A method may include receiving training data comprising a time series of gaps between an ego vehicle and one or more lead vehicles at a plurality of time steps, embedding the training data into a fixed-length sequence, inputting the fixed-length sequence into a Transformer-RNN model comprising a Transformer component and an RNN component, wherein the transformer component applies attention to each data point of the fixed-length sequence based on a fixed number of previous inputs, and training the Transformer-RNN model, using the training data, to output a predicted gap at a future time step based on an input sequence of gaps.


    Access

    Download


    Export, share and cite



    Title :

    METHOD AND SYSTEM FOR PERSONALIZED CAR FOLLOWING WITH TRANSFORMERS AND RNNS


    Contributors:
    WANG ZIRAN (author) / HAN KYUNGTAE (author) / GUPTA ROHIT (author)

    Publication date :

    2024-04-18


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



    GapFormer: Fast Autoregressive Transformers meet RNNs for Personalized Adaptive Cruise Control

    Sachdeva, Noveen / Wang, Ziran / Han, Kyungtae et al. | IEEE | 2022


    METHOD AND SYSTEM FOR PERSONALIZED CAR FOLLOWING WITH TRANSFORMERS

    WANG ZIRAN / ZHAO ZHOUQIAO / GUPTA ROHIT et al. | European Patent Office | 2023

    Free access

    Multi-Level Contextual RNNs With Attention Model for Scene Labeling

    Fan, Heng / Mei, Xue / Prokhorov, Danil et al. | IEEE | 2018


    Deep Object Tracking on Dynamic Occupancy Grid Maps Using RNNs

    Engel, Nico / Hoermann, Stefan / Henzler, Philipp et al. | IEEE | 2018