Development of an AI-supported driver information assistant to optimize the flow of information in the driving service


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Development of an AI-supported driver information assistant to optimize the flow of information in the driving service

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<Text wird generiert, bitte warten...> VAG Freiburg/Anja Thölking
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Background

In cooperation with the Freiburger Verkehrsbetriebe (VAG)

the KIT is developing an AI-supported assistance system for drivers in local public transport. The aim is to test the use of modern methods from artificial intelligence and machine learning in the operational operation of public transport and thus create a practical introduction to data-driven assistance systems.

As part of this cooperation, an intelligent information assistant will be designed and implemented as a prototype that provides relevant operational information in a context-related, reliable and prioritized manner. Based on existing operational and incident data as well as modern AI methods, such as Retrieval Augmented Generation (RAG), the information flow in the transport service is to be optimized. This can reduce queries, support decision-making processes and make operations more efficient.
The system should also provide personalized daily briefings and situation-dependent recommendations for action and thus actively support drivers in their day-to-day operations. The master's thesis is a collaboration between the KIT and VAG.

Objectives

  • Research into the state of the art in the field of AI-supported assistance systems and information management in the transport sector

  • Analysis of existing information flows and relevant data sources in transport services

  • Conception and development of an AI-based information assistant for context-related and prioritized information provision

  • Implementation and evaluation of a prototype based on real or simulated use cases

  • Documentation of the work and presentation of the results on site

Prerequisites

  • Interest in artificial intelligence, data analysis and mobility systems

  • Basic knowledge of machine learning methods, NLP or RAG architectures is essential

  • Team and communication skills

  • Structured way of working

Benefits

Gain relevant practical experience, opportunity to travel regularly to Freiburg, state-of-the-art AI methods and machine learning processes in application