Behavior analysis of vehicles surrounding the egovehicle is an essential component in safe and pleasant autonomous driving. This study develops a framework for activity classification of observed on-road vehicles using 3D trajectory cues and a Long Short Term Memory (LSTM) model. As a case study, we aim to classify maneuvers of surrounding vehicles at four way intersections. LIDAR, GPS, and IMU measurements are used to extract ego-motion compensated surround trajectories from data clips in the KITTI benchmark. The impact of different prediction label space choices, feature space input, noisy/missing trajectory data, and LSTM model architectures are analyzed, presenting the strengths and limitations of the proposed approach.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Surround vehicles trajectory analysis with recurrent neural networks


    Contributors:


    Publication date :

    2016-11-01


    Size :

    951755 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Encoding Bird's Trajectory using Recurrent Neural Networks

    Ardakani, Ilya S. / Hashimoto, Koichi | British Library Conference Proceedings | 2017


    SURROUND MONITORING SYSTEM FOR VEHICLES

    ASHLEY JONATHAN DAVID | European Patent Office | 2019

    Free access

    Surround monitoring system for vehicles

    ASHLEY JONATHAN DAVID | European Patent Office | 2021

    Free access

    Relational Recurrent Neural Networks For Vehicle Trajectory Prediction

    Messaoud, Kaouther / Yahiaoui, Itheri / Verroust-Blondet, Anne et al. | IEEE | 2019


    Vehicle Trajectory Prediction based on LSTM Recurrent Neural Networks

    Ip, Andre / Irio, Luis / Oliveira, Rodolfo | IEEE | 2021