Perceptual Framework for a Modern Left-Turn Collision Warning System
Most of the collision warning systems currently
available in the automotive market are mainly designed to warn
against imminent rear-end and lane-changing collisions. No collision
warning system is commercially available to warn against imminent
turning collisions at intersections, especially for left-turn collisions
when a driver attempts to make a left-turn at either a signalized or
non-signalized intersection, conflicting with the path of other
approaching vehicles traveling on the opposite-direction traffic
stream. One of the major factors that lead to left-turn collisions is the
human error and misjudgment of the driver of the turning vehicle
when perceiving the speed and acceleration of other vehicles
traveling on the opposite-direction traffic stream; therefore, using a
properly-designed collision warning system will likely reduce, or
even eliminate, this type of collisions by reducing human error. This
paper introduces perceptual framework for a proposed collision
warning system that can detect imminent left-turn collisions at
intersections. The system utilizes a commercially-available detection
sensor (either a radar sensor or a laser detector) to detect approaching
vehicles traveling on the opposite-direction traffic stream and
calculate their speeds and acceleration rates to estimate the time-tocollision
and compare that time to the time required for the turning
vehicle to clear the intersection. When calculating the time required
for the turning vehicle to clear the intersection, consideration is given
to the perception-reaction time of the driver of the turning vehicle,
which is the time required by the driver to perceive the message
given by the warning system and react to it by engaging the throttle.
A regression model was developed to estimate perception-reaction
time based on age and gender of the driver of the host vehicle.
Desired acceleration rate selected by the driver of the turning vehicle,
when making the left-turn movement, is another human factor that is
considered by the system. Another regression model was developed
to estimate the acceleration rate selected by the driver of the turning
vehicle based on driver-s age and gender as well as on the location
and speed of the nearest approaching vehicle along with the
maximum acceleration rate provided by the mechanical
characteristics of the turning vehicle. By comparing time-to-collision
with the time required for the turning vehicle to clear the intersection,
the system displays a message to the driver of the turning vehicle
when departure is safe. An application example is provided to
illustrate the logic algorithm of the proposed system.
[1] P. L. Olson, R. E. Dewar, and G. J. Alexander. Human factors in traffic
safety. Tucson, Arizona: Lawyers & Judges Publishing Company, 2002
[2] J Pierowicz, E. Jocoy, M. Lloyd, A. Bittner and B. Pirson. "Intersection
collision avoidance using ITS countermeasures". Final Report, NHTSA,
U.S. DOT, Washington, D.C., USA. DOT HS 809 171, 2000
[3] S. N. J. Watamaniuk and A. Duchon. "The human visual system
averages speed information". Vision Research Vol. 24, 47-53, 1992
[4] National Highway Traffic Safety Administration. "Vehicle-Based
countermeasures for signal and stop sign violation". Progress Report,
NHTSA, U.S. DOT, Washington, D.C., USA. DOT HS 809 716, 2004
[5] K. Fuerstenberg and J. Chen. "New European approach for intersection
safety - results of the EC project INTERSAFE", in Proc. International
Forum on Advanced Microsystems for Automotive Applications. ISBN-
13 978-3-540-71324-1. Springer-Verlag, Berlin, Germany, 2007
[6] B. White and K. A. Eccles. "Inexpensive infrastructure-based
intersection collision-avoidance system to prevent left-turn crashes with
opposite-direction traffic". Transportation Research Record Vol.
1800:02-3846. Transportation Research Board, Washington D.C., USA,
2002
[7] National Highway Traffic Safety Administration. "Automotive collision
avoidance systems (ACAS) program". Final Report, NHTSA, U.S. DOT,
Washington, D.C., USA. DOT HS 809 080, 2000
[8] US Department of Transportation. "Intersection collision avoidance
study". Final report, Department of Transportation (DOT) and Federal
Highway Administration (FHWA), Safety Office. Washington, D.C.,
USA, 2003. Available
http://ntlsearch.bts.gov/tris/record/tris/00987780.html
[9] VORAD Collision warning system, produced by Eaton Corporation,
Cleveland, Ohio, USA. Available www.vorad.com
[10] Smart Microwave Sensors SMS. Smart Microwave Sensors, Germany.
Available www.smartmicro.de/
[11] IBEO Automobile Sensor. IBEO laser scanners. Available
http://www.ibeo-as.com/english/default.asp
[12] R. E. Kalman. "A new approach to linear filtering and prediction
problems". Journal of Basic Engineering Vol. 82 (1): 35-45, 1960
[13] P. Maybeck. Stochastic Models Estimation and Control. Academic
Press. New York, USA, 1979
[14] M. S. Grewal and A. P. Andrews. Kalman Filtering Theory and
Practice. Prentice Hall. New Jersey, USA, 1993
[15] S. M. Easa, M. Z. Ali and E. Dabbour. "Offsetting opposing left-turn
lanes for intersections on horizontal curves". ASCE Journal for
Transportation Engineering Vol. 131:11, 2005
[16] S. M. Easa and M. Z. Ali. "Modified Guidelines for left-turn lane
geometry at intersections". ASCE Journal for Transportation
Engineering Vol. 131:9, 2005
[17] Transport Canada. "Road safety in Canada - 2003". Report, Transport
Canada, Ottawa, Canada. TP 13951 E, 2003
[18] STISIM user-s guide. Systems Technology Inc., Hawthorne, California,
USA. Available http://www.stisimdrive.com/
[19] D. R. Ragland, S. Arroyo, S. E. Shladover, J. A. Misener and C. Chan.
"Gap acceptance for vehicles turning left across on-coming traffic:
Implications for". Report, University of California Berkeley Traffic
Safety Center, California, USA. UCB-TSC-RR-2005-TRB4, 2005
[1] P. L. Olson, R. E. Dewar, and G. J. Alexander. Human factors in traffic
safety. Tucson, Arizona: Lawyers & Judges Publishing Company, 2002
[2] J Pierowicz, E. Jocoy, M. Lloyd, A. Bittner and B. Pirson. "Intersection
collision avoidance using ITS countermeasures". Final Report, NHTSA,
U.S. DOT, Washington, D.C., USA. DOT HS 809 171, 2000
[3] S. N. J. Watamaniuk and A. Duchon. "The human visual system
averages speed information". Vision Research Vol. 24, 47-53, 1992
[4] National Highway Traffic Safety Administration. "Vehicle-Based
countermeasures for signal and stop sign violation". Progress Report,
NHTSA, U.S. DOT, Washington, D.C., USA. DOT HS 809 716, 2004
[5] K. Fuerstenberg and J. Chen. "New European approach for intersection
safety - results of the EC project INTERSAFE", in Proc. International
Forum on Advanced Microsystems for Automotive Applications. ISBN-
13 978-3-540-71324-1. Springer-Verlag, Berlin, Germany, 2007
[6] B. White and K. A. Eccles. "Inexpensive infrastructure-based
intersection collision-avoidance system to prevent left-turn crashes with
opposite-direction traffic". Transportation Research Record Vol.
1800:02-3846. Transportation Research Board, Washington D.C., USA,
2002
[7] National Highway Traffic Safety Administration. "Automotive collision
avoidance systems (ACAS) program". Final Report, NHTSA, U.S. DOT,
Washington, D.C., USA. DOT HS 809 080, 2000
[8] US Department of Transportation. "Intersection collision avoidance
study". Final report, Department of Transportation (DOT) and Federal
Highway Administration (FHWA), Safety Office. Washington, D.C.,
USA, 2003. Available
http://ntlsearch.bts.gov/tris/record/tris/00987780.html
[9] VORAD Collision warning system, produced by Eaton Corporation,
Cleveland, Ohio, USA. Available www.vorad.com
[10] Smart Microwave Sensors SMS. Smart Microwave Sensors, Germany.
Available www.smartmicro.de/
[11] IBEO Automobile Sensor. IBEO laser scanners. Available
http://www.ibeo-as.com/english/default.asp
[12] R. E. Kalman. "A new approach to linear filtering and prediction
problems". Journal of Basic Engineering Vol. 82 (1): 35-45, 1960
[13] P. Maybeck. Stochastic Models Estimation and Control. Academic
Press. New York, USA, 1979
[14] M. S. Grewal and A. P. Andrews. Kalman Filtering Theory and
Practice. Prentice Hall. New Jersey, USA, 1993
[15] S. M. Easa, M. Z. Ali and E. Dabbour. "Offsetting opposing left-turn
lanes for intersections on horizontal curves". ASCE Journal for
Transportation Engineering Vol. 131:11, 2005
[16] S. M. Easa and M. Z. Ali. "Modified Guidelines for left-turn lane
geometry at intersections". ASCE Journal for Transportation
Engineering Vol. 131:9, 2005
[17] Transport Canada. "Road safety in Canada - 2003". Report, Transport
Canada, Ottawa, Canada. TP 13951 E, 2003
[18] STISIM user-s guide. Systems Technology Inc., Hawthorne, California,
USA. Available http://www.stisimdrive.com/
[19] D. R. Ragland, S. Arroyo, S. E. Shladover, J. A. Misener and C. Chan.
"Gap acceptance for vehicles turning left across on-coming traffic:
Implications for". Report, University of California Berkeley Traffic
Safety Center, California, USA. UCB-TSC-RR-2005-TRB4, 2005
@article{"International Journal of Architectural, Civil and Construction Sciences:50229", author = "E. Dabbour and S. M. Easa", title = "Perceptual Framework for a Modern Left-Turn Collision Warning System", abstract = "Most of the collision warning systems currently
available in the automotive market are mainly designed to warn
against imminent rear-end and lane-changing collisions. No collision
warning system is commercially available to warn against imminent
turning collisions at intersections, especially for left-turn collisions
when a driver attempts to make a left-turn at either a signalized or
non-signalized intersection, conflicting with the path of other
approaching vehicles traveling on the opposite-direction traffic
stream. One of the major factors that lead to left-turn collisions is the
human error and misjudgment of the driver of the turning vehicle
when perceiving the speed and acceleration of other vehicles
traveling on the opposite-direction traffic stream; therefore, using a
properly-designed collision warning system will likely reduce, or
even eliminate, this type of collisions by reducing human error. This
paper introduces perceptual framework for a proposed collision
warning system that can detect imminent left-turn collisions at
intersections. The system utilizes a commercially-available detection
sensor (either a radar sensor or a laser detector) to detect approaching
vehicles traveling on the opposite-direction traffic stream and
calculate their speeds and acceleration rates to estimate the time-tocollision
and compare that time to the time required for the turning
vehicle to clear the intersection. When calculating the time required
for the turning vehicle to clear the intersection, consideration is given
to the perception-reaction time of the driver of the turning vehicle,
which is the time required by the driver to perceive the message
given by the warning system and react to it by engaging the throttle.
A regression model was developed to estimate perception-reaction
time based on age and gender of the driver of the host vehicle.
Desired acceleration rate selected by the driver of the turning vehicle,
when making the left-turn movement, is another human factor that is
considered by the system. Another regression model was developed
to estimate the acceleration rate selected by the driver of the turning
vehicle based on driver-s age and gender as well as on the location
and speed of the nearest approaching vehicle along with the
maximum acceleration rate provided by the mechanical
characteristics of the turning vehicle. By comparing time-to-collision
with the time required for the turning vehicle to clear the intersection,
the system displays a message to the driver of the turning vehicle
when departure is safe. An application example is provided to
illustrate the logic algorithm of the proposed system.", keywords = "Collision warning systems, intelligent transportationsystems, vehicle safety.", volume = "3", number = "9", pages = "311-7", }