# Driver Maneuver Detection and Analysis using Time Series Segmentation and Classification

arXiv: https://arxiv.org/abs/2211.06463 · HTML: https://mlsanity.com/paper/2211.06463/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 49 references that are in the map.
- First posted on arXiv 10 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [MIT Advanced Vehicle Technology Study: Large-Scale Naturalistic Driving Study of Driver Behavior and Interaction with Automation](https://mlsanity.com/paper/1711.06976/) (2017)
- [Mobile Sensing for Multipurpose Applications in Transportation](https://mlsanity.com/paper/2106.10733/) (2021)
- [Multimodal Driver State Modeling through Unsupervised Learning](https://mlsanity.com/paper/2110.01727/) (2021)
- [Travel Time, Distance and Costs Optimization for Paratransit Operations using Graph Convolutional Neural Network](https://mlsanity.com/paper/2205.10507/) (2022)

## Built on by (most cited first, 8 of 8)

- [Real-time Multi-Class Helmet Violation Detection Using Few-Shot Data Sampling Technique and YOLOv8](https://mlsanity.com/paper/2304.08256/) (2023)
- [Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event Analysis](https://mlsanity.com/paper/2404.08229/) (2024)
- [DeepSegmenter: Temporal Action Localization for Detecting Anomalies in Untrimmed Naturalistic Driving Videos](https://mlsanity.com/paper/2304.08261/) (2023)
- [AI-Based Framework for Understanding Car Following Behaviors of Drivers in A Naturalistic Driving Environment](https://mlsanity.com/paper/2301.09315/) (2023)
- [Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learning](https://mlsanity.com/paper/2304.09246/) (2023)
- [SigSegment: A Signal-Based Segmentation Algorithm for Identifying Anomalous Driving Behaviours in Naturalistic Driving Videos](https://mlsanity.com/paper/2304.09247/) (2023)
- [Implementing a GRU Neural Network for Flood Prediction in Ashland City, Tennessee](https://mlsanity.com/paper/2405.10375/) (2024)
- [Behavior-Centric Extraction of Scenarios from Highway Traffic Data and their Domain-Knowledge-Guided Clustering using CVQ-VAE](https://mlsanity.com/paper/2603.16964/) (2026)

## Abstract (arXiv)

The current paper implements a methodology for automatically detecting vehicle maneuvers from vehicle telemetry data under naturalistic driving settings. Previous approaches have treated vehicle maneuver detection as a classification problem, although both time series segmentation and classification are required since input telemetry data is continuous. Our objective is to develop an end-to-end pipeline for frame-by-frame annotation of naturalistic driving studies videos into various driving events including stop and lane keeping events, lane changes, left-right turning movements, and horizontal curve maneuvers. To address the time series segmentation problem, the study developed an Energy Maximization Algorithm (EMA) capable of extracting driving events of varying durations and frequencies from continuous signal data. To reduce overfitting and false alarm rates, heuristic algorithms were used to classify events with highly variable patterns such as stops and lane-keeping. To classify segmented driving events, four machine learning models were implemented, and their accuracy and transferability were assessed over multiple data sources. The duration of events extracted by EMA were comparable to actual events, with accuracies ranging from 59.30% (left lane change) to 85.60% (lane-keeping). Additionally, the overall accuracy of the 1D-convolutional neural network model was 98.99%, followed by the Long-short-term-memory model at 97.75%, then random forest model at 97.71%, and the support vector machine model at 97.65%. These model accuracies where consistent across different data sources. The study concludes that implementing a segmentation-classification pipeline significantly improves both the accuracy for driver maneuver detection and transferability of shallow and deep ML models across diverse datasets.