Adaptive cruise control has been an important function in modern vehicles, and has proven to be helpful for assisted driving. The main challenges involve accurate gap prediction between the ego and preceding vehicles, as well as personalizing the driving behaviour for different kinds of drivers and/or cars. Correspondingly, in this paper, we make the following contributions: (1) we propose GAPFoRMER which combines the Transformer and RNN architectures to better model and personalize driving behaviour; (2) make necessary modifications to the Transformer attention mechanism for scaling to long driving contexts in a resource-efficient manner; and (3) propose an architecture-agnostic model training regime, Horizon which improves generalization by incorporating a time-horizon and makes the models more accurate and robust. Detailed experiments on both public and proprietary datasets demonstrate that GAPFoRMER can be up to 50% more accurate when compared to other ACC baselines, demonstrating its efficacy and potential for real-world application.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:


    Publication date :

    2022-10-08


    Size :

    1252274 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Method and system for personalized car following with transformers and RNNs

    WANG ZIRAN / HAN KYUNGTAE / GUPTA ROHIT | European Patent Office | 2025

    Free access

    METHOD AND SYSTEM FOR PERSONALIZED CAR FOLLOWING WITH TRANSFORMERS AND RNNS

    WANG ZIRAN / HAN KYUNGTAE / GUPTA ROHIT | European Patent Office | 2024

    Free access

    Personalized self-adaptive cruise control system

    HE RUI / YANG NINGNING / ZHANG SUMIN | European Patent Office | 2020

    Free access

    Personalized Adaptive Cruise Control Considering Drivers' Characteristics

    Su, Chen / Deng, Weiwen / He, Rui et al. | British Library Conference Proceedings | 2018


    Capacity implications of personalized adaptive cruise control

    Shang, Mingfeng / Wang, Shian / Stern, Raphael | IEEE | 2023