MODI
A leap towards SAE L4 automated driving features
42 months
October 2022 to April 2026
The challenge
The next big leap is automated and autonomous driving. The development of the EU’s automotive industry is moving along a number of different paths. One of these is cooperative, connected, and automated mobility (CCAM). Even though the European Commission announced its commitment to CCAM in 2016, the road to connected and cooperative vehicles has been bumpy. In this context, the EU-funded MODI project will identify and address barriers in confined areas and on public roads for autonomous vehicles (no requirement for human interaction in their operations) on the corridor from Rotterdam to Oslo, and demonstrate the solutions. The project will identify the possibilities and what is not possible yet. MODI accelerates the introduction of highly automated solutions to improve European logistic chains. MODI comprises five use cases, each describing a part of the logistics supply chain.
The solution
MODI is a European cross-border flagship initiative to accelerate the introduction of Connected, Cooperative and Automated Mobility (CCAM) solutions to significantly improve logistic chains. MODI will focus on the interaction between vehicles and logistics processes, traffic safety, security, optimal utilisation of infrastructure capacity and environment, and recommendations for the optimal design or adaptions of infrastructure. The MODI initiative will demonstrate what is needed to have automated freight transport on an entire corridor through five European countries.
The overall objective of MODI is to accelerate the introduction of connected, cooperative and automated mobility (CCAM) vehicles for logistics by demonstrations and to overcome barriers for the roll-out of automated transport systems and solutions in logistics. The overall objective is divided into four individual goals as depicted below:
Implement the latest technology and overcome major CCAM deployment barriers for logistics by demonstrating business-oriented and well-integrated CCAM systems for use cases (UCs) along a transport corridor and between hubs
Define recommendations for adaptations of supporting infrastructure, vehicle regulations and standards to enable broader deployment of CCAM, speeding up the introduction of CCAM vehicles and recommendations for further (e.g., large scale) piloting
Demonstrate business models and partnerships for the application of CCAM vehicles in logistics
Perform technical & socio-economic impact assessments and communicate them in the context of the best practices of the MODI L4 CCAM solutions and systems for real-world conditions
Outcomes: Key results
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Heavy Duty Vehicles (HDV) suitable for demonstration for the selected 4+1 use cases along the motorway corridor between Norway and The Netherlands
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Interface for the coordination of vehicles inside and outside confined areas, while adding more benefits to the use of CCAM vehicles thanks to the increased efficiency achieved with coordination
- Set-up of Physical and Digital Infrastructure (PDI) validated and created in co-design by relevant stakeholders for the use of L4 trucks on motorways
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New sustainable business models and tools, which include CCAM vehicles in the logistics chain
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Impact assessment report, recommendations, and best practices in the field of CCAM applied to Logistics
- To disseminate lessons learnt and accelerate CCAM adoption in Logistics by contributing to Harmonisation, Standardisation, and Infrastructure readiness
Role of Enide
- Communication
- Dissemination
Services related to the role
- Supportive role in WP4, WP5
- Supportive role in WP6
PoDIUM
PoDIUM will identify and assess the connectivity and cooperation enablers to reach higher levels of automation and advance important Physical and Digital Infrastructure (PDI) technologies, through a multi-connectivity approach and an interoperable and hybrid data management environment.
Inpercept
The objective is to develop sustainable and egalitarian mobility supported by technology. The project aims to limit human driving and control capabilities and improve traffic capacity and conditions by designing, developing and implementing key technologies capable of moving driving towards more and more automation.
GAMMS
Is enabling the exploitation of space data for surveying and mapping. It will develop an autonomous terrestrial mobile mapping system (AMMS), based on the tight integration of autonomous vehicle (AV), navigation/geodetic, and artificial intelligence (AI) technologies. ENIDE will lead the communication, dissemination and exploitation activities.