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Best student paper award for Risto Ojala for an innovative way of improving traffic safety with pedestrian localisation

IEEE Finland Section awarded Risto Ojala, a student at Aalto University, with the best student journal paper award 2020
Risto Ojala

IEEE Finland Section awarded Risto Ojala, a student at Aalto University, with the best student journal paper award 2020 for the paper "Novel Convolutional Neural Network-Based Roadside Unit for Accurate Pedestrian Localisation"that was published in IEEE Transactions on Intelligent Transportation Systems.

IEEE Finland Section calls for the nominations of best student papers every year. The purpose of the best student paper award is to encourage young researchers for further pursuit their career in academic. The decision on the award is based on scientific excellence, originality, utility, timeliness and impact.

Improving traffic safety in intersections

The journal article presents an intelligent transportation system aimed for improving traffic safety in occluded intersection areas. The system is based on a camera-unit, which is capable of localizing different road users (vehicles, pedestrians, cyclists etc.) and transmitting their locations to approaching smart vehicles. This location data can be conveyed to the driver, allowing them to prepare for the road users present at the intersection. This preparation helps avoiding hazardous situations where the driver is caught off guard by, for example, a cyclist appearing from behind the corner.

Localization of the road users is carried out with a single camera to avoid the installation costs of stereovision systems, as well as allowing conversion of existing traffic cameras. The journal article evaluates the accuracy of the monovision distance measurements, as well as sensitivity to different sources of error. Based on the results, the monovision localization was found suitably accurate for the application. However, cameras used for the localization task must be carefully calibrated, as even slight errors in the localization parameters can significantly impact the localization accuracy. This work was carried out for a project funded by Henry Ford Foundation Finland.

Risto is a MSc student in Mechanical Engineering, and he is currently finalizing his MSc thesis related to the intelligent transportation system presented in the journal article. He has worked in the Engineering Design research group for nearly three years alongside his studies. His research interests focus on machine vision and machine learning applications in smart mobility and vehicle technology. After completing his MSc degree, Risto aims to continue his research as a PhD student.

Professor Kari Tammi is very proud of Risto's work and happy of the award. "Risto is an extremely talented student, we are likely to hear from him in the future", says Kari. 

Read the paper 

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