Lane change strategy analysis and recognition for intelligent driving systems based on random forest
出版年份 2021 全文链接
标题
Lane change strategy analysis and recognition for intelligent driving systems based on random forest
作者
关键词
Lane change strategy recognition, Random forest, Support vector machine, Intelligent driving system
出版物
EXPERT SYSTEMS WITH APPLICATIONS
Volume 186, Issue -, Pages 115781
出版商
Elsevier BV
发表日期
2021-08-20
DOI
10.1016/j.eswa.2021.115781
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Research on a Cognitive Distraction Recognition Model for Intelligent Driving Systems Based on Real Vehicle Experiments
- (2020) Qinyu Sun et al. SENSORS
- Prediction of Lane-Changing Maneuvers with Automatic Labeling and Deep Learning
- (2020) Vishal Mahajan et al. TRANSPORTATION RESEARCH RECORD
- Classifying travelers' driving style using basic safety messages generated by connected vehicles: Application of unsupervised machine learning
- (2020) Amin Mohammadnazar et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Continuous Car Driving Intent Detection Using Structural Pattern Recognition
- (2020) Sukhan Lee et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Recognition of lane-changing behaviour with machine learning methods at freeway off-ramps
- (2020) Ting Xu et al. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
- Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data
- (2019) Liang Han et al. Plant Methods
- A Comparison of Random Forest Variable Selection Methods for Classification Prediction Modeling
- (2019) Jaime Lynn Speiser et al. EXPERT SYSTEMS WITH APPLICATIONS
- A Framework for Turning Behavior Classification at Intersections Using 3D LIDAR
- (2019) Mingfang Zhang et al. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
- Naturalistic Driver Intention and Path Prediction Using Recurrent Neural Networks
- (2019) Alex Zyner et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Improving the User Acceptability of Advanced Driver Assistance Systems Based on Different Driving Styles: A Case Study of Lane Change Warning Systems
- (2019) Chang Wang et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Multivariate time series prediction of lane changing behavior using deep neural network
- (2018) Jun Gao et al. APPLIED INTELLIGENCE
- Autonomous vehicle perception: The technology of today and tomorrow
- (2018) Jessica Van Brummelen et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Experimental validation of connected automated vehicle design among human-driven vehicles
- (2018) Jin I. Ge et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Personalized Lane-Change Assistance System with Driver Behavior Identification
- (2018) Bing Zhu et al. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
- Spline-Based Motion Planning for Autonomous Guided Vehicles in a Dynamic Environment
- (2017) Tim Mercy et al. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
- Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques
- (2017) Il-Hwan Kim et al. SENSORS
- Autonomous Vehicle Safety: An Interdisciplinary Challenge
- (2017) Philip Koopman et al. IEEE Intelligent Transportation Systems Magazine
- Random forest in remote sensing: A review of applications and future directions
- (2016) Mariana Belgiu et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Lane changing intention recognition based on speech recognition models
- (2016) Keqiang Li et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Multi-parameter prediction of drivers' lane-changing behaviour with neural network model
- (2015) Jinshuan Peng et al. APPLIED ERGONOMICS
- Personalized Driver/Vehicle Lane Change Models for ADAS
- (2015) Vadim Butakov et al. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
- Predicting driver’s lane-changing decisions using a neural network model
- (2014) Jian Zheng et al. SIMULATION MODELLING PRACTICE AND THEORY
- Using connected vehicle technology to improve the efficiency of intersections
- (2014) S. Ilgin Guler et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Empirical characterization of random forest variable importance measures
- (2007) Kellie J. Archer et al. COMPUTATIONAL STATISTICS & DATA ANALYSIS
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started