Τόσο η BMW όσο και η Opel εργάζονται πάνω στο project UR:BAN (User oriented assistance systems and network management) το οποίο έχει ως στόχο να βελτιώσει την ασφάλεια και να μειώσει τα ατυχήματα των αυτοκινήτων μέσα από τρεις βασικούς τομείς, την γνωστική βοήθεια, το συνδεδεμένο σύστημα κυκλοφορίας και τον ανθρώπινο παράγοντα.

Για τον πρώτο τομέα η Opel και η BMW αναπτύσσουν ένα νέο προηγμένο σύστημα υποστήριξης του οδηγού που χρησιμοποιεί μια κάμερα, ένα ραντάρ όπως και τροποποιημένα συστήματα διεύθυνσης και πέδησης. Όταν το σύστημα εντοπίσει αδυναμία εκ μέρους του οδηγού να αποφύγει ένα επικείμενο ατύχημα, αυτό λαμβάνει τα κατάλληλα μέτρα για να κατευθύνει το αυτοκίνητο μακριά από το αντικείμενο που θα χτυπήσει.

Η Opel εργάζεται επίσης και στο σύστημα επικοινωνίας Car-to-X, ενώ αναπτύσσει και έναν αλγόριθμο που μπορεί να ανιχνεύσει τις προθέσεις του οδηγού με τη χρήση διαφόρων αισθητήρων, έτσι ώστε να τον ενημερώσει ή όχι για ένα επικείμενο ατύχημα.

Η BMW ετοιμάζει επίσης ένα νέο σύστημα που αναλύει διάφορες καταστάσεις και τη συμπεριφορά των πεζών, προκειμένου να αξιολογήσει αν υπάρχει πιθανότητα αυτοί να έχουν οποιοδήποτε ατύχημα με το αυτοκίνητο τους. Το τελικό σύστημα θα ελέγχει περιμετρικά το αυτοκίνητο για αντικείμενα ή πεζούς, έτσι ώστε να μειώνει τις πιθανότητες κάποιου ατυχήματος. Η γερμανική εταιρία εργάζεται επίσης και σε ένα σύστημα το οποίο θα επιτρέπει στα αυτοκίνητα της να “καταλαβαίνουν” το πότε ένα φανάρι θα ανάψει πράσινο ή κόκκινο, έτσι ώστε να μειωθούν περαιτέρω οι εκπομπές CO2 τους. Αντίστοιχο σύστημα ετοιμάζει και η Audi.

[learn_more caption=”Δελτίο Τύπου”]

Opel and UR:BAN: Making urban driving safer and more efficient

  • Cognitive assistance systems increase safety
  • Networking of vehicles and infrastructure in urban traffic
  • Urban-appropriate human-machine interaction

Rüsselsheim/Braunschweig. Driving a car in towns or cities means a coexistence of various road users and means of transport. It also means coping with a complex traffic situation and a high density of activities.

Expert teams at Opel developing innovative assistance systems to increase efficiency and safety in urban transport are participating in the UR:BAN research project (user oriented assistance systems and network management). The goal of the partly German government funded project is to provide drivers of passenger cars and commercial vehicles with forward-looking support, tailored and customized to driving in urban traffic. Thirty-one partners from the automotive industry, electronics and software companies, together with research institutes and local authorities, are developing intelligent assistance and cooperative traffic management systems.

The results and findings obtained so far were delivered today in the UR:BAN mid-term presentation at the Deutsche Zentrum für Luft – und Raumfahrt e.V. (DLR – German Center for Air and Space Travel) in Braunschweig, Germany.

The on-going, until 2016, UR:BAN research project, is divided into three pillars: “Cognitive Assistance”, “Networked Traffic System”, and “Human Factors in Traffic”. “Opel is a partner in all three project pillars”, explains Michael F. Ableson, Member of the Management Board and GM Europe Vice President, Engineering. “The focus is always on the human being. Teams of engineers, physicists and traffic psychologists are exploring assistance in difficult situations, the intelligent networking of vehicles and infrastructure, driver behavior, and the appropriate human-machine interaction for urban driving.”

Cognitive Assistance – collision avoidance by evading and braking

Especially in urban traffic, difficult situations can occur where the driver may not react in time. This is where the sub-project “collision avoidance by steering and braking” comes in. The Opel team is developing an advanced driver assistance system that takes advantage of the extra road space created by steering intervention to implement an additional situation-specific evasion. This system should help avoid collisions with vehicles and pedestrians in inner-city traffic.

At the mid-term presentation, Opel will show a demonstration vehicle equipped with advanced camera and radar, complemented by modified braking and steering systems, which allow an intervention in the control over the vehicle. Consideration of the driver’s reaction represents an important criterion for the optimization of the intervention strategy. In addition, Opel will integrate the latest findings of human-machine interaction and driver intention detection in the demonstration vehicle.

Networked Traffic System – smart intersection

Information sent to the vehicle via Wi-Fi from the traffic management infrastructure and other vehicles can help to generate recommendations for driving at intersections, which an intuitive human-machine-interaction can communicate to the driver. This could enable the driver to approach the intersection comfortably, safely and energy efficiently and preferably cross without stopping. This optimization and the further development of car-to-X technology specifically for driving in an urban environment is presented within the framework of UR:BAN.

In this part of the project, Opel is building on many years of experience in the field of car-to-X communication, as shown in projects such as the successfully completed SIM-TD field test. A networked traffic system and cooperative driving will enable the future step from the mere exchange of information to the joint action of all relevant actors.

Human Factors in Traffic – the appropriate HMI for urban driving

Within UR:BAN, an Opel team is working on improving technical systems. However, the information gained by the new technical systems can only improve driving and safety when drivers also know how to deal with the newly won information and how to react correctly. The focus of the sub-project “human-machine interaction” (HMI) therefore is the way humans experience the behavior of driver assistance systems. In the foreground is interdisciplinary teamwork, with psychologists, engineers and physicists collaborating on the development and implementation of new technology elements to communicate with the driver.

Important questions addressed here include what information content do drivers need in order to follow a driving strategy; or what forms of representation motivate drivers to use a system; and how to avoid driver distraction. The experts test the influence of the assistance systems in various simulators and in real traffic, under controlled conditions.

The evaluation of the objective and subjective data provides the basis for the evaluation of system design and layout. Assistance systems that optimally support the user in his driving behavior are then integrated, in close cooperation with the other sub-projects.

Human Factors in Traffic – behavior prediction and intention recognition

Understanding driver behavior can help to optimize the warning strategy of the man-machine interface of assistance systems. Opel’s target in the sub-project “behavior prediction and intention recognition” is therefore to develop an algorithm to detect driver intention using standard vehicle sensors. This allows a prediction of driver behavior in difficult driving situations.

The algorithm should identify, at an early stage, future avoidance maneuvers or emergency braking situations. Extensive testing and test data records show that, before maneuvers begin, specific behavior patterns and characteristics in the transverse and longitudinal guidance of the vehicle show up with all drivers. More than 50 volunteers completed a specific drive route under controlled conditions, where they encountered unexpected situations and the resultant maneuvers.

Based on the tests, the Opel team expects to predict whether the driver can mitigate an emergency situation through steering or braking. This information can help to adapt appropriate warning and intervention strategies beneficial for the sub-project “collision avoidance by evading and braking”. If the system detects that the driver alone can mitigate a difficult situation, it can delay or suppress a driver warning.

UR:BAN research initiative. Specialists from the BMW Group are developing intelligent driver assistance and traffic management systems for enhanced safety, comfort and efficiency in tomorrow’s cities.

Braunschweig. Increasing urbanisation means that city traffic networks are reaching their capacity limits ever more frequently. Driving in urban areas means having to deal with complex traffic situations involving many different road users, and there is a growing risk of congestion and accidents in our cities. Traffic systems need to be able to handle the strain, however, if the future of unrestricted mobility is to be preserved.

As a world-leading manufacturer of premium automobiles, the BMW Group is playing an active role in shaping personal and sustainable mobility. Specialists from the BMW Group are therefore also involved in the research initiative entitled UR:BAN (German acronym standing for Urban Space: User-oriented assistance systems and network management) and working on the development of cooperative and intelligent driver assistance and traffic management systems. “This will enable us to further increase safety, efficiency and comfort in urban areas to significant effect,” explains Dr Christoph Grote, Managing Director of BMW Forschung und Technik GmbH. One of the main focal points is humans and the many different roles they play in the traffic system – be it as a motorist, motorcyclist, cyclist, pedestrian or traffic planner. For this, the research initiative has brought together 31 partners from the automotive industry and its suppliers, the fields of electronics and software, as well as from research institutes and cities. Running from 2012 until 2016 and funded by the German Federal Ministry for Economic Affairs and Energy (BMWi), the initiative comprises three separate projects: “Cognitive Assistance”, “Networked Traffic System” and “Human Factors in Traffic”. The BMW Group is making key contributions to all three projects.

The interim results were presented today at the UR:BAN midterm event held at the German Aerospace Centre (DLR) in Braunschweig.

Increased safety with “Cognitive Assistance”.

Today’s driver assistance systems are designed first and foremost to make driving on motorways safer and more comfortable. There is a whole new set of challenges to be tackled in city traffic, however, where motorists also have to watch out for cyclists and pedestrians. The methods for detecting situations and dangers and for aiding drivers’ braking and steering reactions that already work successfully on motorways need to be adapted accordingly.

As part of the sub-project “Protection of Vulnerable Road Users”, BMW Forschung und Technik GmbH is developing a driver assistance system to help protect pedestrians: the system analyses the situation and the pedestrian’s behaviour 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 recognise 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 modelling for urban scenarios to be used by multiple driver assistance systems.

Energy-efficient driving with a “Networked Traffic System”.

Urban traffic today holds relatively high potential for further improving traffic efficiency and thereby lowering CO2 emissions. In the “Urban Roads” sub-project, BMW AG has therefore joined forces with other project partners to develop 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 centres 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.

Anticipatory and comfortable driving with “Human Factors in Traffic”.

The “Humans Factors in Traffic” project is creating innovative new display and control concepts to enable vehicles to turn into “active helpers” in the event of danger.

In the “Controllability” sub-project, BMW AG and BMW Forschung und Technik GmbH have united with higher education partners and research institutes to devise a standardised 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 “Behaviour Prediction and Intention Detection” sub-project, meanwhile, centres 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.