experts also offer services in the field of deep learning, which are used for navigation, trajectories/path planning and motion prediction.
Autonomous Reply helps to concretise the challenges around the topic of Autonomous Things and to define the appropriate solution paths in order to achieve goals efficiently and precisely.
The team advises on the best possible end-to-end strategies for autonomous systems, starting with advice on system architecture, the selection of hardware and the appropriate communication channels. In a cross-functional team, Autonomous Reply develops the best possible solution for each application.
The advisory services include:
Together with the City of Regenburg and the University of Regensburg, Autonomous Reply is designing a safe, smart city with autonomous vehicles. The aim is collision avoidance, by involving all road users in the interaction between autonomously driving things.
The Digital Product Forum, is the central information and interaction platform around the digitalization of development at
Mercedes-Benz AG and its partners.
Reply is present as an exhibitor both with a digital booth in the virtual world and in presence at the double booth 42.A. Online, the event will take place from July 18-21, on-site at the
ICS Stuttgart from July 20-21, 2022.
Reply faced the best European Robotics teams in the 2021 edition of the SciRoc Challenge and won the "Episode 01 - Deliver Coffee Shops Orders" with its Robotics Team. The team was able to demonstrate the potential and efficiency of the TIAGo Robot and Hey5 robotic hand.
In the development of autonomous driving vehicles, the topic of software is taking up more and more space. Autonomous Reply supports companies in developing safer systems with automated simulation methods in software testing, while saving time.
Executing ArtificiaI Intelligence and Machine Learning directly on Edge devices paves the way for a new era of Intelligent and Autonomous things. Reply has gained considerable experience and technical expertise in the Edge AI landscape through numerous projects, using Autonomous Mobile Robots and drones in various scenarios.
Reference date, permanent and deferred inventory: In intralogistics, keeping track of inventory levels is a central issue – both for smooth supply chain processes and for annual balance sheets. Using automated position determining, path planning and object recognition Autonomous Reply is developing the next generation of drones to reduce the efforts.
Autonomous Reply is represented from 07 to 12 September at the
IAA Mobility 2021. The experts present "Collision Detection in Smart City Systems", a pilot project that combines computer vision and LIDAR, edge-to-cloud methods and deep learning. You can learn more about the
modular object detection and accident avoidance system and about a future-proof software-defined architecture for the car of the future.
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