# Methods and Tools for Monitoring Driver's Behavior

arXiv: https://arxiv.org/abs/2301.12269 · HTML: https://mlsanity.com/paper/2301.12269/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 8 references that are in the map.
- First posted on arXiv 28 Jan 2023.

## Built on by (most cited first, 2 of 2)

- [Anomalous Behavior Detection in Trajectory Data of Older Drivers](https://mlsanity.com/paper/2311.17822/) (2023)
- [In-vehicle Sensing and Data Analysis for Older Drivers with Mild Cognitive Impairment](https://mlsanity.com/paper/2311.09273/) (2023)

## Abstract (arXiv)

In-vehicle sensing technology has gained tremendous attention due to its ability to support major technological developments, such as connected vehicles and self-driving cars. In-vehicle sensing data are invaluable and important data sources for traffic management systems. In this paper we propose an innovative architecture of unobtrusive in-vehicle sensors and present methods and tools that are used to measure the behavior of drivers. The proposed architecture including methods and tools are used in our NIH project to monitor and identify older drivers with early dementia