High quality reference data is crucial for the development of autonomous driving applications. Unfortunately, datasets including fixed, reproducible static environments that contain manifold interactions between traffic participants are not widely available. In this paper we propose a camera based trajectory estimation framework that enables the generation of reference trajectory data in stationary roadside infrastructure. We develop a Simple Online Realtime Tracking (SORT) algorithm that tracks objects in image space utilizing the tracking-by-detection paradigm with a deep neural network detector. By projecting tracks to a ground model, we are able to gather cartesian and georeferenced trajectories for manually driven and autonomous vehicles in the field. We evaluate the framework in stationary roadside infrastructure in the Test Area Autonomous Driving Baden-Württemberg, Germany. A vehicle equipped with inertial measurement unit and differential GPS is used to generate ground truth positions that are compared with our framework.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Robust Tracking of Reference Trajectories for Autonomous Driving in Intelligent Roadside Infrastructure


    Contributors:


    Publication date :

    2020-10-19


    Size :

    2937051 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    ROBUST TRACKING OF REFERENCE TRAJECTORIES FOR AUTONOMOUS DRIVING IN INTELLIGENT ROADSIDE INFRASTRUCTURE

    Fleck, Tobias / Ochs, Sven / Zofka, Marc Renè et al. | British Library Conference Proceedings | 2020



    Roadside Infrastructure Support for Urban Automated Driving

    Pechinger, Mathias / Schroer, Guido / Bogenberger, Klaus et al. | IEEE | 2023


    Influence of Roadside Infrastructure on Driving Behavior

    van der Horst, Richard / de Ridder, Selma | Transportation Research Record | 2007