[go: up one dir, main page]

Koh et al., 2015 - Google Patents

Smartphone-based modeling and detection of aggressiveness reactions in senior drivers

Koh et al., 2015

Document ID
3396991420488730322
Author
Koh D
Kang H
Publication year
Publication venue
2015 IEEE Intelligent Vehicles Symposium (IV)

External Links

Snippet

Reckless driving is one of the leading causes of car accidents. In particular, reckless driving by senior drivers often results in serious consequences due to driver physical fragility. As the population in developed countries is aging, the number of elderly drivers is increasing …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B19/00—Teaching not covered by other main groups of this subclass
    • G09B19/16—Control of vehicles or other craft
    • 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

Similar Documents

Publication Publication Date Title
Koh et al. Smartphone-based modeling and detection of aggressiveness reactions in senior drivers
Zhao et al. An investigation of the relationship between the driving behavior questionnaire and objective measures of highway driving behavior
Helman et al. Validation of the driver behaviour questionnaire using behavioural data from an instrumented vehicle and high-fidelity driving simulator
Carsten et al. Vehicle-based studies of driving in the real world: The hard truth?
Hong et al. A smartphone-based sensing platform to model aggressive driving behaviors
Peng et al. Multi-parameter prediction of drivers' lane-changing behaviour with neural network model
Vhaduri et al. Estimating drivers' stress from GPS traces
Chen et al. Identification of common features of vehicle motion under drowsy/distracted driving: A case study in Wuhan, China
Habibovic et al. Causation mechanisms in car-to-vulnerable road user crashes: Implications for active safety systems
Hill et al. Computer-based hazard perception test scores are associated with the frequency of heavy braking in everyday driving
Simons-Morton et al. Trajectories of kinematic risky driving among novice teenagers
Das et al. Differentiating alcohol-induced driving behavior using steering wheel signals
Underwood On-road behaviour of younger and older novices during the first six months of driving
Vlahodimitrakou et al. Development and evaluation of a Driving Observation Schedule (DOS) to study everyday driving performance of older drivers
Tavakoli et al. HARMONY: A human-centered multimodal driving study in the wild
Tūskė et al. Hazard perception test development for Lithuanian drivers
Wang et al. Predicting drowsy driving in real-time situations: Using an advanced driving simulator, accelerated failure time model, and virtual location-based services
Chee et al. Investigating the driving performance of drivers with and without autism spectrum disorders under complex driving conditions
Fu et al. Variations in naturalistic driving behavior and visual perception at the entrances of short, medium, and long tunnels
Wan et al. Drinking and driving behavior at stop signs and red lights
Hoogendoorn Empirical research and modeling of longitudinal driving behavior under adverse conditions
Han et al. Monitoring Driving in a Monotonous Environment: Classification and Recognition of Driving Fatigue Based on Long Short‐Term Memory Network
Rizzo et al. Driving difficulties in Parkinson's disease
Vasudevan et al. Driver drowsiness monitoring by learning vehicle telemetry data
Scialfa et al. Short-term reliability of a brief hazard perception test