TEST, SAFETY & SYSTEMS
A virtual crash scenario from the platform
CRASHING CARS
How advanced simulation software is benefiting the design of future autonomous vehicles
A
utonomous cars have been identified as potential solutions to reducing the number of traffic
accidents on our roads for some time, however at present they remain involved in crashes, some with fatal consequences. Improving the safety of such vehicles is therefore of high importance within the transport sector, but optimisation remains difficult as simulating crash scenarios of mixed traffic situations is hampered by a scarcity of relevant data. An innovative machine learning-
based software capable of simulating future car crash scenarios is currently under development at IMC Krems – University of Applied Sciences in Austria. At the heart of the project is developing realistic crash scenarios and simulations of future traffic
32
www.engineerlive.com
scenarios involving autonomous vehicles with the ultimate goal of making them safer for passengers, other road users and pedestrians. “Currently, just like humans,
autonomous vehicles have misjudgements and might react in unexpected ways,” says Professor Alessio Gambi, project leader at the Department of Science and Technology at IMC Krems. “For this reason and the current prevalence of mixed traffic, autonomous vehicles must meet the same safety and crash requirements as conventional vehicles.”
SIMULATION FOR SAFETY Part of a wider project funded by the European Union (EU) called Flexcrash, Gambi’s work will contribute to developing safety
mechanisms for autonomous cars that reduce accident-related consequences. The first step of the project involves extracting driving scenarios from publicly available databases and feeding them into specialised driving simulations. Following this, an optimisation process based on state-of-the-art search algorithms will create novel driving scenarios with increased criticality and severity. “We are designing a platform to
enable running large-scale studies in simulated mixed traffic scenarios,” Gambi explains. “This platform will enable us to study the live interactions between different, and possibly incompatible, driving styles by co-locating human drivers and automated driving agents in the same driving simulations. Consequently, those studies let us identify
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44
