Since its inception, automotive transport has been relatively stable in terms of its enabling technologies. While Ford’s model T was very different from today’s cars, it was already powered by a combustion engine and manually driven through a driving wheel, foot pedals and a lever for shifting gears. One can imagine that a time traveler from the early 1900’s, experienced on driving the first automobiles, would probably be surprised to know that he or she would be able to fairly quickly get used to drive a modern car. More than 100 years later that seems to be changing. Motors are becoming electric (battery or fuel cell powered). Perhaps even more important from the driver’s perspective, cars are increasingly becoming “smarter” with more and more Advanced Driving Assistance Systems (ADAS) being incorporated, to the point where they may eventually become able to drive themselves without human intervention.
It just so happens that we are not there yet. Automated driving technology is still evolving and the point at which it may be able to fully replace the driver in every situation may not be so close. In the meantime, what we have are semi-automated vehicles that are able handle certain functions and situations themselves, but still need human support in others. The best way to mediate this relation has become an important matter of discussion in the scientific and engineering community. It was also the main focus of the COOPERATIVE initiative, that during one and a half years has brought together researchers from CCG and IFE, in an effort to identify relevant Human Factors challenges related with Connected, Cooperative and Automated Driving (CCAD). The initiative has materialized in several knowledge sharing and discussion events, also involving moments of outreach to the scientific community and the general public, as well as a scientific publication.
Analyzing the literature regarding human-vehicle interaction in the context of CCAD, we have reached some conclusions. Quite important was the understanding that at the core of many Human Factors issues is the paradigm of function allocation, which places the focus of the interaction in the control transitions between human and automation. Concretely, it assumes that a role division should exist between automation and human, with the former taking care of the “low-level” operational part of the driving task (lane keeping, steering) and the latter assuming a vigilant role and expected to intervene in situations that the automation is unable to handle (Metge et al., 2021). Importantly, this function allocation is supposed to be dynamic. Automation may take more or less responsibility depending on situational circumstances that may allow it greater or lower level of autonomy and authority (e.g. highways with clear markings and without vulnerable road users allow for a greater degree of automation).
Transitions thus become the crux of the question. Humans are not good at maintaining vigilance and it may be that they are required to takeover control when they are “out-of-the-loop”. They may be inattentive or drowsy, or simply unaware of the surrounding situation, and consequently, strongly diminished in their ability to control the vehicle and make proper decisions at the moment of takeover (Morales-Alvarez et al., 2020). Also, in a dynamic function allocation paradigm, they may often be not fully aware of how autonomously can the system operate and consequently, of which degree of driving responsibility they should assume. The initiative has thus focused on finding alternative visions to (dynamic) function allocation paradigm, that may allow taking advantage of automation technology, while avoiding some of its pitfalls. We believe that the idea of human-machine collaboration (inspired by the literature on human-robot collaboration – e.g. Bauer et al., 2008; Bicho et al., 2012; Fong et al., 2003) may provide a path to do so. Contrarily to a notion of role distribution, the main idea behind the humanmachine collaboration is that instead of a definition of different tasks to each collaborator, both may be engaged simultaneously in the same task, interacting continuously between themselves and merging contributions until a final objective is completed.
We believe that achieving a collaboration between human and vehicle will depend on technological solutions to fulfill at least three core principles:
Shared Goals - Agents must share the same objective or set of objectives and understand that each action is done towards them; In a human-machine relation it is normally the human that defines the goal. Methods for the human to communicate this goal easily and clearly or for the machine to be able to infer it correctly are thus a necessary materialization to ensure collaboration.
Shared Understanding - Both agents should develop an understanding of the surrounding environment and the task (in this case driving). Most importantly, this understanding should be mutual, as this will be critical so that each one is able to comprehend the actions of the other and thus be able to act complementarily towards the goals.
Action Coordination - Building on shared goals and on the shared situational understanding, the partners may then be able to plan their individual actions assuming for instance that they may have an indirect contribution to the goal by enabling a complementary action from the partner. This strategic understanding of actions is a hallmark of collaboration and perhaps one of the most difficult to achieve given that it implies modeling and predicting the outcome of a set of interactions.
While the COOPERATIVE initiative has reached its end, the shared understanding developed by the participating institutions and briefly reflected in this document will be the basis for future endeavors. CCG and IFE share the goal of extending the state-of-the art in human-vehicle collaboration, proposing solutions that contribute to more efficient and safer road mobility. Finally, they are also set on undertaking a strategy that will take them through continuous collaboration towards such goal. Stay tuned!
The ideas briefly summarized in this document are further explored and detailed in a recent paper published at the Transportation Research Arena 2022 Block, M. Eitrheim M., Mackay. A., Sousa, E., “Connected, Cooperative and Automated Driving: Stepping away from dynamic function allocation towards human-machine collaboration.” Transport Research Arena, 2022
Authors: Emanuel Sousa, Marten Bloch, Maren Eitrheim and Dário Machado
Bauer, A., Wollherr, D., & Buss, M. (2008). Human-robot collaboration: A survey. International Journal of Humanoid Robotics, 5(1), 47–66. https://doi.org/10.1142/S0219843608001303
Bicho, E., Erlhagen, W., Louro, L., & Costa e Silva, E. (2011). Neuro-cognitive mechanisms of decision making in joint action: a human-robot interaction study. Human Movement Science, 30(5), 846–868. https://doi.org/10.1016/j.humov.2010.08.012
Bicho, E., Erlhagen, W., Sousa, E., Louro, L., Hipólito, N., Costa e Silva, E., Silva, R. M., Ferreira, F., Machado, T., Hulstijn, M., Maas, Y., Bruijn, E. De, Cuijpers, R. H., Newman-Norlund, R., Schie, H. Van, Meulenbroek, R., & Bekkering, H. (2012). The Power of Prediction : Robots that Read Intentions. Proc. International Conference on Intelligent Robots and Systems, 5458– 5459. https://doi.org/10.1109/IROS.2012.6386297
Fong, T., Nourbakhsh, I., & Dautenhahn, K. (2003). A survey of socially interactive robots. Robotics and Autonomous Systems, 42, 143–166.
Metge, A., Maille, N., & Le Blanc, B. (2021). Transition between cooperative and collaborative interaction modes for human-AI teaming. CNIA 2021: Conférence Nationale En Intelligence Artificielle, pp–38.
Morales-Alvarez, W., Sipele, O., Léberon, R., Tadjine, H. H., & Olaverri-Monreal, C. (2020). Automated driving: A literature review of the take over request in conditional automation. In Electronics (Switzerland) (Vol. 9, Issue 12, pp. 1–34). https://doi.org/10.3390/electronics9122087
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