Vehicles with driving automation are increasingly being developed for deployment across the world. However, the onboard sensing and perception capabilities of such automated or autonomous vehicles (AV) may not be sufficient to ensure safety under all scenarios and contexts. Infrastructure-augmented environment perception using roadside infrastructure sensors can be considered as an effective solution, at least for selected regions of interest such as urban road intersections or curved roads that present occlusions to the AV. However, they incur significant costs for procurement, installation and maintenance. Therefore these sensors must be placed strategically and optimally to yield maximum benefits in terms of the overall safety of road users. In this paper, we propose a novel methodology towards obtaining an optimal placement of V2X (Vehicle-to-everything) infrastructure sensors, which is particularly attractive to urban AV deployments, with various considerations including costs, coverage and redundancy. We combine the latest advances made in raycasting and linear optimization literature to deliver a tool for urban city planners, traffic analysis and AV deployment operators. Through experimental evaluation in representative environments, we prove the benefits and practicality of our approach.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Optimal Placement of Roadside Infrastructure Sensors towards Safer Autonomous Vehicle Deployments


    Contributors:


    Publication date :

    2021-09-19


    Size :

    2617800 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Optimization of Roadside Sensors Placement for Cooperative Vehicle-Infrastructure System

    Ji, Yangjie / Yang, Ziru / Zhou, Zewei et al. | IEEE | 2023




    A Pre-study on Data Processing Pipelines for Roadside Object Detection Systems Towards Safer Road Infrastructure

    Yu, Yinan / Scheidegger, Samuel / Grönvall, John-Fredrik et al. | ArXiv | 2022

    Free access

    Generic Approach to Optimized Placement of Smart Roadside Infrastructure Sensors Using 3D Digital Maps

    Kloeker, Laurent / Quakernack, Julian / Lampe, Bastian et al. | IEEE | 2022