CHange stands for Competitive Heavy-vehicles using AI to create Next Generation Efficiency)

How do we arrive at new insights on how AI/Machine Learning methods, in relation to other relevant data science techniques, can effectively contribute to data-driven innovations in commercial vehicles? A new basis for practical deployment of AI/Machine learning aimed at cleaner, safer and more productive transportation.

Transport is responsible for a quarter of EU greenhouse gases. Innovations in freight transport are now mainly focused on the towing vehicle. Many of these innovations are based on the continuous data streams available in the vehicle through on-board systems such as FMS, EBS, GPS, etc. (data-driven innovation). This has made freight traffic cleaner and more efficient (CO2 reduction of 17% between 2010 and 2020, see [1]). But we are far from there yet, given the far-reaching ambitions in the 'Green Deal' and the various national climate plans. Truck traffic is growing in number and transport capacity. Comparable innovations have hardly reached the semi-trailer or trailer yet although the aforementioned data streams can be used perfectly well for that. That is a missed opportunity and CHANGE is going to change that.

This above summarized is being explored in the 4-year RaakPRO program: CHANGE. And for this purpose, the following question has been formulated together with the consortium members:

What added value does (data driven) designing and making towed equipment smarter, whether or not in conjunction with the systems in the towing vehicle, offer for cleaner, more efficient and productive transport?

This question is answered on the basis of research in three Use Cases, see Figure 1. CHange started on 15 January 2023.

This figure contains the research question

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