Extraction and Assessment of Naturalistic Human Driving Trajectories from Infrastructure Camera and Radar Sensors

Dominik Notz, Felix Becker, Thomas Kuhbeck, Daniel Watzenig

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

Collecting realistic driving trajectories is crucial for training machine learning models that imitate human driving behavior. Most of today's autonomous driving datasets contain only a few trajectories per location and are recorded with test vehicles that are cautiously driven by trained drivers. In particular in interactive scenarios such as highway merges, the test driver's behavior significantly influences other vehicles. This influence prevents recording the whole traffic space of human driving behavior. In this work, we present a novel methodology to extract trajectories of traffic objects using infrastructure sensors. Infrastructure sensors allow us to record a lot of data for one location and take the test drivers out of the loop. We develop both a hardware setup consisting of a camera and a traffic surveillance radar and a trajectory extraction algorithm. Our vision pipeline accurately detects objects, fuses camera and radar detections and tracks them over time. We improve a state-of-the-art object tracker by combining the tracking in image coordinates with a Kalman filter in road coordinates. We show that our sensor fusion approach successfully combines the advantages of camera and radar detections and outperforms either single sensor. Finally, we also evaluate the accuracy of our trajectory extraction pipeline. For that, we equip our test vehicle with a differential GPS sensor and use it to collect ground truth trajectories. With this data we compute the measurement errors. While we use the mean error to de-bias the trajectories, the error standard deviation is in the magnitude of the ground truth data inaccuracy. Hence, the extracted trajectories are not only naturalistic but also highly accurate and prove the potential of using infrastructure sensors to extract real-world trajectories.

Original languageEnglish
Title of host publication2020 IEEE 16th International Conference on Automation Science and Engineering, CASE 2020
PublisherIEEE Computer Society
Pages455-462
Number of pages8
ISBN (Electronic)9781728169040
DOIs
Publication statusPublished - Aug 2020
Event16th IEEE International Conference on Automation Science and Engineering, CASE 2020 - Hong Kong, Hong Kong
Duration: 20 Aug 202021 Aug 2020

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2020-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference16th IEEE International Conference on Automation Science and Engineering, CASE 2020
Country/TerritoryHong Kong
CityHong Kong
Period20/08/2021/08/20

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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