Gabor/PCA/SVM-Based Face Detection for Driver’s Monitoring

Benrachou, Djamel Eddine; Boulebtateche, Brahim; Bensaoula, Salah

doi:10.12720/joace.1.2.115-118

Cited by 4 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5 Mutual information measures the mutual dependence between two variables. [162], independent component analysis (ICA) [163], linear discriminant analysis [164], [165], autoencoder [166], etc. can be employed to reduce the high dimensionality of feature space to a reduced dimensionality feature space.…”

Section: Data-driven Behavior Modelsmentioning

confidence: 99%

Driver Behavior Modeling Toward Autonomous Vehicles: Comprehensive Review

Negash

Yang

2023

IEEE Access

View full text Add to dashboard Cite

Driver behavior models have been used as input to self-coaching, accident prevention studies, and developing driver-assisting systems. In recent years, driver behavior recognition has revolutionized autonomous vehicles (AVs) and traffic management studies. This comprehensive survey provides an up-todate review of the different driver behavior models and modeling approaches. In heterogeneous streets where humans and autonomous vehicles operate simultaneously, predicting the intent and action of human drivers is crucial for AVs with the help of wireless communication and artificial intelligence (AI) technologies. Therefore, the review also summarizes the applications of driver behavior modeling (DBM) for effective behavior recognition and human-like AV driving. Moreover, the review also covers the application of DBM in capturing behaviors of complex dynamic driving tasks. In this review, we solely cover car-following (CF) and lane-changing (LC) maneuvers.INDEX TERMS Driver behavior modeling (DBM), autonomous vehicles, behavior recognition, human-like, car-following, lane-changing, perception, decision-making, data-driven, vehicle control. I. INTRODUCTIONIn 2018, the US Department of Transportation reported 6,734,000 motor vehicle-related accidents. 1 Among these accidents, there were 2.71 million injuries and 36,560 fatalities. These deaths and injuries also increased in the years 2019 and 2020. Earlier in 2015, specifically in the US, 80 − 94% of crashes were due to driver-related errors, and 33% were due to the wrong prediction of other drivers [1]. The statistics of accidents caused by human driver-related errors were also reported in [2] and [3].The accidents and their behind reasons will lead us back to human drivers with rich and diverse behaviors. According to Michon [4], a human as a problem solver can be a road user, a transportation consumer, a social agent, and a psycho-biological organism. The issues to be solved are controlling a vehicle, trip-making, communication, and satisfying the basic driving necessity. A human driver can have unique traits towards vehicle controlThe associate editor coordinating the review of this manuscript and approving it for publication was Divanilson Rodrigo Campelo .

show abstract