The project V-Charge is based on the vision that, due to required drastic decrease of CO2 production and energy consumption, mobility will undergo important changes in the years to come. This includes new concept for an optimal combination of public and individual transportation as well as the introduction of electrical cars that need coordinated recharging. A typical scenario of such a concept might be automatic drop-off and recovery of a car in front of a train station without taking care of parking or re-charging. Such new mobility concepts require among other technologies autonomous driving in designated areas.

The objective of this project is to develop a smart car system that allows for autonomous driving in designated areas (e.g. valet parking, park and ride) and can offer advanced driver support in urban environments. The final goal in four years is the demonstration and implementation of a fully operational future car system including autonomous local transportation, valet parking and battery charging on the campus of ETH Zurich and TU Braunschweig. The envisioned key contribution is the development safe and fully autonomous driving in city-like environments using only low-cost GPS, camera images, and ultrasonic sensors.

Within the proposed project, the focus will therefore be set on the following main topics:

  • Development of machine vision systems based upon close-to-market sensor systems (such as stereo vision, ultrasonic etc.) as well as the integration and fusion of each sensors data into a detailed world model describing static and dynamic world contents by means of online mapping and obstacle detection and tracking.
  • Computer-base situation assessment within the world model as well as describing dependencies and interactions between separate model components (e.g. separate dynamic objects). For this purpose, the integration of market-ready map-material (i.e. originating from navigation systems) as well as the use of vehicle-to-infrastructure communication shall be explored.
  • Precise low-cost localization in urban environments through the integration of standard satellite-based technologies with visual map-matching approaches combining both the onboard-perception system and available map material.
  • Highly adaptive global and local planning considering dynamic obstacles (cars, pedestrians) and their potential trajectory.


ETH Zurich – Autonomous Systems Lab

Volkswagen AG

University of Oxford

Università degli Studi di Parma

Technische Universität Braunschweig

Robert Bosch GmbH

ETH Zurich – Computer Vision and Geometry Lab

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