Dein Suchergebnis zum Thema: Trainer

Attack on autopilots

https://www.mpg.de/14931044/attack-on-autopilots

How fast the development from assisted to fully automated vehicles will progress is uncertain. One crucial factor here is the reliability with which a vehicle can navigate in its surroundings and react to unforeseeable incidents. Our group at the Max Planck Institute for Intelligent Systems showed that methods for motion analysis based on deep neural networks – likely components in future autonomous vehicles – can be confused by small patterns designed to “attack” these networks.
Our work can help manufacturers to train their systems to withstand such disturbances

Colour patch could throw self-driving vehicles off track

https://www.mpg.de/14311323/colour-patch-could-throw-self-driving-vehicles-off-track?c=19150566

A team of researchers in Tübingen show that optical flow systems based on deep neural networks – a likely component of future autonomous cars – are vulnerable to adversarial attacks. The computer vision experts are shaking up the automotive industry by warning car manufacturers around the globe that it could take a simple colour pattern to put the brakes on computer vision systems in autonomous cars.
self-driving technology that there is a new possible threat and enable them to train

Ralph Hertwig about behaviourial psychology and climate protection

https://www.mpg.de/17576465/ralph-hertwig-behavioural-psychology-climate-protection

Can psychological tools drive societal change towards a sustainable planet? For years, scientists have been warning the public about the dangers of climate change. Its impact can already be felt in Germany. Only decisive and rapid action can at least mitigate the consquences. But what is preventing us from taking urgently needed action? Psychologist Ralph Hertwig, Director at the Max Planck Institute for Human Development in Berlin, explains the reasons and offers suggestions on how we can change our habits.
For example, buses and trains could be free of charge, or cycle paths could be massively