[go: up one dir, main page]

Li et al., 2019 - Google Patents

Effects of feature selection on lane-change maneuver recognition: An analysis of naturalistic driving data

Li et al., 2019

View HTML @Full View
Document ID
5180703232344173880
Author
Li X
Wang W
Zhang Z
Rötting M
Publication year
Publication venue
Journal of intelligent and connected vehicles

External Links

Snippet

Purpose Feature selection is crucial for machine learning to recognize lane-change (LC) maneuver as there exist a large number of feature candidates. Blindly using feature could take up large storage and excessive computation time, while insufficient feature selection …
Continue reading at www.emerald.com (HTML) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/16Anti-collision systems
    • G08G1/167Driving aids for lane monitoring, lane changing, e.g. blind spot detection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass

Similar Documents

Publication Publication Date Title
Xue et al. Rapid Driving Style Recognition in Car‐Following Using Machine Learning and Vehicle Trajectory Data
Wang et al. A learning-based approach for lane departure warning systems with a personalized driver model
Xiong et al. A new framework of vehicle collision prediction by combining SVM and HMM
Aoude et al. Behavior classification algorithms at intersections and validation using naturalistic data
Lyu et al. Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions
Kumar et al. Learning-based approach for online lane change intention prediction
Zhao et al. Identification of driver’s braking intention based on a hybrid model of GHMM and GGAP-RBFNN
Jahangiri et al. Adopting machine learning methods to predict red-light running violations
Elhenawy et al. Modeling driver stop/run behavior at the onset of a yellow indication considering driver run tendency and roadway surface conditions
Bolovinou et al. Driving style recognition for co-operative driving: A survey
Li et al. Effects of feature selection on lane-change maneuver recognition: An analysis of naturalistic driving data
Khakzar et al. Driver influence on vehicle trajectory prediction
Teimouri et al. A real-time warning system for rear-end collision based on random forest classifier
Peng et al. Rough set based method for vehicle collision risk assessment through inferring driver's braking actions in near-crash situations
Phan et al. Estimation of driver awareness of pedestrian based on Hidden Markov Model
Zhu et al. Real-time crash identification using connected electric vehicle operation data
Chang et al. Exploring contributing factors of hazardous events in construction zones using naturalistic driving study data
Jeong et al. Detection of lateral hazardous driving events using in-vehicle gyro sensor data
Zardosht et al. Identifying Driver Behavior in Preturning Maneuvers Using In‐Vehicle CANbus Signals
Katrakazas Developing an advanced collision risk model for autonomous vehicles
Xu et al. Detecting critical mismatched driver visual attention during lane change: An embedding kernel algorithm
Scanlon Evaluating the Potential of an Intersection Driver Assistance System to Prevent US Intersection Crashes
Rendon-Velez et al. Progress with situation assessment and risk prediction in advanced driver assistance systems: A survey
Amditis et al. System architecture of a driver's monitoring and hypovigilance warning system
Daniel et al. Driving risk assessment with belief functions