Event

Welcome to Daniel Åsljung's doctoral dissertation! He will defend his doctoral thesis "On Statistical Methods for Safety Validation of Automated Vehicles".

Daniel Åsljung is a PhD student in the Mechatronics research group at Chalmers University, in collaboration with Zenseact. His research is based on theory and methods around the verification of autonomous vehicles. Since autonomous vehicles need to be able to handle all situations that may arise, this places significantly higher demands on their verification. New methods are therefore needed to be able to verify security in a more time-efficient way.

• Opponent: Professor Dr. Simon Burton (Fraunhofer IKS, Germany)
• Supervisors: Jonas Fredriksson (Professor, Chalmers) & Carl Zandén (Zenseact)

Summary

Every year more than a million lives are cut short due to traffic accidents. However, most traffic accidents are caused by human error and if these causes can be removed, many lives could be saved. Autonomous vehicles (AVs) will never be as tired or distracted as humans are and are expected to lead to a significantly safer traffic environment.

Before AVs can be used by the public and enable a safer future, it needs to be shown that they are as safe as they should be. As a result, we need evidence that AVs have fewer accidents than human drivers in real traffic. This evidence is not simple to obtain since humans are, on average good drivers, and fatalities may occur less often than once every 100 million kilometers. Driving this distance to show the absence of accidents before every release does not scale well.

This thesis presents multiple approaches to creating this evidence more efficiently. The first method uses computer simulations of the actual vehicle to provide safety evidence of the software before it is used in an actual vehicle. Simulation efforts are also focused on the areas where it is believed to be closest to failure, which makes it more efficient. The result is a precise estimate of how often the AV software will fail and the specific scenarios where it will happen.

A second method is evaluating the safety of AVs in real traffic. It evaluates situations that were close to being accidents and uses them to estimate the frequency of actual accidents. The method makes it possible to show that the AVs are safe without experiencing any real accidents. In addition, the second method is also used to form a predictive safety monitor for a fleet of AVs. The results show that the predictive monitor significantly reduces the risk of deploying unsafe AVs.