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Dynamic and Systematic Survey of Deep Learning Approaches for Driving Behavior Analysis

arXiv 2109.08996 ↗First posted 18 Sep 2021cs.LG cs.AI cs.DBcited by 0

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Abstract (arXiv)

Improper driving results in fatalities, damages, increased energy consumptions, and depreciation of the vehicles. Analyzing driving behaviour could lead to optimize and avoid mentioned issues. By identifying the type of driving and mapping them to the consequences of that type of driving, we can get a model to prevent them. In this regard, we try to create a dynamic survey paper to review and present driving behaviour survey data for future researchers in our research. By analyzing 58 articles, we attempt to classify standard methods and provide a framework for future articles to be examined and studied in different dashboards and updated about trends.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 88 references that are in the map. First posted on arXiv 18 Sep 2021.

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4 of 88 references.

References · 4

  1. 2016Characterizing Driving Styles with Deep Learningcited by 13
  2. 2019Recurrent Neural Networks: An Embedded Computing Perspectivecited by 9
  3. 2019Deep Reinforcement Learning Based High-level Driving Behavior Decision-making Model in Heterogeneous Trafficcited by 5
  4. 2018Spatial-temporal Fusion Convolutional Neural Network for Simulated Driving Behavior Recognitioncited by 1

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