Independent Component Analysis.

Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

In this paper, we review the state-of-the-art technologies for driver inattention monitoring, which can be classified into the following two main categories: 1) distraction and 2) fatigue. Driver inattention is a major factor in most traffic accidents. Research and development has actively been carried out for decades, with the goal of precisely determining the drivers' state of mind. In this paper, we summarize these approaches by dividing them into the following five different types of measures: 1) subjective report measures; 2) driver biological measures; 3) driver physical measures; 4) driving performance measures; and 5) hybrid measures. Among these approaches, subjective report measures and driver biological measures are not suitable under real driving conditions but could serve as some rough ground-truth indicators. The hybrid measures are believed to give more reliable solutions compared with single driver physical measures or driving performance measures, because the hybrid measures minimize the number of false alarms and maintain a high recognition rate, which promote the acceptance of the system. We also discuss some nonlinear modeling techniques commonly used in the literature.

Driver Inattention Monitoring System for Intelligent Vehicles: A Review

Driver driving style plays an important role in vehicle energy management as well as driving safety. Furthermore, it is key for advance driver assistance systems development, toward increasing levels of vehicle automation. This fact has motivated numerous research and development efforts on driving style identification and classification. This paper provides a survey on driving style characterization and recognition revising a variety of algorithms, with particular emphasis on machine learning approaches based on current and future trends. Applications of driving style recognition to intelligent vehicle controls are also briefly discussed, including experts’ predictions of the future development.

/pdf/driving-style-recognition-for-intelligent-vehicle-control-513cyzuxpi.pdf

Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey

http://www.cefala.org/~hani/Papers/hy_evb_spcom1998.pdf

Quantitative association of vocal-tract and facial behavior

All drivers have habits behind the wheel. Different drivers vary in how they hit the gas and brake pedals, how they turn the steering wheel, and how much following distance they keep to follow a vehicle safely and comfortably. In this paper, we model such driving behaviors as car-following and pedal operation patterns. The relationship between following distance and velocity mapped into a two-dimensional space is modeled for each driver with an optimal velocity model approximated by a nonlinear function or with a statistical method of a Gaussian mixture model (GMM). Pedal operation patterns are also modeled with GMMs that represent the distributions of raw pedal operation signals or spectral features extracted through spectral analysis of the raw pedal operation signals. The driver models are evaluated in driver identification experiments using driving signals collected in a driving simulator and in a real vehicle. Experimental results show that the driver model based on the spectral features of pedal operation signals efficiently models driver individual differences and achieves an identification rate of 76.8% for a field test with 276 drivers, resulting in a relative error reduction of 55% over driver models that use raw pedal operation signals without spectral analysis

Driver Modeling Based on Driving Behavior and Its Evaluation in Driver Identification

In this paper, we propose a driver identification method that is based on the driving behavior signals that are observed while the driver is following another vehicle. Driving behavior signals, such as the use of the accelerator pedal, brake pedal, vehicle velocity, and distance from the vehicle in front, were measured using a driving simulator. We compared the identification rate obtained using different identification models. As a result, we found the Gaussian Mixture Model to be superior to the Helly model and the optimal velocity model. Also, the driver's operation signals were found to be better than road environment signals and car behavior signals for the Gaussian Mixture Model. The identification rate for thirty driver using actual vehicle driving in a city area was 73%.

Driver Identification Using Driving Behavior Signals

Estimation of HRTFs on the horizontal plane using physical features

In this paper, we propose a driver identification method that is based on the driving behavior signals that are observed while the driver is following another vehicle. Driving behavior signals, such as the use of the accelerator pedal, brake pedal, vehicle velocity, and distance from the vehicle in front, are measured using a driving simulator. We compared the identification rate obtained using different identification models and different features. As a result, we found the nonparametric models is better than the parametric models. Also, the driver's operation signals were found to be better than road environment signals and car behavior signals. The identification rate for thirty driver using actual vehicle driving in a city area was 73%.

/pdf/driver-identification-using-driving-behavior-signals-se6thga3qk.pdf

Driver identification using driving behavior signals

Human speech-like noise (HSLN) is a kind of bubble noise generated by superimposing independent speech signals typically more than one thousand times Since the basic feature of HSLN varies from that of overlapped speech to stationary noise, keeping long time spectra in the same shape, we investigate perceptual discrimination of speech from stationary noise and its acoustic correlates using HSLN of various numbers of superposition First we confirm the perceptual score, ie how much the HSLN sounds like stationary noise, and that the number of superpositions of HSLN is proportional by subjective tests Then, we show that the amplitude distribution of the difference signal of HSLN approaches the Gaussian distribution from the Gamma distribution as the number of superpositions increase The other subjective test to perceive three HSLN of different dynamic characteristics clarifies that the temporal change of spectral envelope plays an important roll in discriminating speech from noise

/pdf/extracting-speech-features-from-human-speech-like-noise-tb02zcgtoh.pdf

Fumitada Itakura

Papers

Driver Modeling Based on Driving Behavior and Its Evaluation in Driver Identification

Driver Identification Using Driving Behavior Signals

Estimation of HRTFs on the horizontal plane using physical features

Driver identification using driving behavior signals

Extracting speech features from human speech like noise