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AI as powerful tool to create a balance between safety, resilience, sustainability and inclusivity of CAD
CAD, I believe, is a means to an end. Research and Innovation on CAD, pilots, living labs and at the end of the day of course deployment of CAD technologies, should all contribute to high level and overarching targets, like those set in the EC Sustainable and Smart Mobility Strategy (2020), or more recently, Fit for 55. Any new technology introduced should contribute to making our mobility system safer, more sustainable, resilient and inclusive. Key to achieve this will be the user centricity of our solutions, and a systems approach. This drive for a systems approach is an essential part for the European CAD activities, and inherent part of their success. Within the overall portfolio of technologies for CAD, Artificial Intelligence (AI) can be a very powerful tool to achieve several of these targets, as well as to combine ambitions. CAD should not be safe or resilient, but both. It should be sustainable and inclusive. In any combination. AI can ty the knots to achieve this.
AI can be excellently used in complex situations; there it is at its best. Our (road) mobility system is full of such complex situations. Many of these, we experience in our daily trips. But when trying to achieve the overarching ambitions, we do need to take into account several more layers of information and aims. Decreasing congestion by optimised traffic management, greening of road mobility by more efficient use of transport means, enabling seamless multimodal trips, enhancing road safety by using information of what is yet beyond our direct vicinity; they all add to the need of a systems perspective. Using real-time data and shared data, is essential. How this can be done, has been (and will be!) the topic of research and discussion for quite some time. The CAD Knowledge Base supports this, bringing the lessons learned in leading projects together. Fragmentation of data sharing approaches would limit the creation of seamless mobility, and lead to duplication of data storage, huge data inefficiencies, as well as a lack of coherence.
With increasing levels of complexity, we need different subsets of AI, such as Machine Learning and Deep Learning. When developing these technologies, it’s important to keep in mind that context-aware AI is of essence. Up until recently, vehicle-related AI had a primary focus on the vehicle state. Now, as we’re gradually moving to higher levels of automation, it will be needed to incorporate information and predictions on not only vehicle state, but also human (driver) state and system state (environment around the vehicle).
Another topic of increasing relevance is trustworthy and reliable AI. They will play a key role in supporting the public acceptance of AI-based CCAM technologies and their market uptake, as well as in boosting the essential societal benefits (e.g. safety, emissions, inclusivity, different approaches to land use especially in dense urban areas). Progressing beyond the current State of the Art should thus firmly address the recommendations of the AI High Level Expert Group and their Guidelines for Trustworthy AI . In brief, we should put effort in demystifying AI (for CAD or CCAM) to enable user acceptance and user embracement of these new technologies. Technologies which, I’d like to stress that, need to reinforce the already good technologies of CAD. Afterall, AI is just a means to an end….
Margriet van Schijndel
Program Director Responsible Mobility, Eindhoven University of Technology (TU/e)