AI Driven Mobility

Learnings

Over the course of a year, the project emphasized the critical role of AI in mobility and underlined the ongoing need to bridge the knowledge gap between AI and mobility. Maintaining momentum means continued exploration and fostering mutual understanding between AI and mobility experts. Establishing a diverse advisory board is essential to effectively guide future initiatives. Rapid experimentation with ideas is essential, facilitated by an efficiently managed platform. The project's flexible financial structure, which enabled rapid feasibility studies and collaborations, proved decisive. Further expansion of the network, inclusion of additional partners and continuous knowledge sharing are essential to foster new collaborations and bridge existing gaps.

Result

The AI-Driven Mobility project served as an initial exploration of AI's potential in the mobility sector, promoting collaboration between AI and mobility experts. It established a network that facilitates knowledge sharing and partnership formation, critical to advancing AI applications in mobility. In addition, the project strengthened AI competence in mobility, delineated practical implementations, and promoted interdisciplinary understanding. It developed a methodology for idea generation and project development, and identified key areas for collaborative innovation.

Six feasibility studies mentioned below were initiated to identify high-potential AI applications for sustainable mobility solutions, with the goal of laying the foundation for larger projects. The project's success hinged on addressing the concrete operational needs of participating organizations and fostering a culture of transformative collaboration. These efforts underscore the necessity of continued exploration and partnerships to drive innovation in the mobility landscape.

1. AI in roadworks:
Use of Intelligent Transportation Systems (ITS) to improve safety and efficiency in roadworks, reduce rear-end collisions and optimize real-time traffic management strategies.

2. AI reduces the risk of near incidents:
Development of real-time calculation methods for near incidents and risk indicators at entry and exit points, with the goal of improving road safety through effective control strategies.

3. AI-powered identification of roadside objects "Multilayer road data model":
Create a detailed 3D model of road surfaces and surroundings for various applications such as route planning, navigation for autonomous vehicles and predictive maintenance.

4. AI support for society and infrastructure:
Leverage new technologies such as large-scale data analytics, sensor data and AI to address societal challenges and optimize planning processes for agile strategies in society and infrastructure management.

5. AI applications for societal transport - long-term solutions:
Identify project ideas to use AI in long-term transport solutions, address stakeholder needs and promote understanding of AI's potential benefits.

6. Systems analysis of the potential to apply AI in the logistics sector:
Conduct a systems analysis to identify challenges and opportunities with AI implementation in freight transport, including the need for improved data management and collaboration platforms between stakeholders.

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