short-paper

Analysis and evaluation of driving behavior recognition based on a 3-axis accelerometer using a random forest approach: poster abstract

Authors:

Xin Lin,

Lin ZhangAuthors Info & Claims

IPSN '17: Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks

Pages 303 - 304

https://doi.org/10.1145/3055031.3055060

Published: 18 April 2017 Publication History

Get Access

Abstract

Understanding human drivers' behavior is critical for the self-driving cars, and has been intensively studied in the past decade. We exploit the widely available camera and motion sensor data from car recorders, and propose a hybrid method of recognizing driving events based on the random forest approach. The classification results are analyzed by comparing different features, classifiers and filters. A high accuracy of 98.1% on driving behavior classification is obtained and the robustness is verified on a dataset including 2400 driving events.

References

[1]

Chuang-Wen You, Nicholas D. Lane, Fanglin Chen, Rui Wang, Zhenyu Chen, Thomas J. Bao, Martha Montes-de Oca, Yuting Cheng, Mu Lin, Lorenzo Torresani, and Andrew T Campbell. Carsafe app: Alerting drowsy and distracted drivers using dual cameras on smartphones. In Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services, pages 461--462. ACM, June 2013.

Digital Library

Google Scholar

[2]

L. M. Bergasa, D. Almera, J. Almazn, J. J. Yebes, and R. Arroyo. Drivesafe: An app for alerting inattentive drivers and scoring driving behaviors. In 2014 IEEE Intelligent Vehicles Symposium Proceedings, pages 240--245. IEEE, June 2014.

Crossref

Google Scholar

[3]

M. Wu, S. Zhang, and Y. Dong. A novel model-based driving behavior recognition system using motion sensors. Sensors., 16(10):1746, October 2016.

Crossref

Google Scholar

[4]

D. A. Johnson and M. M. Trivedi. Driving style recognition using a smartphone as a sensor platform. In 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pages 1609--1615. IEEE, October 2011.

Crossref

Google Scholar

[5]

A. Sathyanarayana, S. O. Sadjadi, and J. H. L. Hansen. Leveraging sensor information from portable devices towards automatic driving maneuver recognition. In 2012 15th International IEEE Conference on Intelligent Transportation Systems, pages 660--665. IEEE, September 2012.

Crossref

Google Scholar

[6]

S. J. Preece*, J. Y. Goulermas, L. P. J. Kenney, and D. Howard. A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Transactions on Biomedical Engineering., 56(3):871--879, March 2009.

Crossref

Google Scholar

Cited By

View all

Wei LLiang LLei TYin XWang YGao MLiu Y(2023)On-Board Unit (OBU)-Supported Longitudinal Driving Behavior Monitoring Using Machine Learning ApproachesSensors10.3390/s2315670823:15(6708)Online publication date: 27-Jul-2023
https://doi.org/10.3390/s23156708
Psaraftis KAnagnostopoulos TNtalianis K(2022)An innovative quality lane change evaluation scheme based on reliable crowd-ratingsComputer Science and Information Systems10.2298/CSIS210830030P19:3(1093-1114)Online publication date: 2022
https://doi.org/10.2298/CSIS210830030P
He XZhang ZXu LYu J(2022)Efficient normalization for quantitative evaluation of the driving behavior using a gated auto-encoder基于门控自编码器的驾驶行为量化评价标准化策略Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.200066723:3(452-462)Online publication date: 7-Mar-2022
https://doi.org/10.1631/FITEE.2000667
Show More Cited By

Index Terms

Analysis and evaluation of driving behavior recognition based on a 3-axis accelerometer using a random forest approach: poster abstract

Recommendations

The Exploration of Autonomous Vehicle Driving Styles: Preferred Longitudinal, Lateral, and Vertical Accelerations
Automotive'UI 16: Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

This paper describes a new approach in exploring preferred driving styles for autonomous vehicles through simulation of autonomous driving in real road conditions. A Wizard experiment with an equipped car was conducted to investigate the preferences of ...
Numerical Analysis of Tractor Accidents using Driving Simulator for Autonomous Driving Tractor
ICMRE'19: Proceedings of the 5th International Conference on Mechatronics and Robotics Engineering

Autonomous driving of automobiles is a hot research topic in recent years. The autonomous driving tractor also has been studied in the agricultural field as well as an autonomous driving automobile. On the other hand, tractor accidents frequently occur ...
Lane change strategy analysis and recognition for intelligent driving systems based on random forest
Highlights
- A recognition model of lane change strategy was established based on random forest.
- On-road experiments were executed to collect the three kinds of lane change strategy.
- Recognition accuracy under 3 input parameters was 88.78% with ...
Abstract
The development of intelligent connected technology provides a platform for multidimensional information interaction and makes the recognition of a specific lane change strategy become a reality. Intelligent driving systems cannot make human-like ...

Comments

Information & Contributors

Information

Published In

IPSN '17: Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks

April 2017

333 pages

ISBN:9781450348904

DOI:10.1145/3055031

General Chair:
Pei Zhang
Carnegie Mellon University
,
Program Chairs:
Prabal Dutta
University of California, Berkeley
,
Guoliang Xing
Michigan State University

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 April 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

Shenzhen Municipal Development and Reform Commission,Shenzhen Engineering Laboratory for Data Science and Information Technology

Conference

IPSN '17

Sponsor:

SIGBED

IPSN '17: The 16th International Conference on Information Processing in Sensor Networks

April 18 - 20, 2017

Pennsylvania, Pittsburgh

Acceptance Rates

Overall Acceptance Rate 143 of 593 submissions, 24%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

16
Total Citations
View Citations
276
Total Downloads

Downloads (Last 12 months)8
Downloads (Last 6 weeks)5

Reflects downloads up to 25 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Wei LLiang LLei TYin XWang YGao MLiu Y(2023)On-Board Unit (OBU)-Supported Longitudinal Driving Behavior Monitoring Using Machine Learning ApproachesSensors10.3390/s2315670823:15(6708)Online publication date: 27-Jul-2023
https://doi.org/10.3390/s23156708
Psaraftis KAnagnostopoulos TNtalianis K(2022)An innovative quality lane change evaluation scheme based on reliable crowd-ratingsComputer Science and Information Systems10.2298/CSIS210830030P19:3(1093-1114)Online publication date: 2022
https://doi.org/10.2298/CSIS210830030P
He XZhang ZXu LYu J(2022)Efficient normalization for quantitative evaluation of the driving behavior using a gated auto-encoder基于门控自编码器的驾驶行为量化评价标准化策略Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.200066723:3(452-462)Online publication date: 7-Mar-2022
https://doi.org/10.1631/FITEE.2000667
Deng QSoffker D(2022)A Review of HMM-Based Approaches of Driving Behaviors Recognition and PredictionIEEE Transactions on Intelligent Vehicles10.1109/TIV.2021.30659337:1(21-31)Online publication date: Mar-2022
https://doi.org/10.1109/TIV.2021.3065933
Khosravi EHemmatyar ASiavoshani MMoshiri B(2022)Safe Deep Driving Behavior Detection (S3D)IEEE Access10.1109/ACCESS.2022.321764410(113827-113838)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3217644
Zaidan RAlamoodi AZaidan BZaidan AAlbahri OTalal MGarfan SSulaiman SMohammed AKareem ZMalik RAmeen H(2022)Comprehensive driver behaviour review: Taxonomy, issues and challenges, motivations and research direction towards achieving a smart transportation environmentEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.104745111(104745)Online publication date: May-2022
https://doi.org/10.1016/j.engappai.2022.104745
Đorđević BMitić S(2021)The possibility of traffic accident reconstruction using event data Recorders: A reviewIndustrija10.5937/industrija49-3597449:3-4(99-115)Online publication date: 2021
https://doi.org/10.5937/industrija49-35974
Zhou DLiu HMa HWang XZhang XDong Y(2021)Driving Behavior Prediction Considering Cognitive Prior and Driving ContextIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.297375122:5(2669-2678)Online publication date: May-2021
https://doi.org/10.1109/TITS.2020.2973751
Lin HWu Z(2021)Development of Automatic Gear Shifting for Bicycle Riding Based on Physiological Information and Environment SensingIEEE Sensors Journal10.1109/JSEN.2021.311618121:21(24591-24600)Online publication date: 1-Nov-2021
https://doi.org/10.1109/JSEN.2021.3116181
Deng QWang JHillebrand KBenjamin CSoffker D(2020)Prediction Performance of Lane Changing Behaviors: A Study of Combining Environmental and Eye-Tracking Data in a Driving SimulatorIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2019.293728721:8(3561-3570)Online publication date: Aug-2020
https://doi.org/10.1109/TITS.2019.2937287
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

The Exploration of Autonomous Vehicle Driving Styles: Preferred Longitudinal, Lateral, and Vertical Accelerations

Numerical Analysis of Tractor Accidents using Driving Simulator for Autonomous Driving Tractor

Lane change strategy analysis and recognition for intelligent driving systems based on random forest

Comments

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Acceptance Rates

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Index Terms

Recommendations

The Exploration of Autonomous Vehicle Driving Styles: Preferred Longitudinal, Lateral, and Vertical Accelerations

Numerical Analysis of Tractor Accidents using Driving Simulator for Autonomous Driving Tractor

Lane change strategy analysis and recognition for intelligent driving systems based on random forest

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations