Vehicles for the Demonstration Day

Below you can find the demonstrators for our demonstration day at the Aldenhoven Testing Center!

High-Speed-Track / Oval

JKU-ITS: Haptic Guidance on Driving Performance

Description: Dynamic with test drive opportunity

  • Effect on driving performance after a take-over request by a haptic guidance system
  • Participants will drive the JKU-ITS research vehicle while executing a Non Driving Related Task (NDRT)
  • AR Glasses will show a sudden event on the road
  • Participants will be required to gain control of the vehicle

DLR: Maneuver Cooperation in Emergency Situations

Description: Dynamic with test drive opportunity

  • Two automated vehicles drive on adjacent lanes with a similar velocity (ca. 50 km/h)
  • A virtual obstacle is presented to first vehicle so that it switches to emergency braking
  • Cooperative lane change maneuver is negotiated via vehicle-to-vehicle communication with the second vehicle
  • Second vehicle accepts the lane change request and brakes
  • First vehicle is now able to change lanes

FH Aachen & Hyundai Motor: Take over Request and Minimum Risk Maneuver

Description: Dynamic with test drive opportunity

  • This demonstrator shows how an automated vehicle will react in case that the driver does not respond to a Takeover Request (ToR)
  • Test vehicle transitions into automated mode after starting in manual driving mode
  • The test vehicle will be informed about an upcoming danger zone and warns the driver to take control over the vehicle
  • The driver does not take control so that the test vehicle must perform a minimum risk maneuver (MRM) automatically
  • The test vehicle slows down and changes lanes onto the emergency lane and stops

FEV Europe GmbH: Traffic Jam Chauffeur

Description: Dynamic with test drive opportunity

  • The demonstrator vehicle (SVD) follows a target vehicle which
    drives at 60 km/h on the most right lane
  • Target Vehicle simulates a traffic jam with stop & go through the ATC
  • The target vehicle cuts out shortly behing a stationary vehicle close
    to the middle of a straight section
  • SVD will stop in time behind the new target

Oval / Vehicle Dynamic Track

mm-lab GmbH: Ground Safety System for Motorcycles with Maneuver Detection

Description: Dynamic with test drive opportunity

  • Research and Development cooperation project “Zentrales
    Innovationsprogramm Mittelstand (ZIM)” funded by the Bundesministerium für
    Wirtschaft und Energie (BMWi)
  • Selected driving maneuvers will be demonstrated by a motorcyclist
  • Exhibition of the On-Board Unit for Motorcycles supported by video sequences
  • Detection of maneuvers and crashes of motorbikes
  • HMI System tailored to motorcycles
  • Proving Ground Management System (PGMS) already used at the Proving Ground of Aldenhoven

Urban Track

dSpace: Data Driven Development Solutions for Autonomous driving

Description: Dynamic with test drive opportunity

  • Showcase of the dSPACE Car used for data collection
  • Development of AI Algorithm
  • Car is equipped with and advanced data logger (AUTERA)
  • A complete sensor setup generates around 21 Gbit/s of sensor data

Autonomous Driving in Urban Scenarios

Description: Dynamic with test drive opportunity

  • Autonomous driving vehicle (“Joy”) equipped with different sensors
  • Localization in high definition maps based in fusion between differential GPS and visual localization
  • Obstacle detection based on gridmap able to generate mid time prediction for different agents
  • Planning in high definition maps, avoiding unexpected obstacles and optimizing the passenger’s comfort
  • Interaction with the enviroment

fka GmbH: Automated Driving Function

Description: Dynamic with test drive opportunity

  • Demonstration of Automated Driving Function for Urban Scenarios including V2X
  • Communications with traffic lights

ika RWTH Aachen: UNICARagil

Description: Dynamic without test drive opportunity

  • Presentation of disruptive modular architectures for self-driving vehicles
  • Demonstration using full-sized vehicle prototypes
  • Driverless vehicle under supervision of a control room operator
  • The control room operator takes control when the vehicle encounters a situation that it cannot resolve on its own
Copyright: Patrick Pintscher 

ika RWTH Aachen: EMMI-Project: Wizard-of-Oz Vehicle - An immersive, "automated" test drive

Description: Dynamic with test drive opportunity

  • Simulation of automated functions
  • Corresponding functions are not technically realized, but are realistically simulated by a human (wizard) without the user (test person) having any knowledge of the wizard’s presence or actions
  • Test vehicle represents an outstanding instrument for investigating the interaction between humans and automated systems where it is relevant, i.e. in real road traffic

Urban Track/ Static

IMECH Uni-Duisburg-Essen: Charging Robot

Description: Static

  • The charging robot is a collaborative robot syste
  • Functional demonstrator that implements the automated insertion and removal of a charging cable in an electric vehicle
  • Robot is equipped with appropriate tools to grab the charging cable from the wallbox and plug it into the vehicle
  • System has a camera system to detect the charging socket on the vehicle


TH Aschaffenburg: Cooperative Intention Detection

Description: Static

  • Cooperation between car and roadside unit (RSU)
  • Car and RSU equipped with a stereo camera and able to communicate using a wireless network
  • Individual tracking and forecasting by single agents (car and RSU)
  • Resolvement of occlusion by the RSU
  • Reducing uncertainty in the pedestrian’s position through cooperation
  • Probabilistic forecast of pedestrian positions

University of Texas at Dallas: Data Discovery and Targeted Learning

Description: Static

  • In case of a rare event during autonomous driving the deep learning
    models perfom poorly
  • Demonstration on data discovery and targeted learning
  • Use of data discovery in a large pool of unlabeled data in case of a rare event
  • Search of semantically similar samples from unlabeled data set
  • Targeted learning is the mining of these samples in order to increase performance

Robosense: RoboSense RS-Reference

Description: Static

  • Robosense RS-Reference is a tool-chain to evaluate all sensor’s performance in the vehicle, such as LiDAR, millimeter wave, camera, etc.
  • Can assist OEM, Tier1, and research institutes to perform highly efficient tests and validation and accelerate mass production of ADAS and AD applications.

Call for Paper