Currently, a significant gap exists between academic and industrial research in automated driving development. Despite this, there is common sense that cooperative control approaches in automated vehicles will surpass the previously favored takeover paradigm in most driving situations due to enhanced driving performance and user experience. Yet, the application of these concepts in real driving situations remains unclear, and a holistic approach to driving cooperation is missing. Existing research has primarily focused on testing specific interaction scenarios and implementations. To address this gap and offer a contemporary perspective on designing human–vehicle cooperation in automated driving, we have developed a three-part taxonomy with the help of an extensive literature review. The taxonomy broadens the notion of driving cooperation towards a holistic and application-oriented view by encompassing (1) the “Cooperation Use Case”, (2) the “Cooperation Frame”, and (3) the “Human–Machine Interface”. We validate the taxonomy by categorizing related literature and providing a detailed analysis of an exemplar paper. The proposed taxonomy offers designers and researchers a concise overview of the current state of driver cooperation and insights for future work. Further, the taxonomy can guide automotive HMI designers in ideation, communication, comparison, and reflection of cooperative driving interfaces.

Research on automotive user interfaces has highlighted the importance of transparent and explainable user interfaces, especially in safety-critical situations where automation failures can lead to (fatal) accidents. In this work, we investigated the use of augmented reality (AR) windshield displays as a means to improve explainability in safety-critical driving situations. To this end, we conducted a mixed-methods study (VR-based driving simulation combined with a semi-structured interview) with N = 24 participants and evaluated two concepts, containing (1) first-person information about vehicle behavior and (2) additional information about other traffic objects. Results showed that confidence and situational awareness were significantly higher when additional information was presented. The highest scores were achieved when participants were shown "how & why" information.

Cooperation between drivers and automated vehicles requires transparent communication of the automation’s current status. This can be achieved by communicating confidence or certainty in its current perception or decision. We evaluate different sensory modalities for communicating information about how safely an automated driving system can perform the driving task in critical traffic situations to a driver who is present as a cooperator. We aimed to improve communication between the driving system and the human driver, ultimately increasing the overall driving experience and performance. In a virtual reality driving simulation study with 34 participants, we presented confidence information across three modalities: visual, auditory, and vibrotactile, compared to a baseline condition. Our results indicate that communicating automation uncertainty through the auditory and vibrotactile modalities improved user experience, trust in automation, and perceived safety. At the same time, interactions with the Non-Driving Related Task were reduced by communicating confidence information in critical driving situations.

Most of today’s studies investigating the driver-vehicle interaction of automated driving systems are conducted in simulated environments like driving simulators or virtual reality. While this simulation-based experimental research can produce valuable and valid results, it is at the same time limited by the inherent lack of realism. Important insights into real-world driving experiences and repeated system usage are rarely collected due to the constraints imposed by time and financial resources. In a multi-step research approach, we aim to connect the AutoUI research with real-world users. In the first step, we conducted qualitative interviews with 10 experienced, tech-savvy users of current automated driving systems (Waymo, Cruise, Tesla) and clustered the results into the most important issues from a human factor perspective.

Cooperative driving has been established as a method to engage the human driver even in higher levels of automated driving, in which human intervention is not strictly necessary. However, a key question regarding a cooperative approach is whether users of automated vehicles even feel the need for cooperative guidance in the vehicles equipped with SAE level 4 and 5 automation, where the human driver is no longer required to intervene. In order to answer this question, we conducted a use case analysis, in which we interviewed (N=16) participants regarding their need for cooperation in different situations. Results show that nearly all participants still wanted to be able to intervene in the driving task, which, however, was dependent on the specific situation. User needs were similarly dependent on the specific situation, while “Autonomy”, “Security”, “Competence”, and “Relatedness” emerged as the most important psychological needs.