Prediction of Route and Destination Intent

Shibumon Alampatta

(Roll No. 12CS60D02)

Guided by: Prof. Arobinda Gupta

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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

2Image Courtesy: http://exploringthemind.com

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 normallycomes from the Experience, Knowledgeand Analytical skill to understandPatterns

A Prediction Model

ModelPast

DataFuture

Prediction

Real Time

Input

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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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Markov Model

• Captures sequential model of behavior

• Markov Property: – Future is independent of past; given present

• <S, A, T>– 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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S(t-1) S(t) S(t+1)

Hidden Markov Model (HMM)

• A Markov Model with Hidden(Unobservable) States

• <S, A, O, T, Z, ∏ >

• O – Finite set of Observations

• Z – Observation function– Z : O x S x A R

– 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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Hidden State

Intentions in Drivers Mind

Observation

Current Link

HMM

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S(t-1) S(t) S(t+1)

s1 s2

o1 o2 o3

O(t-1) O(t) O(t+1)

Sequential Representation

State Transition Representation

Predicting Driver’s Intent

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Build the Intent Prediction Model

Train the Model using Collected Trip Data

Use the Model for Prediction

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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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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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

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Map Courtesy: maps.google.com

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)

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l

g<l, g>

Image Courtesy: depositphotos.com Map Courtesy: maps.google.com

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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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,we first predict the nextlink that the driver will go and then we predict his goal destination based on that link

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lj

li

gi

Map Courtesy: maps.google.com

Building the Model – Probability Computation

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To compute p(gi|li)

To compute p(li | sj) = p(li | <lj, gj>)

lj

li

gj

Building the Model – Next Link Prediction

• Possible next states

– <l1,gi>

– <l2,gi>

– <l3, gi>

• Link li which

scores maximum

is predicted as

next link

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Map Courtesy: maps.google.com

c

Building the Model – Next Link Prediction

• p(si|sj) = p(<li, gi> | sj)

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<l3, g1><l3, g2><l3, g3>

<l2, g1><l2, g2><l2, g3>

<l1, g1><l1, g2><l1, g3>

Score for l1

Score for l2

Score for l3

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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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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Past Trip Data

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Ability to predict something normallycomes from the Experience, Knowledgeand Analytical skill to understandPatterns

Probabilistic Model

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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Summary

• Problem Definition

• Applications and Motivation

• Various Algorithmic Techniques

• HMM based model

• Limitations

• Possible enhancements

• Extension of application domains

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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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Thank You

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