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UR:BAN research project gives mid-term update on advanced driver assistance systems, connected vehicles

24 May 2014

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Mid-term review in the UR:BAN research project. Click to enlarge.

Partners in Germany’s UR:BAN research consortium recently gave a mid-term status report on the three major component projects in an event at the German Aerospace Centre (DLR) in Braunschweig, Germany. UR:BAN—User oriented assistance systems and network management—is developing advanced driver assistance and traffic management systems for cities. The focus is on the human element in all aspects of mobility and traffic.

Consortium members in the four-year project include: Opel; Audi; BMW Group; BMW Forschung und Technik; Bundesanstalt für Straßenwesen; Continental; Daimler; Deutsches Zentrum für Luft- und Raumfahrt; Fraunhofer-Institut für Arbeitswirtschaft u. Organisation; GEVAS Software; Heusch/Boesefeldt; ifak Magdeburg; Hochschule für Technik und Wirtschaft des Saarlandes; MAN Truck & Bus; PTV Group; Robert Bosch; RWTH Aachen; Landeshauptstadt Düsseldorf; Stadt Kassel; Technische Universität Braunschweig; Technische Universität Chemnitz; Technische Universität München; TomTom Development Germany; TRANSVER; Universität der Bundeswehr München; Universität Duisburg-Essen; Universität Kassel; Universität Würzburg; and Volkswagen AG. Numerous additional university and research institutes as well as small and mid-size companies are also participating in the projects as subcontractors.

The research objectives are being pursued in three main thematic target areas: Cognitive Assistance; Networked Traffic System; and Human Factors in Traffic.

Cognitive Assistance. The focus of the project “Cognitive Assistance” is improvement of safety in urban traffic in complex situations such as intersections with pedestrian movements and bicycle traffic; narrow or obstructed streets; conflicts with opposing traffic; and lane changing.

New technologies support panoramic sensing capabilities, allowing an evaluation of space available for possible lateral avoidance (swerving) maneuvers. Thus, collisions can be avoided not only by automatic braking, but also by swerving as required. All these challenges are supported by studies of legal issues and effectiveness evaluation.

Partners in this area are exploring five sub-projects:

  • Measurement and modeling of vehicle surroundings. Robust perception, modeling, and interpretation of complex traffic movements as a pre-requisite for use of safety relevant advanced driver assistance functions in urban areas.

  • Protection of vulnerable road users. Development of cognitive and user-friendly driver assistance systems for effective, anticipatory protection of vulnerable road users in urban areas.

  • Collision avoidance by braking and swerving. Cognitive assistance systems in urban areas to support accident avoidance maneuvers such as braking and swerving.

  • Safe lateral and longitudinal vehicle control in urban areas. Decrease burden on driver in narrow urban streets by timely information on available lateral clearance and by continuous lateral and longitudinal control support.

  • Areas of effective application, legal issues. Comprehensive assessment of cognitive assistance systems for urban areas concerning potential, effectiveness, and legal requirements.

Partners on this theme, which is funded with €40 million, include Opel; Audi; BMW Forschung und Technik GmbH; Bundesanstalt für Straßenwesen; Continental; Daimler; MAN Truck & Bus; Robert Bosch GmbH; and Volkswagen. This theme is led by Daimler.

Daimler in cooperation with research partners was able to enhance the performance of surroundings sensing technology in complex urban situations. The researchers now merge the results obtained by stereo cameras and high-performance radar systems to form 360-degree models of the surroundings with a previously unachieved level of detail. The process involves transitioning the some 500,000 three-dimensional pixels surveyed by the stereo camera into an extremely compact representation—the so-called stixel world.

This not only contains the position, type, size and motion of other road users, but also stationary obstacles such as parked cars or curbs. As a result, the unoccupied space for driving is identified reliably and described precisely. The advance in sensing the surroundings is crucial for urban traffic safety. Because the generated data are of a cross-application nature, they will be able to be used by a large number of widely different systems in the future.This saves costs and resources, thus resulting in a fast propagation of the assistance systems.

As other examples of work, BMW Forschung und Technik GmbH is developing a driver assistance system to help protect pedestrians: the system analyses the situation and the pedestrian’s behavior to assess whether there is a risk of collision with the vehicle. Accidents with pedestrians can be avoided by braking, steering or a combination of the two. In a BMW 5 Series research vehicle it is already possible to recognize detailed features of a pedestrian—i.e. the head and upper part of the body—and to classify the direction in which the pedestrian is moving.

If driver assistance systems that effectively prevent accidents are to work in an urban environment, they need a reliable and complete “picture” of their surroundings and must also be able to correctly interpret complex situations involving many different protagonists and boundary conditions. BMW Forschung und Technik GmbH is therefore working on the development of powerful assessment algorithms for fusing data and evaluating situations in a cross-disciplinary sub-project designated “Measurement and Modeling of the Environment”.

At the midterm event, object detection was demonstrated along with free space or generic object detection using grids. The aim is to develop a system of 360° environment modeling for urban scenarios to be used by multiple driver assistance systems.

Volkswagen noted that it is developing three assistance systems as part of the Cognitive Assistance project; these systems offer safe longitudinal and transverse guidance to help the driver realize a mode of driving in urban traffic that is anticipatory, safe and relaxed.

For example, the “Lane changing assistant” assists with active interventions in longitudinal and transverse guidance when switching driving lanes in dense traffic on urban access and arterial roads. During a maneuver, the system observes surrounding vehicles with all-round 360° monitoring and assists the driver in selecting an open space in the destination lane and approaching it by indicators and steering movements.

The “Bottleneck assistant” helps the driver if there are vehicles that are parking which are partially or completely blocking the driving lane, for example, so that only a very narrow passage is possible. An advanced development of the Lane Assist system that is already in production cars detects obstacles in the car’s own driving lane as well as in opposing traffic with a sensor that visualises the surroundings in 3D. The system checks whether a safe path exists, and it assists by active steering intervention when driving past the obstacle while maintaining a safe distance.

The “Emergency braking assistant” reduces or even avoids imminent collisions in the urban environment by situation-specific braking and steering interventions.

Networked Traffic Systems. The focus of this sub-project is the development of applications for energy- and traffic-efficient driving in urban areas. The aim is to find new approaches to solution of the following scientific and technical issues:

  • Measurement of complex urban situations for information and assistance systems supporting energy and traffic efficient, comfortable and safe mobility;

  • Determination of requirements posed by vehicles with alternative power sources and optimization with respect to these special requirements;

  • Infrastructure control and networking with innovative vehicle concepts and systems; and

  • Complex network control with aim of satisfying new demands on mobility in the post-fossil fuel era.

Four sub-projects have been defined for prototype implementations of these aims. Three of these are closely coordinated and deal with various aspects of urban traffic: large-scale traffic management in “Regional Networks”; forward-looking energy and traffic efficient driving in “Urban Streets”; and adaptive control of “Smart Intersection”, with support of the driver in the vehicle.

The fourth sub-project, “Cooperative Infrastructure”, brings together the traffic-based sub-projects. It aims to implement the traffic-based solutions in additional communities and regions by setting up a roadmap for introduction of co- operative systems. Assessment of the combined effect of the applications on traffic and extrapolation of the results will provide estimates of the potential of these new applications.

Parters in this €23-million theme are Opel; BMW; Continental; Daimler; DLR; GEVAS; HTW Saarland; ifak Magdeburg; Landeshauptstadt Düsseldorf; MAN Truck & Bus; PTV AG (project leader); Stadt Kassel; TU Braunschweig; TU München; TomTom; Transver; Uni Duisburg-Essen; Uni Kassel; and Volkswagen AG.

As an example of work, project partners are developing a “Green Coordination and Deceleration Assistant.” This makes use of predictive information on the switching times of traffic lights and the local traffic situation ahead of junctions to unlock previously unused potential for increasing traffic efficiency while as reducing fuel and noise emissions at traffic light-controlled junctions.

Consequently, directing the traffic flows in this way also opens up the possibility of making the most of the different drive systems in today’s cars, such as electric and hybrid drives.

The Green Coordination and Deceleration Assistant has been implemented in a BMW X5 and a BMW 4 series test vehicle. The project thereby showcases how communications could be channelled from the traffic infrastructure to vehicles via traffic control centers and the Mobility Data Marketplace (MDM). The first field tests are due to begin this year at the test facilities in Düsseldorf and Kassel. The results will be incorporated directly into an impact analysis to confirm the gain in efficiency.

Volkswagen Group Research is developing the “Intersection Pilot”, with the goal of improving traffic efficiency in the vicinity of intersections based on Car-to-X communication. This assistance function informs the driver locally about traffic nodes located ahead. It supports the driver with optimal driving manoeuvres and simultaneously enables improved traffic light switching by routing vehicle information.

The “Merge and start assistant” recommends an optimal speed right at the entrance to the intersection to enable driving through on a green traffic light phase without having to stop. If a stop is unavoidable, this maneuver is also made as efficient and convenient as possible by choosing a good stopping point for a “flying start”, which significantly reduces the start-up losses that are typical today. This lets more vehicles pass during the short green phase, which benefits all traffic participants.

An “Emergency vehicle assistant” informs all traffic participants directly of approaching emergency vehicles. It optimizes traffic light switching and ensures more rapid passage of the emergency vehicle that is safer for all vehicles.

Human Factors in Traffic. This project focuses specifically on the user of future assistance and information systems. In the case of advanced driver assistance systems for urban areas, the primary emphasis is safety rather than just comfort. By means of an individualized design of the driver-vehicle interaction for different drivers, the aim is to achieve low-stress, efficient, and safe driving in urban traffic.

Four sub-projects explore the issues:

  • Human-machine-Interaction (HMI). How, and with which technologies, should the HMI concept be designed for urban traffic driving conditions?

  • Intention detection and behavior prediction. –How can the vehicle detect the driver’s intentions and react appropriately according to the driver’s needs?

  • Simulation. How can pedestrians and cyclists with their high density in urban traffic be appropriately modeled in driving and traffic simulation?

  • Controllability. What measures must be taken to ensure that complex assistance systems remain controllable for the driver in urban traffic?

Parters in the €17-million project are: Opel; Audi; BMW; BMW Forschung und Technik GmbH; Bundesanstalt für Straßenwesen; Daimler; DLR; Fraunhofer IAO; MAN Truck & Bus; PTV AG; Robert Bosch; RWTH Aachen; TU Braunschweig; TU Chemnitz; TU München (project leader); Universität der Bundeswehr München; Uni Würzburg; and Volkswagen AG.

As examples of work, BMW AG and BMW Forschung und Technik GmbH have united with higher education partners and research institutes to devise a standardized and methodical basis for an efficient and valid form of verifying the controllability of functions and HMI concepts, with the focus on situations where time is a critical factor.

The “Behavior Prediction and Intention Detection” sub-project, meanwhile, centers on the development of methods for detecting the driver’s intentions at the earliest stage possible in order to align the assistance system’s suggestions with what the driver plans to do.

May 24, 2014 in Autonomous driving, Connected vehicles, Driver Assistance Systems, Intelligent Transportation Systems (ITS), Urban mobility, V2X | Permalink | Comments (2) | TrackBack (0)

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Comments

We have a vintage Vespa from Italy. It is a work of art and has not been driven in over 15 years (kept only for artistic purposes).
Honda has a simple answer, the SH125i and SH150i micro hybrid 4 stroke, gets 47.4 km/liter of gasoline. Honda the world's largest scooter manufacturer gave up on 2 strokes years ago as well.

Driverless vehicles will soon be one of the most important safety option on electrified vehicles as soon as 2020 or shortly thereafter.

Road accidents, fatalities and injuries will drop quickly.

Pedestrians will be safe again.

Insurances, lawyers and repair shops will raise hell against it.

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