Autonomous vehicles represent a significant development in our society, and their acceptance will largely depend on trust. This study investigates strategies to increase trust and acceptance by making the cars' decisions. For this purpose, we created a virtual reality (VR) experiment with a self-explaining autonomous car, providing participants with verbal cues about crucial traffic decisions. First, we investigated attitudes toward self-driving cars among 7850 participants using a simplified version of the Technology Acceptance Model (TAM) questionnaire. Results revealed that female participants are less accepting than male participants, and that there is a general decline among all genders. Otherwise in general, a self-explaining car has a positive impact on trust and perceived usefulness. Surprisingly, it adversely affected the intention to use and perceived ease of use. This entails dissociation of trust from the other items of the questionnaire. Second, we analyzed behavioral of 26 750 participants to investigate the effect of self-explaining systems on head movements during the VR drive. We observed significant differences in head movements during the

While abundant in biology, foveated vision is nearly absent from computational models and especially deep learning architectures. Despite considerable hardware improvements, training deep neural networks still presents a challenge and constraints complexity of models. Here we propose an end-to-end neural model for foveal-peripheral vision, inspired by retino-cortical mapping in primates and humans. Our model has an efficient sampling technique for compressing the visual signal such that a small portion of the scene is perceived in high resolution while a large field of view is maintained in low resolution. An attention mechanism for performing “eye-movements” assists the agent in collecting detailed information incrementally from the observed scene. Our model achieves comparable results to a similar neural architecture trained on full-resolution data for image classification and outperforms it at video classification tasks. At the same time, because of the smaller size of its input, it can reduce computational effort tenfold and uses several times less memory. Moreover, we present an easy to implement bottom-up and top-down attention mechanism which relies on task-relevant features and is therefore a convenient byproduct of the main architecture. Apart from its computational efficiency, the presented work provides means for exploring active vision for agent training in simulated environments and anthropomorphic robotics.

Autonomous vehicles as cognitive agents will be an important use case of artificial intelligence in modern societies. Investigating how to increase acceptance and trust, we created a self-explaining car, informing passengers before actions in virtual reality. This study investigates the attitude towards self-driving cars with data from 7850 participants. We show how gender and age affect the attitude towards autonomous vehicles, resulting in female participants being generally less trusting of overall conditions than male participants and a general decrease of acceptance with increasing age. Surprisingly, a self-explaining car providing the passenger with crucial traffic information, although it has a positive impact on trust but influences the intention of using such a car negatively. Therefore, we argue for a highly individualizable in-car communication that meets the adversarial needs of different demographic groups to enable human-machine interactions that foster safe traffic behavior and increase trust and the willingness to use such technology.