Predicting the speed of a vehicle for a future point on the road ahead is an important subtask of many advanced safety systems. We propose a two-stage neural net approach: first, (a small number of) characteristics of the overall speed distribution at a given location are estimated from road features alone. Second, for the case of a particular trip the speed at the current location, together with the speed characteristics output by the first stage for both the current and a future location, is used to predict the speed at the latter. Our approach parallels the previous empirical constant-percentile approach. It achieves nearly the same predictive accuracy, while at the same time reduces the data requirement to a feasible amount and additionally is able to generalize to extreme speeds not previously seen in the training set.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Predicting driving speed using neural networks


    Contributors:


    Publication date :

    2003-01-01


    Size :

    407225 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Predicting Driving Speed Using Neural Networks

    Schroedl, S. / Zhang, W. / IEEE | British Library Conference Proceedings | 2003


    Neural Learning of Predicting Driving Environment

    Y. L. Murphey / Z. Chan / L. Kiliaris et al. | NTIS | 2008


    Predicting Airport Capacities Using Neural Networks

    Tolley, Benjamin / Jones, James | IEEE | 2024


    Self Driving Car using Neural Networks

    Kalathil, Divit / Mandal, Vivek Kumar / Gune, Archis et al. | IEEE | 2022


    Predicting Appropriate Speed for Driving Based on External Factors

    Bhope, Adwait / Zanpure, Aashay / Kamat, Vinod et al. | Springer Verlag | 2022