From: Technical University of Munich
September 15, 2020 -- Before autonomous
vehicles participate in road traffic, they must demonstrate conclusively that
they do not pose a danger to others. New software developed at the Technical
University of Munich (TUM) prevents accidents by predicting different variants
of a traffic situation every millisecond.
A car approaches an intersection.
Another vehicle jets out of the cross street, but it is not yet clear whether
it will turn right or left. At the same time, a pedestrian steps into the lane
directly in front of the car, and there is a cyclist on the other side of the
street. People with road traffic experience will in general assess the
movements of other traffic participants correctly.
"These kinds of situations present
an enormous challenge for autonomous vehicles controlled by computer
programs," explains Matthias Althoff, Professor of Cyber-Physical Systems
at TUM. "But autonomous driving will only gain acceptance of the general
public if you can ensure that the vehicles will not endanger other road users
-- no matter how confusing the traffic situation."
Algorithms that peer into the future
The ultimate goal when developing
software for autonomous vehicles is to ensure that they will not cause
accidents. Althoff, who is a member of the Munich School of Robotics and
Machine Intelligence at TUM, and his team have now developed a software module
that permanently analyzes and predicts events while driving. Vehicle sensor
data are recorded and evaluated every millisecond. The software can calculate
all possible movements for every traffic participant -- provided they adhere to
the road traffic regulations -- allowing the system to look three to six
seconds into the future.
Based on these future scenarios, the
system determines a variety of movement options for the vehicle. At the same
time, the program calculates potential emergency maneuvers in which the vehicle
can be moved out of harm's way by accelerating or braking without endangering
others. The autonomous vehicle may only follow routes that are free of
foreseeable collisions and for which an emergency maneuver option has been
identified.
Streamlined models for swift
calculations
This kind of detailed traffic situation
forecasting was previously considered too time-consuming and thus impractical.
But now, the Munich research team has shown not only the theoretical viability
of real-time data analysis with simultaneous simulation of future traffic
events: They have also demonstrated that it delivers reliable results.
The quick calculations are made possible
by simplified dynamic models. So-called reachability analysis is used to
calculate potential future positions a car or a pedestrian might assume. When
all characteristics of the road users are taken into account, the calculations
become prohibitively time-consuming. That is why Althoff and his team work with
simplified models. These are superior to the real ones in terms of their range
of motion -- yet, mathematically easier to handle. This enhanced freedom of
movement allows the models to depict a larger number of possible positions but
includes the subset of positions expected for actual road users.
Real traffic data for a virtual test
environment
For their evaluation, the computer
scientists created a virtual model based on real data they had collected during
test drives with an autonomous vehicle in Munich. This allowed them to craft a
test environment that closely reflects everyday traffic scenarios. "Using
the simulations, we were able to establish that the safety module does not lead
to any loss of performance in terms of driving behavior, the predictive
calculations are correct, accidents are prevented, and in emergency situations
the vehicle is demonstrably brought to a safe stop," Althoff sums up.
The computer scientist emphasizes that
the new security software could simplify the development of autonomous vehicles
because it can be combined with all standard motion control programs.
https://www.sciencedaily.com/releases/2020/09/200916113601.htm
No comments:
Post a Comment