Every now and then a new dataset appears that aims to help training computer vision models for autonomous vehicles (AVs), being KITTI [1] the most famous, but others like Waymo [2] or nuScenes [3] can be found that try to innovate by adding more sensors, giving more data about the ego-vehicle surroundings or offering new scenarios. These approaches tend to produce a high quality dataset, but at the cost of time and money. The key difficulty is to create datasets with enough data to generalize multiple road scenarios and hazard use-cases in the training process of perception models for AVs. To address this concern, we propose AD PerDevKit, a perception development kit for autonomous driving based on CARLA simulator [4] able to generate an infinite set of annotated data referred to the ego-vehicle by recording virtual car sensors (camera, LiDAR, Radar) in virtual environments. Our ground-truth generator provides real-time information of the objects surrounding the vehicle that are visible for each sensor by publishing them on a ROS [5] topic. In addition, a dataset generated with this tool is also presented for some specific challenging scenarios and an evaluation of its contribution in the improvement of 2D object detection methods on real data is included. The code of our tool and its database is available in the following git repository: https://github.com/Javier-DlaP/ad_perdevkit.


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    Titel :

    AD PerDevKit: An Autonomous Driving Perception Development Kit using CARLA simulator and ROS


    Beteiligte:


    Erscheinungsdatum :

    08.10.2022


    Format / Umfang :

    2497128 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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