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SAFETY, MOBILITY, AND ENVIRONMENTAL IMPACTS OF FORWARD COLLISION WARNING
ALGORITHMS ON A ROADWAY NETWORK
Mostafa H Tawfeek, M.Sc., E.I.T.
Karim El-Basyouny, Ph.D., P.Eng.
Department of Civil & Environmental EngineeringUniversity of Alberta
28th CARSP Conference, Victoria, BC
CONTENTS
2
• Background• Objectives
INTRODUCTION
• Study area• FCW Algorithms comparison framework• FCW algorithms• Measures of Effectiveness (MOEs)
METHODOLOGY
RESULTS AND CONCLUSIONS
28TH CARSP CONFERENCE, VICTORIA, BC
INTRODUCTIONBACKGROUND
oRear-end collisions are one of the most frequenttypes of collisions occurring on North Americanroads 1,2
28TH CARSP CONFERENCE, VICTORIA, BC 3
Other Collisions
Rear-end Collisions(25%)
In CanadaoForward Collision Warning
(FCW) algorithms wereintroduced as an activecountermeasure to avoidthese collisions
INTRODUCTIONBACKGROUND
o Several studies were conducted to compare the
efficiency of various FCW algorithms on a
microscopic/individual level 4,5
o Since the implementation of FCW technologies is
expected to occur in a gradual manner over multiple
years, the impact of these technologies is worth
investigation on a network level
28TH CARSP CONFERENCE, VICTORIA, BC 4
INTRODUCTIONOBJECTIVES
oAssess and compare different FCW algorithms
from a safety, mobility, and environmental
perspectives under varying market penetration rates
(i.e., 25%, 50%, 75%, and 100%)
28TH CARSP CONFERENCE, VICTORIA, BC 5
FORWARD COLLISION WARNING ALGORITHMS COMPARISON
STUDY AREA
28TH CARSP CONFERENCE, VICTORIA, BC 6
FORWARD COLLISION WARNING ALGORITHMS COMPARISON
FRAMEWORK
28TH CARSP CONFERENCE, VICTORIA, BC 7
Whitemud Drive Microsimulation Model
Update FCW Algorithm
Adjust MP
Input Vehicles Trajectories in SSAM Travel Time
Rear End Conflicts
Fuel Consumption
Summarize and Compare the Results
Start Multi-runs
Node Evaluation
FORWARD COLLISION WARNING ALGORITHMS COMPARISON
WHITEMUD DRIVER MICROSIMULATION MODEL
28TH CARSP CONFERENCE, VICTORIA, BC 8
o A previously calibrated VISSIM model representing
Whitemud Drive evening peak hours was used7,8
o External driver models were coded to make the cars that have
an FCW decelerate when needed
o The cars will decelerate based on the braking distance which
differs from an algorithm to another
FORWARD COLLISION WARNING ALGORITHMS COMPARISON
FCW ALGORITHMS
28TH CARSP CONFERENCE, VICTORIA, BC 9
𝑑𝑤𝑎𝑟𝑛
Leading vehicleFollowing vehicle𝑓𝑜𝑙𝑙𝑜𝑤𝑖𝑛𝑔 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒
o Six of the most commonly cited FCW algorithms8-13 were
modeled in VISSIM and the results of the MOEs were
compared on a network basis
FORWARD COLLISION WARNING ALGORITHMS COMPARISON
MODELING ASSUMPTIONS
28TH CARSP CONFERENCE, VICTORIA, BC 10
o The FCW car is equipped with sensing technology which is
in a perfect condition and the braking distance with
sufficient accuracy
o The model assumes the maneuver is followed perfectly
regardless of any variations (i.e., mechanical components,
warning system interface or drivers’ braking application)
FORWARD COLLISION WARNING ALGORITHMS COMPARISON
MODELING ASSUMPTIONS
28TH CARSP CONFERENCE, VICTORIA, BC 11
o Each algorithm was modeled based on its own assumptions
with respect to driver and system delays.
o The weather condition is clear and stable and has no effect
on the drivers’ and/or vehicles’ performance.
FORWARD COLLISION WARNING ALGORITHMS COMPARISON
MEASURES OF EFFECTIVENESS
28TH CARSP CONFERENCE, VICTORIA, BC 12
o Safety measure: rear-end conflicts
oMobility measure: travel time
oEnvironmental measure: fuel consumption
RESULTS
REAR-END CONFLICTS
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0
50
100
150
200
250
300
25% 50% 75% 100%
No.
of R
ear-
End C
onflic
ts
Alg.1 Alg.2 Alg.3 Alg.4 Alg.5 Alg.6 Base Condition
RESULTS
REAR-END CONFLICTS
28TH CARSP CONFERENCE, VICTORIA, BC 14
0
5
10
15
20
25
30
35
40
45
0 10 20 30 40 50 60
Bra
kin
g D
ista
nce (
m)
Relative Speed (km/hr)
Alg.3 (80 km/hr)Alg.5 (80 km/hr)Alg.6 (80 km/hr)Alg.3 (60 km/hr)Alg.5 (60 km/hr)Alg.6 (60 km/hr)
RESULTS
TRAVEL TIMES
28TH CARSP CONFERENCE, VICTORIA, BC 15
400
425
450
475
25% 50% 75% 100%
Tra
vel T
ime (
seconds)
Alg.1 Alg.2 Alg.3 Alg.4 Alg.5 Alg.6 Base Condition
RESULTS
FUEL CONSUMPTION
28TH CARSP CONFERENCE, VICTORIA, BC 16
2200
2250
2300
2350
2400
25% 50% 75% 100%
Fuel C
on
sum
ption (
Liter)
Alg.1 Alg.2 Alg.3 Alg.4 Alg.5 Alg.6 Base Condition
CONCLUSIONS
28TH CARSP CONFERENCE, VICTORIA, BC 17
o Systematic improvements (i.e., on the network level) caused
by the FCW systems will generally overlap with the
situational improvements (i.e., on a driver level)
o More tangible improvements were noticed with higher
penetration rates
CONCLUSIONS
28TH CARSP CONFERENCE, VICTORIA, BC 18
o Generally, safety benefits on the network level for most of
the FCW algorithms did not have a substantial effect on
mobility and environment
o The FCW systems, which did not provide a network-level
safety benefit, were more likely to have negative impacts on
mobility and environment
CONCLUSIONS
28TH CARSP CONFERENCE, VICTORIA, BC 19
o The more conservative algorithms (e.g., Alg.3) in terms of
braking distance (i.e., longer distance) had inconsistent
results on a network level for all measures
o Alg.2, which is a perceptual FCW algorithm, gave the best
results in terms of safety benefits
LIMITATIONS AND FUTURE RESEARCH
o The modeled FCW algorithms assumed perfect drivers’
compliance and sensing capabilities
o Varying levels of service and weather conditions were not
taken in consideration while modeling the FCW algorithms
o The assessment of integrating the FCW systems with other
Connected Vehicle applications should be investigated
28TH CARSP CONFERENCE, VICTORIA, BC 20
REFERENCES
1. National Safety Council, “National Safety Council Injury Facts,” 2015.2. Transport Canada, “National Collision Database Online,” 2017.3. P. Seiler, B. Song, and J. Hedrick, “Development of a collision avoidance system,” Automot. Eng., vol. Vol. 106, pp. 24–28, 1998.4. L. Yang, J. H. Yang, E. Feron, and V. Kulkarni, “Development of a Performance-Based Approach for a Rear-End Collision
Warning and Avoidance System for Automobiles,” pp. 316–321, 2003.5. K. Lee and H. Peng, “Evaluation of automotive forward collision warning and collision avoidance algorithms,” Veh. Syst. Dyn., vol.
43, no. 10, pp. 735–751, 2005.6. M. Hadiuzzaman, “Variable Speed Limit Control to Mitigate Freeway Congestion,” University of Alberta, Canada, 2014.7. X. Wang, M. Hadiuzzaman, and T. Z. Qiu, “Analyzing Sensitivity of Freeway Capacity at a Complex Weaving Segment,” in 2012
CSCE Conference, 2012, pp. 1–11.8. R. Kiefer, D. LeBlanc, M. Palmer, J. Salinger, R. Deering, and M. Shulman, “Development and Validation of Functional
Definitions and Evaluation Procedures For Collision Warning/Avoidance Systems,” no. August, p. 75, 1999.9. Y. Fujita, K. Akuzawa, and M. Sato, “Radar brake system,” JSAE Rev., vol. 16(2), pp. 95–101, 1995.10. A. Doi, T. Butsuen, T. Niibe, T. Takagi, Y. Yamamoto, and H. Seni, “Development of a rear-end collision avoidance system with
automatic brake control,” JSAE Rev., vol. 15, no. 4, pp. 335–340, 1994.11. S. J. Brunson, E. M. Kyle, N. C. Phamdo, and G. R. Preziotti, “Alert Algorithm Development Program NHTSA Rear-End Collision
Alert Algorithm,” no. September, 2002.12. P. Seiler, B. Song, and J. Hedrick, “Development of a collision avoidance system,” Automot. Eng., vol. Vol. 106, pp. 24–28, 1998.13. A. L. Burgett, A. Carter, R. J. Miller, and W. G. Najm, “A collison warnig alorithm for rear-end collision,” Natl. Highw. Traffic Saf.
Adm. Washington, DC, pp. 566–587, 1998.
Images:
https://automobiles.honda.com/images/2016/pilot/features-safety/forward-collision-warning.jpg
https://transformingedmonton.ca/get-there-faster-slow-down/
28TH CARSP CONFERENCE, VICTORIA, BC 21
QUESTIONS?CONTACT INFO
Mostafa H Tawfeek, M.Sc., E.I.TDepartment of Civil and Environmental Engineering
University of [email protected]