Road user detection with convolutional neural networks

An application to the autonomous shuttle WEpod

Conference paper (2017)

Authors

F. Gaisser Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
P.P. Jonker Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
Research Group
Intelligent Vehicles (Mechanical, Maritime and Materials Engineering) (TU Delft)
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Published Date

2017

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Intelligent Vehicles

Abstract

Over a million fatal accidents occur every year with road vehicles. Road user detection for Advanced Driver Assistance Systems and Autonomous Vehicles could significantly reduce the number of accidents. Despite the research focus on road user detection and such systems, there is a surprising lack of research in real-world applications. In this work, radar and camera data are combined on an autonomous shuttle called `WEpod', driving on the public road in Wageningen, The Netherlands. With experiments we show that our method reduces the candidate region margin to 0.2m and reduces the miss rate significantly. Furthermore, our specifically trained Convolutional Neural Network improves the performance by 1.4% over vision-based road user detection, and combined with radars we improve by 7.6%. Finally, with our approach we show a performance of 95.1% on the WEpod while driving on the public road.

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