In the fourth stage of the Proreta
research project, technology company Continental and TU Darmstadt have
developed a machine-learning vehicle system designed to support drivers in
urban traffic and have installed it in a prototype. Data from radar sensors
helps drivers to assess the traffic situation when turning left, entering a
roundabout or at right-before-left intersections.
Machine-learning has played a
key role in the three-and-a-half-year research project. Algorithms create an
always up-to-date driver profile based on a range of vehicle data, allowing
them to adapt the driving maneuver recommendations given by the City Assistant
System in line with the driving style.
The task of the Proreta project
was highly complex. “The goal of collaboration between our institutes and
Continental during the course of Proreta 4 was to use adaptive systems as a
mean of developing solutions that have not yet been addressed due to the lack
of adaptability. The results of our work will help to further increase safety
both within the vehicle and for other road users,” says Professor Hermann
Winner, Director of the Institute of Automotive Engineering at TU Darmstadt.
Acting like a good passenger,
an advanced driver assistance system must analyse the driver’s style of driving
and, in turn, their subjective sense of safety or risk so that, in complex
traffic situations, it can give the driver recommendations that are also met
with a high degree of acceptance. The driving profile is created quickly and
accurately on the basis of a machine-learning process. For this, a range of
data recorded during journeys is evaluated. Acceleration, yaw rates, braking
and lateral acceleration in particular give the algorithm an idea of what type
of driver is behind the wheel.
Extensive test drives with
testers showed that the algorithms used in the City Assistant System allow
conclusions to be drawn about the current driving style within three to five
driving maneuvers. The system can therefore assign the driver to one or more
clusters of driving profiles, meaning that the City Assistant System can then
offer highly personalized driving recommendations.
Machine-learned algorithms are
becoming increasingly common in vehicle systems. While the number of vehicle
system units utilising artificial intelligence stood at 7 million in 2015, this
figure is expected to increase to 225 million by 2025. Efficient
machine-learned algorithms are mostly highly complex models that, in their raw
form and like a black box, either are hard to interpret or cannot be
interpreted at all by humans. This poses particular challenges for ensuring the
reliability of advanced driver assistance systems, which is why a safety
approval strategy has been developed as part of the algorithm selection process
for advanced driver assistance systems. During the course of Proreta 4, various
methods for reducing the number of test cases for learned algorithms were
identified; these will now be subject to further research.
The City Assistant System detects if a gap in the traffic is big enough
“The driver has to develop
confidence in the City Assistant System and its recommendations. Trust is the
basis for the acceptance of advanced driver assistance systems, which in turn
are an essential component of accident-free driving,” says Ralph Lauxmann, Head
of Systems & Technology at Continental’s Chassis & Safety
division. Based on the driving profile,
the system monitors the time windows for driving recommendations – for example,
with the left-turn assistant. This determines how big the gaps in the oncoming
traffic are for a left turn based on data about the vehicle’s own position as
well as the speed of and distance between oncoming vehicles. The task of object
detection is carried out by ready-for-production long- and short-range radars
installed on the sides of a vehicle. These are already in use in many assistance
systems today, such as Adaptive Cruise Control or Blind Spot Detection.
The driver does not require
assistance when the gaps in the oncoming traffic are extremely large, only when
the necessary time window for safe turning is critical or it is difficult for
the driver to estimate it accurately. This may be the case at night or in poor
visibility as well as with inexperienced or elderly drivers. In heavy traffic,
the City Assistant System reduces the stress of finding gaps and informs the
driver when a sufficiently large gap is approaching. Test drives conducted
during the course of PRORETA 4 identified a time window of between five and
seven seconds, during which the system can assist with recommendations. The
lower value with smaller gaps in oncoming traffic applies to a more dynamic
style of driving, while the upper value applies to extremely defensive drivers.
In both cases, however, it is guaranteed that the driver can complete the turn
The same principle applies to
the second application: entering a roundabout. Here, too, the system uses the
vehicle and environment sensors to determine whether a gap in traffic is large
enough and whether it makes sense, in view of the driver profile, to recommend
that the driver enters the roundabout or waits for a larger gap.
The driving recommendation can
be given in different ways. “Assistance systems whose warnings are not
perceived as useful are often viewed by motorists as annoying and are even
ignored or switched off. This is why we are supporting the approach of an
adaptive advanced driver assistance system featuring a special interaction
concept. Visual, acoustic and haptic signals display the recommendations for
the driver as intuitively as possible,” says Dr. Karsten Michels, Head of
Systems & Technology at Continental’s Interior division. Most obvious is
the visual display with a big green or red arrow, but it would also be possible
to configure a vibration in the seat edge or other haptic signals.
The interior camera detects whether the driver is aware of the traffic
Another complex task for the
City Assistant System is dealing with right-before-left intersections. Here,
the system first recognises from a map, GPS and self-determined location data
that the driver is approaching such a junction. With the help of the interior
camera, the system analyses whether the driver has detected incoming traffic
that is to be given priority. The system checks whether the driver has actually
turned their head to the right at the intersection and registered the other
road user; this process of registering other road users takes 250 to 500
milliseconds. In more dangerous situations, the system can alert the driver
through signals. The system can also establish whether the driver has acted
correctly and communicate its verdict to the driver. In a version ready for
series production, the City Assistant System could also feature an emergency
brake function for the applications described.
Accurate position detection thanks to self-determined landmarks
The more accurately the
position of one’s own vehicle is known; the more reliably advanced driver
assistance systems can make decisions in complex traffic situations. One
component of Proreta 4 was therefore a camera-based system for automatically
mapping landmarks such as prominent points on buildings or infrastructure.
These landmarks will later be recognized by the vehicle camera, allowing for
even more accurate localization of the vehicle than is possible with GPS or
navigation data. In this long-term Simultaneous Localization and Mapping (SLAM)
method, landmarks along frequently traveled routes are detected, evaluated and
stored in a data memory in the vehicle. This makes position detection possible
to an accuracy of less than one meter along these routes.
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