Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data
Authors
Keywords
-
Journal
JOURNAL OF ADVANCED TRANSPORTATION
Volume 2019, Issue -, Pages 1-11
Publisher
Hindawi Limited
Online
2019-01-24
DOI
10.1155/2019/9085238
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Three-Phases Smartphone-Based Warning System to Protect Vulnerable Road Users Under Fuzzy Conditions
- (2018) Roya Bastani Zadeh et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- A context aware system for driving style evaluation by an ensemble learning on smartphone sensors data
- (2018) Mohammad Mahdi Bejani et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary
- (2017) Johan Wahlstrom et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Estimation of driving style in naturalistic highway traffic using maneuver transition probabilities
- (2017) Guofa Li et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Stability and Scalability of Homogeneous Vehicular Platoon: Study on the Influence of Information Flow Topologies
- (2016) Yang Zheng et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Driver Distraction Detection Using Semi-Supervised Machine Learning
- (2016) Tianchi Liu et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- k-Nearest Neighbor Model for Multiple-Time-Step Prediction of Short-Term Traffic Condition
- (2016) Bin Yu et al. JOURNAL OF TRANSPORTATION ENGINEERING
- A Novel Model-Based Driving Behavior Recognition System Using Motion Sensors
- (2016) Minglin Wu et al. SENSORS
- Integrated solution for anomalous driving detection based on BeiDou/GPS/IMU measurements
- (2016) Rui Sun et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- An inference engine for smartphones to preprocess data and detect stationary and transportation modes
- (2016) Hamid Reza Eftekhari et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- k-Nearest Neighbor Model for Multiple-Time-Step Prediction of Short-Term Traffic Condition
- (2016) Bin Yu et al. JOURNAL OF TRANSPORTATION ENGINEERING
- Trajectory data reconstruction and simulation-based validation against macroscopic traffic patterns
- (2015) Marcello Montanino et al. TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
- Prediction on building vibration induced by moving train based on support vector machine and wavelet analysis
- (2014) Jinbao Yao et al. Journal of Mechanical Science and Technology
- Three Decades of Driver Assistance Systems: Review and Future Perspectives
- (2014) Klaus Bengler et al. IEEE Intelligent Transportation Systems Magazine
- Automated safety diagnosis of vehicle–bicycle interactions using computer vision analysis
- (2013) Tarek Sayed et al. SAFETY SCIENCE
- Intervehicle Safety Warning Information System for Unsafe Driving Events
- (2013) Cheol Oh et al. TRANSPORTATION RESEARCH RECORD
- Driver Behavior Classification at Intersections and Validation on Large Naturalistic Data Set
- (2012) Georges S. Aoude et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- A Pattern-Recognition Approach for Driving Skill Characterization
- (2010) Yilu Zhang et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Head Pose Estimation and Augmented Reality Tracking: An Integrated System and Evaluation for Monitoring Driver Awareness
- (2010) Erik Murphy-Chutorian et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started