Security software for autonomous vehicles

Date:
September 16, 2020
Source:
Technical University of Munich (TUM)
Summary:
Before autonomous vehicles participate in road traffic, they
must demonstrate conclusively that they do not pose a danger to
others. New software prevents accidents by predicting different
variants of a traffic situation every millisecond.



FULL STORY ========================================================================== 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.


========================================================================== Story Source: Materials provided by
Technical_University_of_Munich_(TUM). Note: Content may be edited for
style and length.


========================================================================== Journal Reference:
1. Christian Pek, Stefanie Manzinger, Markus Koschi, Matthias
Althoff. Using
online verification to prevent autonomous vehicles from causing
accidents. Nature Machine Intelligence, 2020; 2 (9): 518 DOI:
10.1038/ s42256-020-0225-y ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/09/200916113601.htm

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