Development of a method for detecting jerks in safety critical events.

A new method for detecting jerks in safety critical events, based on the characteristics of the braking caused by the driver in time critical situations, has been developed and evaluated in a small pilot test and also applied on a naturalistic driving study. A portable event data recorder, capable of measuring and recording acceleration profiles for a predetermined time period before and after the safety critical situation, has also been developed to ensure high data quality used to evaluate the proposed method. Thus, an analysis of the acceleration profile is possible during the entire braking event. The study involves analyses of acceleration profiles and different characteristics of the rate of change of the acceleration profiles, i.e. jerks, such as negative jerk, used in previous studies, and a peak-to-peak value of the jerk. The finding is that the proposed method provides a more distinct difference between critical and potentially critical events and thus may be an appropriate method used to detect safety critical events.

[1]  Miles Tight,et al.  The development of an automatic method of safety monitoring at Pelican crossings. , 2005, Accident; analysis and prevention.

[2]  A. Horst.  A time-based analysis of road user behaviour in normal and critical encounters , 1990 .

[3]  R Risser,et al.  Behavior in traffic conflict situations. , 1985, Accident; analysis and prevention.

[4]  Hikaru Inooka,et al.  Real-time algorithms for estimating jerk signals from noisy acceleration data , 2003 .

[5]  Pavel Matějka,et al.  Noise reduction in Raman spectra: Finite impulse response filtration versus Savitzky–Golay smoothing , 2007 .

[6]  J. Staggs.  Savitzky-Golay smoothing and numerical differentiation of cone calorimeter mass data , 2005 .

[7]  A. Wåhlberg.  The relation of acceleration force to traffic accident frequency: a pilot study , 2000 .

[8]  A Várhelyi,et al.  Drivers' speed behaviour at a zebra crossing: a case study. , 1998, Accident; analysis and prevention.

[9]  C. Hydén.  THE DEVELOPMENT OF A METHOD FOR TRAFFIC SAFETY EVALUATION: THE SWEDISH TRAFFIC CONFLICTS TECHNIQUE , 1987 .

[10]  Andre Ivanov,et al.  Fully Automated High-Performance Signal-to-Noise Ratio Enhancement Based on an Iterative Three-Point Zero-Order Savitzky—Golay Filter , 2008, Applied spectroscopy.

[11]  Takashi Imamura,et al.  A study of analysis method for driver features extraction , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.

[12]  Jianwen Luo,et al.  Properties of Savitzky-Golay digital differentiators , 2005, Digit. Signal Process..

[13]  Łukasz Komsta,et al.  A comparative study on several algorithms for denoising of thin layer densitograms. , 2009, Analytica chimica acta.

[14]  Magnus Nygård,et al.  A method for analysing traffic safety with help of speed profiles , 1999 .

[15]  Omar Bagdadi,et al.  Jerky driving--An indicator of accident proneness? , 2011, Accident; analysis and prevention.

[16]  Jonathan M Hankey,et al.  A method for evaluating collision avoidance systems using naturalistic driving data. , 2008, Accident; analysis and prevention.

[17]  Jianwen Luo,et al.  Savitzky-Golay smoothing and differentiation filter for even number data , 2005, Signal Process..

[18]  P. Barak.  Smoothing and Differentiation by an Adaptive-Degree Polynomial Filter , 1995 .

[19]  Christer Hydén,et al.  Estimating the severity of safety related behaviour. , 2006, Accident; analysis and prevention.