This document discusses predicting a driver's intended route and destination using a Hidden Markov Model approach. It begins by outlining the problem and applications, such as route guidance and fuel efficiency. It then discusses using a probabilistic model to capture the sequential and routine nature of driving behavior. The model represents routes as a graph and states as links and goals. It is trained on past trip data to learn transition probabilities and predict the next link and destination. The approach achieves over 80% accuracy on average but has limitations for non-routine trips. Possible enhancements include additional parameters and expanding to other domains.

1. Prediction of Route and
Destination Intent
Shibumon Alampatta
(Roll No. 12CS60D02)
Guided by: Prof. Arobinda Gupta
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2. What is it About?
• Predict driver’s intent – intended route and
destination
– Predict the goal and route; given current location
– Predict the route; given a goal(destination) and
current location
Image Courtesy: http://exploringthemind.com 2

3. Application Area
• Route Guidance in Navigation
• Improving Hybrid Fuel Economy
– 7.8% fuel economy, Research by Nissan
(Froehlich 2008)
• Intelligent Transportation System
• VANET
• Points of interest and Advertisement
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4. Ability to predict something normally
comes from the Experience, Knowledge
and Analytical skill to understand
Patterns
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5. A Prediction Model
Real Time
Input
Past Future
Model
Data Prediction
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6. Algorithmic Techniques
• Hidden Markov Model [Simmons 2006][Froehlich
2008][Nagaraj 2011]
• Artificial Neural Network [Mikluščák 2012]
• Nearest Neighbour algorithm [Yu 2011]
• Genetic Algorithm [Kanoh 2008]
• Bayesian Classifier [Cook 2004]
• Support Vector Machines [Yu 2011]
• Choice set Generation Model [Prato 2009]
• Pattern Matching [Hattab 2012]
• Plan Recognition [Cook 2004]
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7. Markov Model
• Captures sequential model of behavior
• Markov Property:
– Future is independent of past; given present
• <S, A, T> S(t-1) S(t) S(t+1)
– S : Set of States
– A : Set of Actions
– T: Transition function T: S x A x S  R
• T(si, a, sj) = P(si| sj, a)
• Probability of transitioning to a state si; given that the
system is in state sj and action a is executed
• In some cases explicit actions may not be there
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8. Hidden Markov Model (HMM)
• A Markov Model with Hidden(Unobservable) States
• <S, A, O, T, Z, ∏ > Observation Hidden State
• O – Finite set of Observations
• Z – Observation function Current Intentions
in Drivers
Link
– Z:OxSxAR Mind
– Z(o, s, a) = P(o| s, a)
– Probability of receiving observation o, given system ends up
in state s on executing action a
– For many problems Z(o, s, ai) = Z(o, s, aj), so we write Z(o, s)
• ∏ - Initial state distribution
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9. HMM
s1 s2
State Transition
Representation
o1 o2 o3
S(t-1) S(t) S(t+1)
Sequential
Representation O(t-1) O(t) O(t+1)
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10. Predicting Driver’s Intent
Build the Intent
Prediction Model
Train the Model using
Collected Trip Data
Use the Model for
Prediction
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11. Building the Model
• Assumption
– Driving is mostly routine
– Past performance can be used to predict what the
driver will do in future
– Route map and a GPS is available and can compute
segment of the map the vehicle is on
• Routine nature of driving
– Tend to go to same destination again and again
– Tend to follow same route
– Same time
– Even when better alternatives exists (shorter or faster)
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12. Building the Model
• Perfect prediction is not possible
– Example scenarios
• Conclusion: Prediction of driver intent is
probabilistic
– So, we can make prediction with certain
probability only
– But never be 100% sure about the prediction
• So we can use a probabilistic approach
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13. Building the Model
• Road Graph Representation
– Model a Graph G(V, E) from the
road map
– Vertices (v) for each intersection
– Link (l)– unique labeling for an
edge between two intersections
Map Courtesy: maps.google.com
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14. Building the Model
• We want to predict the intention that driver is
going to have in his mind
• Based on intention in his mind he take turns
• State s = <l, g> ; l – link, g - goal
• State Transition Function T(si, sj) = p(si|sj)
<l, g> g
l
Image Courtesy: 14
depositphotos.com Map Courtesy: maps.google.com

15. Building the Model
• What we can observe?
– Current link ; ie segment on the road
corresponding to current location
– Observation function
Z(ol, s) = p(ol|<l, g>) which is 1 here
– Probability of current link being l given the system
is in state s = <l, g>
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16. Building the Model
• We can write the transition probability p(si|sj) as
p(<li, gi> | sj ) = p(li | sj) p(gi | li)
• Given the current state, gi
we first predict the next
link that the driver will go
li
and then we predict his
goal destination based lj
on that link
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Map Courtesy: maps.google.com

17. Building the Model – Probability
Computation
gj
li
To compute p(gi|li) lj
To compute p(li | sj) = p(li | <lj, gj>) 17

18. Building the Model – Next Link
Prediction
• Possible next states
– <l1,gi>
– <l2,gi>
– <l3, gi>
• Link li which
scores maximum
is predicted as
c
next link
Map Courtesy: maps.google.com
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19. Building the Model – Next Link
Prediction
• p(si|sj) = p(<li, gi> | sj)
<l1, g1>
<l1, g2> Score for l1
<l1, g3>
<l2, g1>
<l2, g2> Score for l2
<l2, g3>
<l3, g1>
<l3, g2> Score for l3
<l3, g3>
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20. Building the Model - Prediction
• Predicting the goal/route given current link
– Using the ability to predict next link continue the
prediction until we reach some goal or most
probable goal
• Predicting route given goal
– Use this to bias the prediction of next link and
continue prediction until we reach g
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21. Training the Model
• Collect Trips data
• A Trip is an ordered list of links (<l1, t1>, <l2, t2>…..)
• Go through and trip sequence, fill or update the
tables.
• This helps in computing the probabilities
• Training data shall be reliable
• More the data; better accuracy
• Once Training is done; Use for prediction with real-
time data
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22. Past Trip
Data
Ability to predict something normally
comes from the Experience, Knowledge
and Analytical skill to understand
Patterns
Probabilistic Model
22

23. Observations
• Achieves more than 80% accuracy in average
– Can be harnessed for , route planning, traffic
prediction, smarter route guidance
– emergency route etc.
• We can include the parameters like time of the
day, day of the week etc. to state tuple to
enhance the model
• Scenarios where routine nature is not maintained
(sales people or delivery boys)
• Ad-hoc predictions are difficult (Like terrorists)
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24. Summary
• Problem Definition
• Applications and Motivation
• Various Algorithmic Techniques
• HMM based model
• Limitations
• Possible enhancements
• Extension of application domains
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25. References
[1] Reid Simmons, Brett Browning, Yilu Zhang, Varsha Sadekar, Learning to predict driver route and
destination intent, IEEE Intelligent Transportation System Conference, 2006
[2] Hitoshi Kanoh, Kenta Hara, Hybrid genetic Algorithm for Dynamic Multi Objective Route Planning
with Predicted Traffic in a Real World Road Network, Proceedings of the 10th Annual Conference
on Genetic and Evolutionary Computation, 2008
[3] John Froehlich, John Krumm, Route Prediction from Trip Observation, Society of Automotive
Engineers (SAE) World Congress, 2008
[4] Uma Nagaraj, N N Kadam, Study of Statistical Models for Route Prediction Algorithms in VANET,
Journal of Information Engineering and Applications, Vol 1, No. 4, 2011
[5] Carlo Giacomo Prato, Route Choice Modeling: Past, Present and Future Research Directions,
Journal of Choice Modeling, 2(1), pp. 65-100, 2009
[6] http://en.wikipedia.org/wiki/Hidden_Markov_model
[7] http://en.wikipedia.org/wiki/Markov_property
[8] Diane J Cook, Prediction Algorithms for Smart Environments, Chapter 8, Smart Environments:
Technology, Protocols and Applications, John Wiley & Sons, 2004
[9] M Al-Hattab, M Takruri, J Agbinya, Mobility Prediction using Pattern Matching, International
Journal of Electrical and Computer Sciences, Vol.12 No.3, 2012
[10] Tomáš Mikluščák, Michal Gregor, Aleš Janota, Using Neural Networks for Route and Destination
Prediction in Intelligent Transport Systems, 12th International Conference on Transport Systems
Telematics, 2012
[11] Bin Yu, William H.K. Lam, Mei Lam Tam, Bus Arrival Time Prediction at Bus Stop with Multiple
Routes, Transportation Research Part C: Emerging Technologies, Volume 19 Issue 6 pp. 1157–
1170, 2011
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26. Thank You
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