Abstract
In the field of smart urban traffic networks, Vehicle Trajectory Prediction or VTP is a crucial research field in the area of driving assistance and autonomous vehicles. A vehicle can be autonomous or manual; trajectory prediction involves future location and turns (Left or Right). Unlike the pedestrian trajectory prediction problem, Vehicle Trajectory Prediction is more complex. The trajectory prediction problem involves the human decision-making process and many factors affecting the decision for predicting the trajectory of the vehicle. Therefore, the nature of the problem is non-linear and comes under classification or regression problems. Recent developments in the field of artificial intelligence models of machine learning and deep learning, have made researchers provide promising solutions to the problem in different traffic situations. In this paper, firstly, we are providing the taxonomy of popular existing machine learning and deep learning models that have been used to solve the problem of vehicle trajectory prediction so far. Further, a discussion of some existing deep learning models follows. Secondly, we are listing some public datasets for the study of vehicle trajectory prediction and different performance metrics used to measure the performance of the models by researchers. Finally, a discussion on the limitations of the deep learning models is presented. After reading this paper, one can start their initial research in the area of vehicle trajectory prediction.
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Manish, Dohare, U., Kumar, S. (2023). A Survey of Vehicle Trajectory Prediction Based on Deep Learning Models. In: Shakya, S., Balas, V.E., Haoxiang, W. (eds) Proceedings of Third International Conference on Sustainable Expert Systems . Lecture Notes in Networks and Systems, vol 587. Springer, Singapore. https://doi.org/10.1007/978-981-19-7874-6_48
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DOI: https://doi.org/10.1007/978-981-19-7874-6_48
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