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Deep Learning Based Decision Support Framework for Dead Reckoning in Emergency Vehicle Preemption

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Abstract

Dead reckoning, within the realm of emergency vehicle preemption, entails the art of deducing the present position and course of an emergency vehicle (EV), such as a police cruiser, ambulance, or fire engine. This deduction is rooted in prior knowledge and measurements, particularly vital when the accuracy of the Inertial Measurement Unit (IMU) is susceptible to decline amidst challenges like urban canyons, tunnels, or inclement weather. To surmount this challenge, we introduced a technique for estimating the EV’s position, termed “dead reckoning,” which leverages a deep neural network (DNN) in conjunction with an Inertial Measurement Unit (DNN-IMU). This self-contained system equips EVs with reliable navigation capabilities. In our initial phase, we designated six test routes, recording velocity, attitude (pitch and roll angle), and position data before integrating them with the DNN-IMU. These datasets underwent comprehensive training and testing. Throughout this process, we gauged the performance of the DNN-IMU using four key performance metrics, contrasting its effectiveness with prevailing methods. Simulation outcomes strongly suggest the efficacy of our proposed DNN-IMU across all six test routes. Notably, when tested in two different routes under GPS outage conditions, our method outperformed others, yielding significantly greater accuracy (92.45% for trajectory-1 and 93.62% for trajectory-2).

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

EVs:

Emergency vehicles

DNN:

Deep neural network

DNN-IMU:

Deep neural network and an extended Kalman filter-based Inertial Measurement Unit

EVP:

Emergency Vehicle Preemption

IR-EVP:

Infrared based EVP

GPS:

Global Positioning Systems

SMS:

Short message service

GNSS:

Global navigation satellite system

ILP:

Integer linear programming

GTT:

Ground-truth trajectories

RMSE:

Root means square error

MAE:

Mean absolute error

CD:

Coefficient of determination

ECM:

Efficiency coefficient method

OI:

Overall index

GPSO:

GPS outage

ENU:

East–north–upward

LSTM:

Long-Short Term Memory

FCL:

Fully connected lay

NLL:

Negative log-likelihood

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Prasad V Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, 520007, India

    C. Subba Rao

  2. Department of Electronics and Communication Engineering, SRM TRP Engineering College, Tiruchirappalli, 621105, India

    C. Chellaswamy

  3. Department of Electronics and Communication Engineering, S.A. Engineering College, Chennai, 600077, India

    T. S. Geetha

  4. Department of Electronics and Communication Engineering, Jeppiaar Institute of Technology, Chennai, 631604, India

    S. Arul

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Correspondence to C. Chellaswamy.

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Subba Rao, C., Chellaswamy, C., Geetha, T.S. et al. Deep Learning Based Decision Support Framework for Dead Reckoning in Emergency Vehicle Preemption. Int. J. ITS Res. 22, 117–135 (2024). https://doi.org/10.1007/s13177-023-00384-y

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