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Levenberg–Marquardt –LSTM based Efficient Rear-end Crash Risk Prediction System Optimization

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Abstract

The Almost 1.3 million casualties are reported round a calendar year due to road accidents. Advanced collision avoidance systems play major role in predicting the collision risk to avoid accidents. The existing deep learning algorithms are unable to predict the crash risk efficiently. In the existing system, Long Short Term Memory algorithm is used to predict the crash risk where weights are not optimized. The objective is to predict the rear end collision risk with optimized weight by combining Long Short Term Memory(LSTM) with Levenberg–Marquardt (LM) algorithms. The proposed algorithm predicts the collision risk considering vehicle, driver related factors, and temporal dependencies. Next Generation Simulation Project (NGSIM) dataset is used to evaluate the proposed model. The performance of the proposed system is compared with the performance of Long Short Term Memory and Back Propagation Neural Network. 95.6% of accuracy is achieved by LM-LSTM based Time series Deep Network Model. The prediction accuracy has been improved considerably than the existing algorithms. There is the drastic improvement in minimization of false alarm and missed alarm rate. The main advantage of the proposed system is that it will present warning at the time of high collision risk and it helps drivers to prevent from accident.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Rajiv Gandhi Salai, Chennai, 600119, Tamilnadu, India

    D. Deva Hema & K. Ashok Kumar

  2. Department of Computer Science and Engineering, SRM Institute of Science and Technology, BharathiSalai, Ramapuram, Chennai, 600089, Tamilnadu, India

    D. Deva Hema

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  1. D. Deva Hema
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  2. K. Ashok Kumar
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Correspondence to D. Deva Hema.

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Hema, D.D., Kumar, K.A. Levenberg–Marquardt –LSTM based Efficient Rear-end Crash Risk Prediction System Optimization. Int. J. ITS Res. 20, 132–141 (2022). https://doi.org/10.1007/s13177-021-00273-2

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  • DOI: https://doi.org/10.1007/s13177-021-00273-2

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