Abstract
Autonomous ground vehicles (AGVs) play a significant role in performing the versatile task of replacing human-operated vehicles and improving vehicular traffic. This facilitates the advancement of an independent and interdependent decision-making process that increases the accessibility of transportation by reducing accidents and congestion. Presently, human-machine cooperation has focused on developing advanced algorithms for intelligent path planning and execution that is functional in providing reliable transportation. From industry simulations to field tests, AGVs exhibited various mishaps or errors that have a probability to cause fatalities and undermine the potential benefits. Therefore, it is very important to focus on reducing fatalities due to either human error or AGV system error. To solve this problem, the paper proposes an error prediction model to reduce AGV errors through appropriate human intervention. In this paper, we use the data from AGV exteroceptive sensors such as stereo-vision cameras, long and short range RADARS, and LiDAR to predict the AGVs error through Dempster–Shafer theory (DST) based on sensor data fusion technique. The results obtained in this work suggest that there is a lot of scope for improvement in the performance of AGV when conflicts are predicted in advance and alerting human for intervention. This would, in turn, improve human-machine cooperation.
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Acknowledgments
The University of Toledo and Round 1 Award from the Ohio Federal Research Jobs Commission (OFMJC) through Ohio Federal Research Network (OFRN) fund this research project; authors also appreciate support of the Paul A. Homer Family CSTAR (Cybersecurity and Teaming Research) Lab and EECS (Electrical Engineering and Computer Science) Department at the University of Toledo.
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Damacharla, P., Junuthula, R.R., Javaid, A.Y., Devabhaktuni, V.K. (2019). Autonomous Ground Vehicle Error Prediction Modeling to Facilitate Human-Machine Cooperation. In: Chen, J. (eds) Advances in Human Factors in Robots and Unmanned Systems. AHFE 2018. Advances in Intelligent Systems and Computing, vol 784. Springer, Cham. https://doi.org/10.1007/978-3-319-94346-6_4
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DOI: https://doi.org/10.1007/978-3-319-94346-6_4
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