[go: up one dir, main page]

Halim et al., 2016 - Google Patents

Artificial intelligence techniques for driving safety and vehicle crash prediction

Halim et al., 2016

Document ID
5978044122909436117
Author
Halim Z
Kalsoom R
Bashir S
Abbas G
Publication year
Publication venue
Artificial Intelligence Review

External Links

Snippet

Accident prediction is one of the most critical aspects of road safety, whereby an accident can be predicted before it actually occurs and precautionary measures taken to avoid it. For this purpose, accident prediction models are popular in road safety analysis. Artificial …
Continue reading at link.springer.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/20—Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal operating condition and not elsewhere provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00362—Recognising human body or animal bodies, e.g. vehicle occupant, pedestrian; Recognising body parts, e.g. hand
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
Halim et al. Artificial intelligence techniques for driving safety and vehicle crash prediction
Azadani et al. Driving behavior analysis guidelines for intelligent transportation systems
Bouhsissin et al. Driver behavior classification: a systematic literature review
Xue et al. Rapid Driving Style Recognition in Car‐Following Using Machine Learning and Vehicle Trajectory Data
Khodairy et al. Driving behavior classification based on oversampled signals of smartphone embedded sensors using an optimized stacked-LSTM neural networks
Yi et al. Implicit personalization in driving assistance: State-of-the-art and open issues
Shirazi et al. Looking at intersections: a survey of intersection monitoring, behavior and safety analysis of recent studies
Meiring et al. A review of intelligent driving style analysis systems and related artificial intelligence algorithms
EP2758879B1 (en) A computing platform for development and deployment of sensor-driven vehicle telemetry applications and services
Rettore et al. Vehicular data space: The data point of view
Alluhaibi et al. Driver behavior detection techniques: A survey
Wang et al. Driving safety monitoring using semisupervised learning on time series data
Wu et al. Towards human-vehicle interaction: Driving risk analysis under different driver vigilance states and driving risk detection method
Wan et al. Driving anger states detection based on incremental association markov blanket and least square support vector machine
Zinebi et al. Driver behavior analysis methods: Applications oriented study
Lee et al. A privacy-preserving learning method for analyzing hev driver’s driving behaviors
Butt et al. REEEC-AGENT: human driver cognition and emotions-inspired rear-end collision avoidance method for autonomous vehicles
Zardosht et al. Identifying Driver Behavior in Preturning Maneuvers Using In‐Vehicle CANbus Signals
Sahoo et al. Two layered gated recurrent stacked long short-term memory networks for driver’s behavior analysis
Soultana et al. Context-awareness in the smart car: Study and analysis
Hou et al. Research progress of dangerous driving behavior recognition methods based on deep learning
Nasr Azadani Driving Behavior Analysis and Prediction for Safe Autonomous Vehicles
Herke et al. Self-Driving Vehicles and Their Impact on the European Convention on Human Rights
Lashkov et al. A multimodal approach to psycho-emotional state detection of a vehicle driver
Fasanmade et al. Context-aware quantitative risk assessment machine learning model for drivers distraction