Methods for Improving the Accuracy of the Virtual Assessment of Autonomous Driving Assessing the safety of an autonomous vehicle is an open problem within the research domain for autonomous vehicles. Next to realworld driving tests, simulation and reprocessing of recordings play a crucial role in validating the correct and safe behavior. Current stateoftheart methods for function reprocessing suffer from several sources of error and hence, might lead to incorrect results. In this work, an overview of the most recent reprocessing methods is given and their shortcomings are described. We suggest the derivation of explicit sensor models and the learning of behavior models for traffic objects. An overview of different levels of sensor and different kinds of agent models is given along with a discussion for the need for statistical and machine learning based models. Furthermore, a novel method, based on infrastructure sensors, to collect the data needed for the derivation of the models is presented.

A behavior driven approach for sampling rare event situations for autonomous vehicles Performance evaluation of urban autonomous vehicles (AVs) requires a realistic model of the behavior of other road users in the environment. Learning such models from data involves collecting naturalistic data of realworld human behavior. In many cases, acquisition of this data can be prohibitively expensive or intrusive. Additionally, the available data often contain only typical behaviors and exclude behaviors that are classified as rare events. To evaluate the performance of AVs in such situations, we develop a model of traffic behavior based on the theory of bounded rationality. Based on the experiments performed on a large naturalistic driving data, we show that the developed model can be applied to estimate probability of rare events, as well as to generate new traffic situations for testing.