Harkous et al., 2018 - Google Patents
Application of hidden Markov model on car sensors for detecting drunk driversHarkous et al., 2018
- Document ID
- 16773938389079231444
- Author
- Harkous H
- Bardawil C
- Artail H
- Daher N
- Publication year
- Publication venue
- 2018 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET)
External Links
Snippet
The ability to detect drunk driving behavior on roadways enhances road safety by significantly reducing the risk of fatal accidents. In this paper, a set of measurements, readily available via on-board vehicle sensors, was selected to detect drunk driving behaviors …
- 230000006399 behavior 0 abstract description 23
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
- G06K9/62—Methods or arrangements for recognition using electronic means
-
- 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
- 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
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
- G07C5/0841—Registering performance data
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Basavaraju et al. | A machine learning approach to road surface anomaly assessment using smartphone sensors | |
Yuan et al. | Using traffic flow characteristics to predict real-time conflict risk: A novel method for trajectory data analysis | |
US10137889B2 (en) | Method for smartphone-based accident detection | |
Aljaafreh et al. | Driving style recognition using fuzzy logic | |
CN109572550B (en) | Driving track prediction method, system, computer equipment and storage medium | |
JP5583338B2 (en) | Operation monitoring method and operation monitoring apparatus | |
Harkous et al. | A two-stage machine learning method for highly-accurate drunk driving detection | |
Xu et al. | Aggressive driving behavior prediction considering driver’s intention based on multivariate-temporal feature data | |
US20090177602A1 (en) | Systems and methods for detecting unsafe conditions | |
US20230237310A1 (en) | Adaptable On-Deployment Learning Platform for Driver Analysis Output Generation | |
Wang et al. | Driving safety monitoring using semisupervised learning on time series data | |
Harkous et al. | Application of hidden Markov model on car sensors for detecting drunk drivers | |
Öztürk et al. | Driver status identification from driving behavior signals | |
Lindow et al. | Driver behavior monitoring based on smartphone sensor data and machine learning methods | |
CN106339692A (en) | Fatigue driving state information determination method based on route offset detection and system | |
Jan et al. | Non-intrusive drowsiness detection techniques and their application in detecting early dementia in older drivers | |
CN112435466B (en) | Method and system for predicting take-over time of CACC vehicle changing into traditional vehicle under mixed traffic flow environment | |
Qiu et al. | Use of triplet-loss function to improve driving anomaly detection using conditional generative adversarial network | |
Wowo et al. | Towards sub-maneuver selection for automated driver identification | |
CN113435239A (en) | Rationality checking of outputs of neural classifier networks | |
Taherifard et al. | Machine learning-driven event characterization under scarce vehicular sensing data | |
Peng et al. | A Method for Vehicle Collision Risk Assessment through Inferring Driver's Braking Actions in Near-Crash Situations | |
Boateng et al. | Abnormal driving detection using GPS data | |
Zhang et al. | Detecting driver distractions using a deep learning approach and multi-source naturalistic driving data | |
Menendez et al. | Detecting and Predicting Smart Car Collisions in Hybrid Environments from Sensor Data |