AI ROAD SIDE - Recommendation for methodology on AI-based Road-side object identification

Project manager
John-Fredrik Grönvall
2021-05-10 - 2021-09-30

The project aims to investigate and specify the Swedish Transport Administration’s requirements for Road-side image processing, e.g. what is state of the art and what has already been done, are there existing solutions we can further utilize? Possible data scources, e.g. road scanning data and vehicle camera images from reference road areas will be investigated and specified. Processing possibilities will also be worked through e,g, requirements and costs for data storage and data access interfaces. Finally, the draft requirement for Proof of Concept for a road-side image processing project will be set up.

The result aim to be a recommendation for methods and requirements for data processing, including also how to set-up a Proof of Concept, for road-side object processing. The tool will help us to create very precise dynamic maps that can optimize road maintenance and information to automated vehicles in their self-driving mode.

Short facts

Project title: AI ROAD SIDE - Recommendation for methodology on AI-based Road-side object identification
Project type:
Research area:
Safety performance evaluation

Project publications

Single-vehicle accidents are the most common type of fatal accidents in Sweden, where a car drives o the road and runs into hazardous roadside objects. Proper installation and maintenance of protective objects, such as crash cushions
Yinan Yu, Samuel Scheidegger, John-Fredrik Grönvall, Magnus Palm, Erik Svanberg, Johan Amoruso Wennerby, Jörg Bakker
Year of publication

Safer – Vehicle and Traffic Safety Centre

SAFER is the open research arena where researchers and expertise work together to create safe mobility. Our traffic safety approach covers people, vehicles and the infrastructure – and together we contribute to safer road transports and smarter, more sustainable cities.

Contact information


Lindholmspiren 3A
SE-417 56 Göteborg

 +46 31-772 21 06
 safer [at]