A large number of testing procedures have been developed to ensure vehicle safety in common and extreme driving situations. However, these conventional testing procedures are insufﬁcient for testing autonomous vehicles. They have to handle unexpected scenarios with the same or less risk a human driver would take. Currently, safety related systems are not adequately tested, e.g. in collision avoidance scenarios with pedestrians. Examples are the change of pedestrian behavior caused by interaction, environmental inﬂuences and personal aspects, which cannot be tested in real environments. It is proposed to use augmented reality techniques. This method can be seen as a new (Augmented) Pedestrian in the Loop testing procedure. Currently, driving situations with pedestrians are often tested in observational statistical studies rather than in randomized control experiments, due to safety reasons. This has an enormous impact on the development of motion planning strategies (conservative conﬁguration) in autonomous vehicles and the usage for real scenarios (low generalizability, some as- pects are not tested, i.e. intention, environmental aspects). Some facts are revisited to propose the new test concept: 1. There is no absolute certainty in pedestrian movement prediction due to a lack of knowledge. 2. Environmental understanding and human behaviour is a core challenge for automated vehicles. 3. Many situation predictions for pedestrians might be plausible. 4. Motion planning with pedestrians is a safety critical application; environmental inﬂuences, intention changes, perception, interaction and personal aspects are not directly testable in a randomized controlled experiment. 5. Personal aspects, interaction, perception, intention changes and environmental inﬂuences on pedestrians must be tested. The new test environment for indoor and outdoor environ- ments is proposed with incorporation of augmented reality glasses to consider the stated facts.