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Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges

机译：深度学习模型用于自动驾驶汽车的交通流量预测：回顾，解决方案和挑战

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摘要

In the last few years, there has been an exponential increase in the usage of the autonomous vehicles across the globe. It is due to an exponential increase in the popularity and usage of the artificial intelligence techniques in various applications. Traffic flow predication is important for autonomous vehicles using which they decide their itinerary and take adaptive decisions (for example, turn let or right, move straight, lane change, stop, or accelerate) with respect to their surrounding objects. From the existing literature, it has been observed that research on autonomous vehicles has shifted from the traditional statistical models to adaptive machine learning techniques. However, existing machine learning models may not be directly applicable in this environment due to non-linear complex relationship between spatial and temporal data collected from the surroundings during the aforementioned adaptive decisions taken by the vehicles. So, with focus on these issues, in this article, we explore various deep learning models for traffic flow prediction in autonomous vehicles and compared these models with respect to their applicability in modern smart transportation systems. Various parameters are chosen to have a relative comparison among different deep learning models. Moreover, challenges and future research directions are also discussed in the article. (c) 2019 Elsevier Inc. All rights reserved.
机译：在过去的几年中，全球自动驾驶汽车的使用呈指数增长。这是由于人工智能技术在各种应用中的普及和使用呈指数增长。交通流量预测对于自动驾驶车辆来说非常重要，因为自动驾驶车辆可以根据其周围物体来决定自己的行程并做出适应性决定（例如，转弯或向右转，直行，换道，停车或加速）。从现有文献中可以看出，对自动驾驶汽车的研究已经从传统的统计模型转向了自适应机器学习技术。但是，由于在上述车辆做出的自适应决策期间从周围环境收集的空间和时间数据之间存在非线性复杂关系，因此现有的机器学习模型可能无法直接应用于此环境。因此，针对这些问题，在本文中，我们探索了用于自动驾驶汽车交通流量预测的各种深度学习模型，并将这些模型与它们在现代智能交通系统中的适用性进行了比较。选择各种参数以在不同的深度学习模型之间进行相对比较。此外，本文还讨论了挑战和未来的研究方向。 （c）2019 Elsevier Inc.保留所有权利。

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