TEST, SAFETY & SYSTEMS
potentially critical situations that humans, autonomous vehicles, or both cannot safely handle. Additionally, those studies will help us understand how well autonomous vehicles can cope with unpredictable (and sometimes unsafe and erroneous) human manoeuvres. For instance, could autonomous vehicles avoid collisions caused by distracted human drivers departing their traffic lanes.” Gambi notes that there is currently
only one globally available openly accessible database that records the level of autonomy of car involved in a crash – California’s DMV autonomous vehicle collision report database. This is a very limited base for simulating future car crash scenarios, and the project’s aim is to widen that base. “The ability to virtually simulate
various crash scenarios and configurations will allow future generations of vehicles to be designed and engineered for more realistic, non-laboratory crash scenarios,” he adds. “Doing so can improve overall occupant protection and compatibility.”
HARNESSING MACHINE LEARNING Using data from existing sources, the team will select a set of reference
driving scenarios as a base for the next project step to feed the BeamNG tech driving simulation. Additionally, the team will develop an online, open simulation platform that follows multi-player paradigms, such as those used in video games where remote players interact with other, and artificial intelligence (AI). Using the platform, the researchers will study virtual live interactions between human drivers and simulated autonomous vehicles in order to generate an additional set of traffic scenarios that are not based on previous accidents, but real interactions. Leveraging these two sources,
specialised search algorithms will calculate virtual crash scenarios that anticipate possible actions by autonomous vehicles. “We actually develop these
algorithms in-house and hence we can easily increase the virtual criticality and severity of the simulated crashes,” says Gambi. “This will expose car structures chiefly involved in severe crashes and predict their behaviour in such situations.”
IMPROVING FUTURE VEHICLE DESIGN Not only is the project aiming
to improve the safety of future autonomous vehicles, but it is also tackling challenges surrounding pollution and manufacturing costs. “Vehicle safety in general, and
occupant protection in particular, is at odds with the ecological lightweight of safety mechanisms as these requirements conflict with each other: increasing occupant protection usually implies increasing a vehicle’s mass, which requires using more material and increases production costs and pollution,” offers Gambi. “In the future, hybrid manufacturing and hybrid material technologies will make it possible to develop lighter, safer, and more cost- effective crash structures through virtual optimisation and simulation.” IMC Krems’ work is the basic work
package of the overall Flexcrash project, the results of which will provide data for improving the design of future autonomous cars. The final goal of the wider project is to use hybrid manufacturing technology for applying surface patterns using additive manufacturing onto preformed parts, a technique that the participants hope will contribute to greatly reducing accident-related fatalities, injuries, pollution and manufacturing costs in future.
The platform aims to anticipate possible actions by autonomous vehicles
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