Vehicle Intelligence & Automated Driving
Connected and automated driving will significantly shape the future of mobility. In the research area Vehicle Intelligence & Automated Driving, the institute bundles the necessary competencies from conceptual design to system and function development to verification and validation for the realization of connected and automated vehicles and their functions. All aspects of the conflicting fields of sustainability, safety and mobility experience are considered equally. The research area researches and develops methods and tools for this purpose, both in simulation and for use in real vehicles. Thanks to the comprehensive research infrastructure, forward-looking research questions can be addressed efficiently and competently in direct bilateral projects or within the framework of sovereign research. The latest artificial intelligence methods, which are capable of solving the complex challenges of automated and networked mobility with a high degree of reliability and in a way that is comprehensible to humans, play a major role here.
The research area has modern infrastructure at its disposal. Thus, among other things, research vehicles for operation in public road traffic, independently developed vehicle prototypes for the implementation of novel research approaches, infrastructure test fields, scenario data beacons and a unique simulator center are available. The latter includes various simulator test rigs, from pedestrian to cyclist and static driving simulators, to a highly dynamic driving simulator - unique in Germany in its free availability.
The "Automated Driving Function Development" group researches and develops software modules and systems. These include, in particular, perception, fusion, tracking, prediction, planning, control, and V2X communication, which are combined as microservices in a service-oriented architecture.
A particular focus of the group is also on networking automated vehicles with each other and with smart infrastructure services and (edge) clouds. Methods for collective perception, planning and learning are being researched that support automated vehicles with functions and over-the-air updates & upgrades. This will make an important contribution to the transformation to Software-Defined-Vehicles and the efficient use of DevOps methods in the field of automated connected mobility.
The "Data and Effectiveness" group researches and develops methods and concepts for analyzing mobility data, quantifying effectiveness, and safeguarding connected and automated vehicles and their functions.
In addition to the analysis of vehicle data, the focus is also on information from other observation sources such as drones and intelligent infrastructure, which allow important additional insights. The aim is to comprehensively analyze aspects of automated driving with regard to safety in public road traffic. Downstream and supported by simulations, the data will be used for validation and effectiveness analysis. For these aspects, the scenarios (concepts) developed at the institute are introduced and expanded in various national and international research projects. The scenarios are then used for detailed analysis of the data.
The "Simulation" group researches and develops methods for the simulative mapping of real mobility systems and their elements. This provides important tools for the development, analysis and testing of networked and automated functions.
This includes, among other things, the establishment of end-to-end simulation tool chains, the development of multivalent driver models, automated environment generation, and test automation of scenarios.
The basis for this research is the simulator center of the ika, where subject studies of various sizes can be implemented according to project needs. In particular, the potentials through the use of augmented and virtual reality are increasingly illuminated, for example, for purposes of rapid prototyping. With the developed simulation environments, human, software and hardware-in-the-loop tests can be realized for various use cases.
The "Intelligent Infrastructure" group researches and develops methods for infrastructure-side support of connected and automated vehicles.
The main fields of work are environment sensing by intelligent sensors installed in road traffic, infrastructure-to-vehicle (I2V) communication, and the transfer of the sensed data into a highly accurate digital twin of the traffic. The digital twin generated by the intelligent infrastructure represents a virtual image of the physical real traffic and enables both the collection of trajectory data sets, which can be used for further development and validation purposes, and the transmission of safety-relevant real-time environment information to test vehicles in real traffic.
The datasets resulting from the smart infrastructure extend the public trajectory datasets highD, inD, rounD, uniD and exiD already offered by the ika.
As a university institute, teaching is one of the most important tasks of all involved. We offer the expertise we have built up over the years to customers from industry and conduct customized continuing education and training courses for companies in the ika Academy to prepare employees for the networked and automated automotive future. The course content can be adapted to specific customer requirements and offered in various levels of detail. From sensors to actuators and from object recognition to V2I networking, all relevant topics are prepared and taught in interactive formats.
The research area Vehicle Intelligence & Automated Driving stands for excellent teaching at RWTH Aachen University. We address students of different disciplines from mechanical engineering, automotive engineering, automation engineering to electrical engineering and computer science. Our courses form the basis for a later career in the field of connected and automated mobility:
- Automotive Engineering III: Introduction to ADAS (Driver Assistance Systems)
- Automated Driving: Independent lecture with a focus on networked and automated mobility. (Previous participation in Automotive Engineering I-III is not required).
- Automated and Connected Driving Challenges: Our latest learning event for students at RWTH and worldwide. Offered as a Moog, ACDC provides insight into the latest research questions and gives students the chance to independently implement and test automated and networked functions step by step.
- Root cause analysis in motor vehicle accidents: How do traffic accidents occur and what can we learn from them? In this course held by accident expert Prof. Möhler, students learn exactly that.
- Automotive Engineering – Practical Course: As part of the automotive lab, students get to know our highly dynamic driving simulator and have the chance to experience the latest assistance systems up close.
- Intelligent infrastructure sensor technology
- Infrastructure-to-vehicle (I2V) communication via ITS-G5 and mobile communications
- Cloud Intelligence and Digital Twin
- Driving simulators
- Driver models
- Human/Software/Hardware in the Loop Testing
- Explainable and Trustworthy AI
- (Micro-)Service-Oriented Architecture
- Data sets from drones and infrastructure
- Tailored training and continuing education offerings in the field of connected and automated mobility
As a strong partner in bilateral and sovereign-funded projects, the Vehicle Intelligence & Automated Driving research area supported its partners in the following issues, among others:
- Definition of requirements for “Automation Level 2 Hands-Off Systems”
- Design of software architectures for connected and automated driving
- Integration of service-oriented software functions
- Design and implementation of multi-week test person studies in the highly dynamic driving simulator for various questions
- Verification and validation of systems using scenario-based approaches
- Generation of trajectory data sets from drone images
- Integration of innovative camera and LiDAR-based assistance and automation functions
- Building smart infrastructure and creating digital twins
- Construction and commissioning of test vehicles and prototypes
- Predictive powertrain control for conventional and hybrid drive concepts
- communication-based traffic performance assistance and intersection assistants
- Automated valet parking (L4)
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Timo Woopen M.Sc.
Manager Research Area
Vehicle Intelligence & Automated Driving
+49 241 80 23549
Equipment and test benches
- Aldenhoven Testing Center (ATC)
- Bicycle Simulator
- Highly Dynamic Driving Simulator
- Pedestrian Simulator
- Roadside Infrastructure
- Seating Bucket
- Spray Truck
- Static Driving Simulator
- Tools for Evaluation and Validation of ADAS Sensors and Functions
- V2X Communication
- ika test track
- Generation of Concrete Parameters from Logical Urban Driving Scenarios based on Hybrid Graphs
Friday, April 14, 2023
- Cloud-Intelligenz und kollektives Lernen für das automatisierte und vernetzte Fahren
Saturday, October 15, 2022
- Generic Approach to Optimized Placement of Smart Roadside Infrastructure Sensors Using 3D Digital Maps
Wednesday, October 12, 2022
- Selection of Test Cases for the Verification of Automated Vehicles
Wednesday, October 12, 2022
- Future Mobility Applications in the KoMoD-next and ACCorD Digital Test Fields
Wednesday, October 12, 2022
Selection of current theses:
- Assessment of Simulation Quality Impact on Automated Driving Functions
- Künstliche Generierung komplexer Fahrszenarien zur Absicherung automatisierter Fahrzeuge
- Deep Learning getriebene Domänenadaption von Punktwolken
- 3D Object Detection with Transformers in LiDAR Point Clouds
- Systematische Erkennung und Extraktion von Edge Cases aus großen Datensätzen