Project

UNICARagil

Goal: Autonomous electric vehicles will play a key role in mastering the challenges relating the increasing demand for mobility and the ongoing urbanization. They provide the basis for sustainable and smart road traffic, innovative mobility and transport concepts as well as improved road safety and urban quality of life.

In the UNICARagil project, fully automated, driverless, electric vehicles will be developed based on the latest results of research in automated and connected driving as well as electric mobility. The foundation for this will be a modular and scalable vehicle concept, which is consisting of a utility and a drive unit. It can be adapted to a variety of applications in logistics and passenger transport. In particular, these applications can be utilized by driverless, locally emission-free vehicles. Key component of the research and development work is the functional vehicle architecture, which is connected to the cloud, the road infrastructure and a sensor drone. Further key areas are the development of generic sensor modules for environment detection and a flexible expandable and updateable software and hardware architecture along with highly dynamic wheel hub drives. These enable the vehicle to perform completely new forms of movement. The finalization of the project consists of a demonstration in four different applications on test fields in Germany.

For more information visit: www.unicaragil.de

Date: 1 February 2018 - 31 January 2022

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Project log

Lennart Reiher
added a research item
Collective Memory developed in the project UNICARagil
Bastian Lampe
added 2 research items
Cloud-based Inverse Perspective Mapping developed in the project UNICARagil
Cloud Services for Collective Driving developed in the project UNICARagil
Frank Diermeyer
added a research item
Rapid prototyping has become increasingly popular over the past years. However, its application is heavily confined to a part size that fits the small build volume of additive machines. This paper presents a universal design method to overcome this limitation while preserving the economic advantages of rapid prototyping over conventional processes. It segments large, thin-walled parts and joins the segments. The method aims to produce an assembly with minimal loss to the performance and characteristics of a solid part. Based on a set of requirements, a universal segmentation approach and a novel hybrid joint design combining adhesive bonding and press fitting are developed. This design allows for the force transmission, positioning, and assembly of the segments adaptive to their individual geometry. The method is tailored to fused deposition modeling (FDM) by minimizing the need for support structures and actively compensating for manufacturing tolerances. While a universal application cannot be guaranteed, the adaptive design was proven for a variety of complex geometries. Using automotive trim parts as an example, the usability, benefits, and novelty of the design method is presented. The method itself shows a high potential to overcome the build volume limitation for thin-walled parts in an economic manner.
Raphael van Kempen
added a research item
Self-driving vehicles as part of a connected mobility system are posing completely new challenges compared to conventional driver-centric vehicles. The UNICARagil consortium has developed disruptive architectures for a future mobility system, cover- ing all relevant domains inside and outside of the vehicle. Its modular structure in combination with adequate project organization and tools enable an agile and distrib- uted vehicle development, which is demonstrated during the ongoing construction of four vehicle prototypes. This paper outlines the key factors enabling this agile vehicle development from an organizational and technical point of view.
Armin Mokhtarian
added a research item
This paper outlines a novel framework for a cloud-based computational model that supports the interconnection of autonomous vehicles, road-infrastructure and humans. Besides serving as a central entity to call autonomous vehicles like the private vehicle, taxi or bus, this architecture can be used to automate and manage vehicle delivery services. Examining these different application domains led to a service-oriented cloud architecture that distinguishes between two kinds of services. Domain dependent services for each of the vehicle types and core services which are imperative for all applications. Since several core services require real-time vehicle-to-cloud communication, we present and evaluate a suitable communication system. This cloud architecture is characterized by its modular design in order to enable adaptation to different system requirements. In fact, it has found its application in the UNICARagil project, where four types of networked and autonomous vehicles are developed.
Private Profile
added a research item
A reliable and highly-available estimation of the vehicle's dynamic state, which is providing integrity information, is essential for automated driving. A possible solution for this challenge is a modular structure consisting of a first level with redundant multi-sensor data fusion filters and a second level processing the first level's outputs. In this work, we propose a concept for the second fusion level performing a data fusion based on the first level's integrity information. We present an implementation of our concept using three fusion filters which input sensor data from a Global Navigation Satellite Signal receiver, an Inertial Measurement Unit and odometry sensors. Outputs are the estimated state including the position, velocity and heading (yaw angle) of the vehicle. This implementation is evaluated using measurements collected in four typical scenarios for automated driving with different environmental conditions especially regarding the satellite reception. In all tested scenarios, the proposed second fusion level fulfills the integrity requirements. The provided solution outperforms the inputs' solutions in the challenging urban scenario with respect to the accuracy while reaching a similar to increased availability depending on the estimated state under consideration.
Bassam Alrifaee
added a research item
This paper presents our testbed and software pipeline for automatic latency estimation for a service-oriented software architecture (SOA). This type of architecture consists of modular services that are dynamically combined at runtime to form a functioning system. As different service combinations become possible at runtime, agile approaches for testing the resulting systems become necessary. Besides other factors, latencies are of particular interest for the implementation of control systems. Our agile approach automatically generates dummy services, including interfaces and tasks for internal processing, based on a service description in a human-readable format. Services are then automatically distributed to the computers of our testbed, which are connected through Ethernet. We empirically obtain latency estimates for processing and communication steps for a given composition of services. In this paper we describe our data format, abstractions about internal run-time behavior of services and the code generation pipeline. The evaluation presents latency estimates that we are able to obtain through our testbed that resembles the sense-plan-act paradigm.
Armin Mokhtarian
added a research item
This paper presents the Automotive Service-Oriented Software Architecture (ASOA) [1]. ASOA enables software to be built using a runtime-integrated service-oriented software architecture. Software components in ASOA are split into platform-agnostic services , that can be integrated at runtime by a central controller. Services also possess generic interfaces, that allow any service supplying appropriate data to be connected to any other service requiring this data at runtime. The architecture also offers significant advantages related to reuse and replacement of services, as well as the interaction of the organizational structure of services. ASOA is already utilized in the UNICARagil project [12] and also provides organizational tools to aid organization in development.
Torben Stolte
added a research item
This paper presents a taxonomy that allows to define the fault tolerance regimes "fail-operational", "fail-degraded", and "fail-safe" in the context of automotive systems. Fault tolerance regimes such as these are widely used in recent publications related to automated driving, yet without definitions, which largely holds true for automotive safety standards, too. Moreover, we show that fault tolerance regimes defined in scientific publications related to the automotive domain are partially ambiguous as well as taxonomically unrelated. The presented taxonomy is based on terminology stemming from ISO 26262 as well as from systems engineering and uses four criteria to distinguish fault tolerance regimes. In addition to "fail-operational", "fail-degraded", and "fail-safe", the core terminology consists of "operational" and "fail-unsafe". These terms are supported by definitions of "available performance", "nominal performance", and a novel definition of the "safe state". For verification, we show by means of two examples from the automotive domain that the taxonomy can be applied to hierarchical systems of different complexity. Finally, we relate the definitions to the recently published technical report ISO/TR 4804, which also presents definitions of fault tolerance regimes.
Torben Stolte
added a research item
The advent of automated vehicles operating at SAE levels 4 and 5 poses high fault tolerance demands for all functions contributing to the driving task. At the actuator level, fault-tolerant vehicle motion control, which exploits functional redundancies among the actuators, is one means to achieve the required degree of fault tolerance. Therefore, we give a comprehensive overview of the state of the art in actuator fault-tolerant vehicle motion control with a focus on drive, brake, and steering degradations, as well as tire blowouts. This review shows that actuator fault-tolerant vehicle motion is a widely studied field; yet, the presented approaches differ with respect to many aspects. To provide a starting point for future research, we survey the employed actuator topologies, the tolerated degradations, the presented control approaches, as well as the experiments conducted for validation. Overall, and despite the large number of different approaches, the covered literature reveals the potential of increasing fault tolerance by fault-tolerant vehicle motion control. Thus, besides developing novel approaches or demonstrating real-time applicability, future research should aim at investigating limitations and enabling comparison of fault-tolerant motion control approaches in order to allow for a thorough safety argumentation.
Raphael van Kempen
added a research item
Evidential occupancy grid maps (OGMs) are a popular representation of the environment of automated vehicles. Inverse sensor models (ISMs) are used to compute OGMs from sensor data such as lidar point clouds. Geometric ISMs show a limited performance when estimating states in unobserved but inferable areas and have difficulties dealing with ambiguous input. Deep learning-based ISMs face the challenge of limited training data and they often cannot handle uncertainty quantification yet. We propose a deep learning-based framework for learning an OGM algorithm which is both capable of quantifying uncertainty and which does not rely on manually labeled data. Results on synthetic and on real-world data show superiority over other approaches.
Inga Jatzkowski
added a research item
Automated vehicles need to be aware of the capabilities they currently possess. Skill graphs are directed acylic graphs in which a vehicle's capabilities and the dependencies between these capabilities are modeled. The skills a vehicle requires depend on the behaviors the vehicle has to perform and the operational design domain (ODD) of the vehicle. Skill graphs were originally proposed for online monitoring of the current capabilities of an automated vehicle. They have also been shown to be useful during other parts of the development process, e.g. system design, system verification. Skill graph construction is an iterative, expert-based, manual process with little to no guidelines. This process is, thus, prone to errors and inconsistencies especially regarding the propagation of changes in the vehicle's intended ODD into the skill graphs. In order to circumnavigate this problem, we propose to formalize expert knowledge regarding skill graph construction into a knowledge base and automate the construction process. Thus, all changes in the vehicle's ODD are reflected in the skill graphs automatically leading to a reduction in inconsistencies and errors in the constructed skill graphs.
Private Profile
added a research item
High-integrity information about the vehicle’s dynamic state, including position and heading (yaw angle), is required in order to implement automated driving functions. In this work, a comparison of three integrity algorithms for the vehicle dynamic state estimation of a research vehicle for an application in automated driving is presented. Requirements for this application are derived from the literature. All implemented integrity algorithms output a protection level for the position and heading solution. In the comparison, four measurement data sets obtained for the vehicle dynamic state estimation, which is based on a Global Navigation Satellite Signal receiver, inertial measurement units and odometry information (wheel speeds and steering angles), are used. The data sets represent four driving scenarios with different environmental conditions, especially regarding the satellite signal reception. All in all, the Kalman Integrated Protection Level demonstrated the best performance out of the three implemented integrity algorithms. Its protection level bounds the position error within the specified integrity risk in all four chosen scenarios. For the heading error, this also holds true, with a slight exception in the very challenging urban scenario.
Private Profile
added a research item
For automated driving high-integrity localization information is essential. Often sensor fusion algorithms are used to fulfill this task. In this work three implementations of integrity concepts for such algorithms used in automated driving are compared. Therefore, requirements for sensor fusion algorithms used for automated driving functions of a prototype vehicle in a German research project are derived. Known integrity concepts are reviewed. A selection of three concepts that include the computation of protection levels is implemented. They are evaluated using a set of measurement data obtained for our loosely coupled GNSS/INS/Odometry fusion algorithm. With the chosen set of tuning parameters, all implemented protection levels bound the horizontal position error in the given measurements according to the specified integrity risk.
Bastian Lampe
added 3 research items
Accurate environment perception is essential for automated vehicles. Since occlusions and inaccuracies regularly occur, the exchange and combination of perception data of multiple vehicles seems promising. This paper describes a method to combine perception data of automated and connected vehicles in the form of evidential Dynamic Occupany Grid Maps (DOGMas) in a cloud-based system. This system is called the Collective Environment Model and is part of the cloud system developed in the project UNICARagil. The presented concept extends existing approaches that fuse evidential grid maps representing static environments of a single vehicle to evidential grid maps computed by multiple vehicles in dynamic environments. The developed fusion process additionally incorporates self-reported data provided by connected vehicles instead of only relying on perception data. We show that the uncertainty in a DOGMa described by Shannon entropy as well as the uncertainty described by a non-specificity measure can be reduced. This enables automated and connected vehicles to behave in ways not before possible due to unknown but relevant information about the environment.
Accurate environment perception is essential for automated driving. When using monocular cameras, the distance estimation of elements in the environment poses a major challenge. Distances can be more easily estimated when the camera perspective is transformed to a bird's eye view (BEV). For flat surfaces, Inverse Perspective Mapping (IPM) can accurately transform images to a BEV. Three-dimensional objects such as vehicles and vulnerable road users are distorted by this transformation making it difficult to estimate their position relative to the sensor. This paper describes a methodology to obtain a corrected 360° BEV image given images from multiple vehicle-mounted cameras. The corrected BEV image is segmented into semantic classes and includes a prediction of occluded areas. The neural network approach does not rely on manually labeled data, but is trained on a synthetic dataset in such a way that it generalizes well to real-world data. By using semantically segmented images as input, we reduce the reality gap between simulated and real-world data and are able to show that our method can be successfully applied in the real world. Extensive experiments conducted on the synthetic data demonstrate the superiority of our approach compared to IPM. Source code and datasets are available at https://github.com/ika-rwth-aachen/Cam2BEV.
Accurate environment perception is essential for automated driving. When using monocular cameras, the distance estimation of elements in the environment poses a major challenge. Distances can be more easily estimated when the camera perspective is transformed to a bird's eye view (BEV). For flat surfaces, Inverse Perspective Mapping (IPM) can accurately transform images to a BEV. Three-dimensional objects such as vehicles and vulnerable road users are distorted by this transformation making it difficult to estimate their position relative to the sensor. This paper describes a methodology to obtain a corrected 360{\deg} BEV image given images from multiple vehicle-mounted cameras. The corrected BEV image is segmented into semantic classes and includes a prediction of occluded areas. The neural network approach does not rely on manually labeled data, but is trained on a synthetic dataset in such a way that it generalizes well to real-world data. By using semantically segmented images as input, we reduce the reality gap between simulated and real-world data and are able to show that our method can be successfully applied in the real world. Extensive experiments conducted on the synthetic data demonstrate the superiority of our approach compared to IPM. Source code and datasets are available at https://github.com/ika-rwth-aachen/Cam2BEV
Stefan Ackermann
added a research item
Striving towards deployment of SAE level 4+ vehicles in public traffic, researchers and developers face several challenges due to the targeted operation in an open environment. Due to the absence of a human supervisor, ensuring and validating safety while driving automatically is one of the key challenges. The arising complexity of the technical system must be handled during the entire research and development process. In this contribution, we outline the coherence of different safety-activities in the research project UNICARagil. We derive high-level safety requirements and present the central safety mechanisms applied to automated driving. Moreover, we outline the approaches of the project UNICARagil to address the validation challenge for automated vehicles. In order to demonstrate the overall ap-proach towards a coherent safety argumentation, the connection of high-level safety requirements, safety mechanisms, as well as validation approaches is illustrated by means of a selected example scenario.
Timo Woopen
added a research item
In 2018, the German research project UNICARagil started as a collaboration between eight universities and eight industrial partners. Within four years, the project will develop disruptive modular architectures for agile automated vehicle concepts and present four fully automated and driverless vehicle prototypes. This paper gives a general mid-term overview on the project status and the first results. It introduces the different research domains addressed within the project. This paper does not contain any unpublished research and only summarizes the current project status.
Bassam Alrifaee
added a research item
This paper presents a methodology for the agile development of a cloud system in a multi-partner project centered around automated vehicles. Besides providing an external environment model as an additional input to the automation, the cloud system is also the main gateway for users to interact with automated vehicles through applications on mobile devices. Multiple factors are posing a challenge in our context. Coordination becomes especially challenging, as stakeholders are spread among different locations with backgrounds from various domains. Furthermore, automated vehicles for different applications, such as delivery or taxi services, give rise to a large number of use cases that our cloud system has to support. For our agile development process, we use standardized templates for the description of use-cases, which are initialized from storyboards and iteratively refined by stakeholders. These use-case templates are subsequently transformed into machine-readable specifications, which allows for generation of REST APIs for our cloud system.
Private Profile
added a research item
Hardware Structure of the Brainstem of UNICARagil
Private Profile
added 2 research items
Automatisiert gesteuerte, mobile Systeme verfügen über eine planende und eine ausführende Instanz, die sowohl integriert als auch unabhängig voneinander vorliegenkönnen. Beide benötigen Informationen über die aktuelle Pose des Systems. Um ein unerwünschtes Systemverhalten zu verhindern, ist es notwendig, dass beide Instanzen identische (konsistente) Informationen über die aktuelle Pose des Systems erhalten. Am Beispiel eines automatisiert gesteuerten Fahrzeugs wird ein Verfahren vorgeschlagen, das ermöglicht, für Planung und Regelung inkonsistente Lokalisierungsdaten zu verwenden. Dazu wird der Offset der ermittelten Posen bestimmt, überwacht und korrigiert. Außerdem werden die Effekte von Sprüngen der Pose im Rahmen der Sensordatenfusion auf die Bewegungsregelung unterdrückt und ein Beitrag zur Selbstwahrnehmung des Systems geleistet.
Stefan Ackermann
added 2 research items
Maschinelle Systeme übernehmen einen immer größer werdenden Anteil der dynamischen Fahraufgabe automatisierter Fahrzeuge. Funktionale Degradationen können die Fähigkeiten dieser Systeme negativ beeinflussen, sodass sie die Fahraufgabe nicht weiter erfüllen können. In diesen Fällen wird bei höher automatisierten Systemen die Fahraufgabe von einer maschinellen Rückfallebene übernommen. Im Rahmen des Forschungsprojekts UNICARagil wird eine modulare und dienstbasierte funktionale Fahrzeugarchitektur entwickelt, für die in diesem Beitrag die Anforderungen und die Systemarchitektur einer geeigneten funktionalen Rückfallebene vorgestellt werden und der weitere Forschungsbedarf hinsichtlich der erforderlichen Fähigkeiten der Teilfunktionen, ihrer gegenseitigen Abhängigkeiten und der Absicherung der Teil- und Gesamtfunktionen erläutert wird.
Timo Woopen
added an update
UNICARagil held its digital half-time event during the last months. This newsletter summarizes the research activities of the past two years. Many posters give an insight on the different research domains and the results that are already available.
 
Timo Woopen
added a research item
This presentation gives an overview on the question, how UNICARagil addresses Equity, Accessibility, Inclusivity and Acceptance in the Development of new Architectures for Automated Vehicles.
Fabian Gies
added a research item
A main task for automated vehicles is a complete and robust environment perception. Especially, an error-free detection and modeling of other traffic participants is of great importance to drive safely in any situation. Therefore, multi-object tracking approaches, based on object detections from raw sensor measurements, are commonly used. However, false object hypothesis can occur due to complex, arbitrary scenarios with a high density of different traffic participants. For that reason, the presented approach introduces a probabilistic model to verify the existence of a track. Therefore, an object verification module is introduced, where the influences of multiple digital map elements on a track's existence are evaluated. Finally, a probabilistic model fuses the various influences and estimates an extended existence probability for every track. In addition, a Bayes Net is implemented as directed graphical model to highlight this work's expandability. The presented approach, reduces the number of false positives, while retaining true positives. Real world data is used to evaluate and highlight benefits of the presented approach, especially in urban scenarios.
Timo Woopen
added a project reference
Torben Stolte
added 2 research items
The complex functional structure of driverless vehicles induces a multitude of potential malfunctions. Established approaches for a systematic hazard identification generate individual potentially hazardous scenarios for each identified malfunction. This leads to inefficiencies in a purely expert-based hazard analysis process, as each of the many scenarios has to be examined individually. In this contribution, we propose an adaptation of the strategy for hazard identification for the development of automated vehicles. Instead of focusing on malfunctions, we base our process on deviations from desired vehicle behavior in selected operational scenarios analyzed in the concept phase. By evaluating externally observable deviations from a desired behavior, we encapsulate individual malfunctions and reduce the amount of generated potentially hazardous scenarios. After introducing our hazard identification strategy, we illustrate its application on one of the operational scenarios used in the research project UNICARagil.
Bastian Lampe
added a research item
Accurate environment perception is essential for automated vehicles. Since occlusions and inaccuracies regularly occur, the exchange and combination of perception data of multiple vehicles seems promising. This paper describes a method to combine perception data of automated and connected vehicles in the form of evidential Dynamic Occupany Grid Maps (DOGMas) in a cloud-based system. This system is called the Collective Environment Model and is part of the cloud system developed in the project UNICARagil. The presented concept extends existing approaches that fuse evidential grid maps representing static environments of a single vehicle to evidential grid maps computed by multiple vehicles in dynamic environments. The developed fusion process additionally incorporates self-reported data provided by connected vehicles instead of only relying on perception data. We show that the uncertainty in a DOGMa described by Shannon entropy as well as the uncertainty described by a non-specificity measure can be reduced. This enables automated and connected vehicles to behave in ways not before possible due to unknown but relevant information about the environment.
Bastian Lampe
added a research item
Future Cooperative Intelligent Transport Systems (C-ITS) require an integrated functional framework that provides cloud-based services to automated vehicles and other traffic participants. The goal is to process, store and share relevant information in order to continually assure and improve the efficiency, safety and comfort of the C-ITS. This paper introduces a first conceptual hypothesis for such a framework that is developed in the project UNICARagil, funded by the German Federal Ministry for Education and Research (BMBF). Three main components of this framework, the Collective Environment Model, the Collective Memory and the Collective Behavior, are presented. Open challenges associated with current and future technology are discussed.
Timo Woopen
added a research item
In the context of autonomous driving, additional possibilities for passenger occupation arise. Parallel to this, vehicle concepts especially in the field of autonomous driving provide more degrees of freedom to apply novel interior concepts and seating configurations. To derive user requirements early on in the development process in this new field, three user studies in two research projects were conducted. As autonomous driving technologies take the focus away from the driving task, interior design in general and seating can be modified to allow different activities other than driving. A user study in the research project UNICARagil focused on the interior design and seat arrangement of a highly automated shuttle concept. By bringing users close to the use case of riding in an autonomous shuttle in a workshop situation, an early user integration was achieved. In this vein, more degrees of freedom in seat arrangement lead to a need to review existing restraint systems regarding their applicability to the autonomous context. Moreover, two user studies were conducted with the EU H2020 project OSCCAR in order to provide input to a matrix for selecting the most relevant test cases. The goal is to derive and design novel safety principles for advanced, safe, and comfortable sitting postures. While one study focused on preferred seat rotations, the second study examined the impact of different user scenarios on preferred sitting postures in an artificial autonomous driving situation. Results provide insights into the perception of seat rotations and detailed sitting postures that are most likely to be obtained by occupants in future use cases. The results of the user studies of the two projects independently revealed valuable insights, which will help to derive requirements towards occupant safety in future vehicle concepts.
Nils Rexin
added a research item
Environment modeling in autonomous driving is realized by two fundamental approaches, grid-based and feature-based approach. Both methods interpret the environment differently and show some situation-dependent beneficial realizations. In order to use the advantages of both methods, a combination makes sense. This work presents a fusion, which establishes an association between the representations of environment modeling and then decoupled from this performs a fusion of the information. Thus, there is no need to adapt the environment models. The developed fusion generates new hypotheses, which are closer to reality than a representation alone. This algorithm itself does not use object model assumptions, in effect this fusion can be applied to different object hypotheses. In addition, this combination allows the objects to be tracked over a longer period of time. This is evaluated with a quantitative evaluation on real sequences in real-time.
Andreas Danzer
added a research item
For many automated driving functions, a highly accurate perception of the vehicle environment is a crucial prerequisite. Modern high-resolution radar sensors generate multiple radar targets per object, which makes these sensors particularly suitable for the 2D object detection task. This work presents an approach to detect object hypotheses solely depending on sparse radar data using PointNets. In literature, only methods are presented so far which perform either object classification or bounding box estimation for objects. In contrast, this method facilitates a classification together with a bounding box estimation of objects using a single radar sensor. To this end, PointNets are adjusted for radar data performing 2D object classification with segmentation, and 2D bounding box regression in order to estimate an amodal bounding box. The algorithm is evaluated using an automatically created dataset which consist of various realistic driving maneuvers. The results show the great potential of object detection in high-resolution radar data using PointNets.
Timo Woopen
added a project goal
Autonomous electric vehicles will play a key role in mastering the challenges relating the increasing demand for mobility and the ongoing urbanization. They provide the basis for sustainable and smart road traffic, innovative mobility and transport concepts as well as improved road safety and urban quality of life.
In the UNICARagil project, fully automated, driverless, electric vehicles will be developed based on the latest results of research in automated and connected driving as well as electric mobility. The foundation for this will be a modular and scalable vehicle concept, which is consisting of a utility and a drive unit. It can be adapted to a variety of applications in logistics and passenger transport. In particular, these applications can be utilized by driverless, locally emission-free vehicles. Key component of the research and development work is the functional vehicle architecture, which is connected to the cloud, the road infrastructure and a sensor drone. Further key areas are the development of generic sensor modules for environment detection and a flexible expandable and updateable software and hardware architecture along with highly dynamic wheel hub drives. These enable the vehicle to perform completely new forms of movement. The finalization of the project consists of a demonstration in four different applications on test fields in Germany.
For more information visit: www.unicaragil.de
 
Timo Woopen
added 2 research items
This paper introduces UNICARagil, a collaborative project carried out by a consortium of seven German universities and six industrial partners, with funding provided by the Federal Ministry of Education and Research of Germany. In the scope of this project, disruptive modular structures for agile, automated vehicle concepts are researched and developed. Four prototype vehicles of different characteristics based on the same modular platform are going to be build up over a period of four years. The four fully automated and driverless vehicles demonstrate disruptive architectures in hardware and software, as well as disruptive concepts in safety, security, verification and validation. This paper outlines the most important research questions underlying the project.
Themen der Studie: Aktuelles Mobilitätsverhalten sowie Einstellungen gegenüber autonomen Fahrzeugen generell, automoatisierten Shuttes / People Movern (AUTOshuttle), automatisierten Taxen (AUTOtaxi), automatisierten Fahrzeugen im Privatbesitz (AUTOelfe) sowie gegenüber automatisierten Lieferfahrzeugen / Last-Mile (AUTOliefer)