automated vehicle decision support system (avdss) for...
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AUTOMATED VEHICLE DECISION SUPPORT SYSTEM (AVDSS) FOR
INCLEMENT WEATHER
Anissa Jensen Gerard
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INTRODUCTION ■ Current issues ■ Current methods ■ Decision Support System
(DSS) developed ■ DSS results ■ Applications of DSS in
Connected Vehicle Technology (CVT)
■ Future refinement ■ Conclusions & Questions
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LEARNING OUTCOMES
■ Identify current issues with weather and freight ■ Learn how decisions are currently made ■ Show development of DSS using BBN and data ■ Learn how results of AVDSS in terms of knowledge
based decisions add benefit to freight during weather
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KEY POINT
AVDSS enhances safety and mobility of freight during weather through knowledge based system using connected vehicle technology
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CURRENT ISSUES ■ Weather related delay costs the freight industry almost $9
billion annually (FHWA, 2012) ■ Trucks move 64 % of the weight of freight (about 11.3 billion
tons) in 2011, increasing to 18.8 billion tons by 2040. (FHWA FFF, 2012)
■ In 2012, 80,302 fatal and injury crashes ■ 75 % of the fatalities and injuries occurring in occupants of
other vehicles (NHTSA, 2012) ■ Many of the decisions are left up to the individual truck
driver, these decisions are called naturalistic decisions
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NATURALISTIC DECISION MAKING
■ Entire field of research dedicated to Naturalistic Decision Making (NDM) that evolved in the 1980’s
■ Research has found that the naturalistic decision is based on prior experience rather than a ranking of options
■ This led to using information enhancing technologies such as decision support systems to improve decision making
(Klein, 2008)
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Decision Support Systems
■ Decision support systems (DSS) are a growing field to assist the transportation manager
■ Many different types of DSS including Artificial Intelligence and neural networks
■ Successful example MDSS ■ Bayesian Belief Network (BBN) chosen
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Description of BBN ■ Graphical models using the knowledge domain
similar to modeling the expert in rule based systems ■ A probabilistic model that can deal with
uncertainties in data ■ Uses a system of nodes and links and establishes a
probability for each node ■ The probabilities are assigned based on both
evidence, educated hypotheses and priori knowledge (Cao et al, 2009)
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Inputs of BBN
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Inputs 2
■ 5 storm parameters; rain, snow, ice, wind and visibility
■ 6 storms modeled ■ Levels of 4 discrete storm categories ■ Probabilities of delay and crash determined from
existing data
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Storm categories
STORM CATEGORY
Wind (mph) Rain Snow Ice Visibility(mi)
0 <15 NONE NONE NONE >2
1 ≤25 Light Light Moderate
Snow 1-2
2 ≤35 Moderate Moderate Heavy Snow 0.25-1
3 >35 Heavy Heavy Heavy snow 0.25
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Final delay probabilities per storm category
PERCENTAGE PROBABILITY OF DELAY(%) Wind Rain Snow Ice Visibility
0 0 0 0 0 0 1 0.02 0.05 0.05 0.22 0.18 2 0.02 0.22 0.22 0.79 0.22 3 0.04 0.36 0.79 0.79 0.22
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Results STORM 2
Storm Category
Probability Delay %
Probability Crash %
Normal Driving Crash %
Scenario 1 Denver 0 0 0.2746 0.0265
Des Moines 1-2 18.9 Davenport 3 100
Scenario 2 Kansas City 0-1 2.5 0.1205 0.0116
Des Moines 2-3 35.5 Scenario 3 Chicago 1-2 15.5 0.1281 0.0124
Des Moines 3 100 Scenario 4 Springfield IL 1-2 24 0.1629 0.0157
Springfield MO 1-2 40.9 Scenario 5 Kansas City 1-2 18.9 0.0958 0.0092
to Des Moines 1-2 15.5
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Development of Indexes of collective risk ■ Use of Geometric mean to put in terms of useful output for
decision
■ 𝐺𝐺.𝑀𝑀. = √(𝑁𝑁𝐶𝐶 ∗ 𝑁𝑁𝐷𝐷)
■ 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 = 10 ∗ 𝐺𝐺.𝑀𝑀 ■ Final indexes between 0-10
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FINAL INDEXES
0 0 0 0 0.39 0.41 0.42 0.59 0.6 0.65
0.93 0.98 1.05 1.28 1.38
1.76 1.8 1.89 1.99 2.06 2.12 2.33 2.46 2.57 2.66 2.69 2.71 2.71 2.85 2.88 2.96 3.14
3.77 3.78 3.95 4.07 4.35
4.93
6.83 6.83
10
-2
0
2
4
6
8
10
12
Break points
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Results 2
RANGE OF INDEXES DECISION
8.5-10 STOP NOW
6-8.5 STOP SOON
2-6 POSSIBLE STOP*
0-2 DRIVE THROUGH
* More research needed to develop better decisions in this range
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SURVEY PROVIDES VALUABLE INFORMATION ■ Three items to compare to assumptions in AVDSS: storm
category, decisions during certain storms and at what probability would you stop.
■ RESULTS: – Drivers think storm is less severe than our model – Most drivers would never stop in severe storms – Most claim they would stop if they knew risk was 3x
normal or less
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KNOWLEDGE BASED DSS FOR AUTOMATED VEHICLES ■ Growing use of telematics equipment is part of the USDOT
Connected Vehicle initiative (formerly known as IntelliDrive and Vehicle Infrastructure Integration [VII]
■ New technologies: rain intensity sensing wipers, adaptive cruise control, stability control, and driver assist systems
■ Advancement of connected vehicle technology, the importance of decision making increases
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CONVOY EXAMPLE ■ If convoyed with 5-10
trucks, decision of one affects all
■ Example of connected vehicle technology: Lane delineation
■ Decisions become much more important
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Discussion of Results
■ More robust than NDM based ■ Decision is data driven ■ Quantifying risk that hasn’t been done ■ Survey shows that drivers NDM conflicts data of existing
crash and delay ■ With Automated Vehicles, becomes a critical tool in Truck
Decision Making of Automated Fright vehicles.
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Future refinements, some not all…
■ Significant gaps in data ■ Time dependent weather forecast accuracy ■ Expand crash probability research for trucks in
different storm conditions ■ Extensive survey to relate AVDSS output and storm
category for freight
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LEARNING OUTCOMES
■ Identify current issues with weather and freight ■ Learn how decisions are currently made ■ Show development of DSS using BBN and data ■ Learn how results of AVDSS in terms of knowledge
based decisions add benefit to freight during weather
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CONCLUSION & QUESTIONS?
■ Contact information ■ Anissa Jensen Gerard [email protected] ■ Wilfrid A. Nixon [email protected]