Identifying Inattentive and Aggressive Driving Behavior in Human Drivers Through Deep Learning: Recent Developments, Necessities and Ongoing Challenges

The increase in intelligent transport systems has stimulated a rising curiosity in applying deep learning to identify distracted and hostile driving actions. This type of conduct continues to be a major factor in car crashes, resulting in significant social and economic consequences. This article examines recent progress in utilizing deep learning methods for identifying distracted and hostile driving behaviors. Moreover, it emphasizes the need for certain technical and environmental prerequisites for successful execution, such as acquiring data, hardware, and software specifications. In conclusion, we investigate the unresolved issues like issues related to data privacy, the ability to interpret deep learning models, and differences in driver behaviors.