ICMLA 2015 - Car Following Markov Regime Classification and Calibration
1. Car Following Markov Regime
Classification and Calibration
Eng.Ahmed Bayoumy Zaky Prof.Waild Gomaa Dr. Mohamed A. Khamis
Department of Computer Science and Engineering
Cyber-Physical Systems Lab
12/15/2015
2. Agenda
• Introduction
• Problem Statement
• Previous Approaches
• Proposed System
• Experiment and Results
• Conclusion
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3. Motivation
– 5.4 million - automobile crashes in 2012.
– 1.1 million - crashes due to a driver talking on a cell phone.
– 1.24 million - Estimated deaths worldwide caused by road traffic accidents.
– 12000 – Egypt total fatalities due to traffic road accident per year.
– Study human behavior in a complex environment.
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Source: World Health Organization (http://apps.who.int/gho/data/node.main.A997)
4. Problem Statement
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• How we can model driver behavior during
different driving tasks and situations ?
• Are regime switching models suitable for this
task ?
• What is the important features we will have
by using such models ?
5. Multitasking is a myth
• The stochastic nature of the driving
environment.
• National safety council states that
– Multitasking is a myth.
– The brain handles tasks sequentially.
By switching between one task and
another.
• The driver usually switches
between different driving behaviors
as car following, lane changing,
mobile communication, sign
reading…….
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6. Car Following Behavior
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• Describes the longitudinal relation between a following vehicle with a
leader and how follower behavior is constrained by the leader.
• Models such as
– Intelligent Driver Model (IDM), velocity difference , Gipps
• Car following parameters
– Observed parameters such as acceleration, velocity and position.
– Non-observed parameters, which are calibrated by driving data sets or assumed while
simulating the behavior.
• Classified into five regimes:
– Acceleration
– Stable Following
– Free Flow
– Approaching
– Braking
• Model parameters can be used in classifying the driving situation and
driver characteristics
7. Classification Approaches
• Car Following Models
– driver behavior is simulated by an equation (IDM)
or multiple equation (Gipps)
– Models face the problems of
• Calibration the parameters
• Sensitivity to the parameters
– Machine learning approaches Such as:
• Supervised Fuzzy inference approach
• Fuzzy clustering algorithm with time continuity
• Neural networks as back-propagation, fuzzy ARTMAP and
Radial Basis Function Networks (RBFNs)
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8. Proposed Model
Previous approaches
disregard the
switching process
dynamics between
different driving
regimes.
Proposed model able to:
• Classify car following regimes.
• Classify normal car following driving behavior, rare
events, and short time events.
• Extract different driving regimes’ characteristics such
as:
• Expected duration
• Probability of moving from the regime to another
• Generate the regime switching process dynamics.
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9. Regime Switching Models I
• Regime switching models:
– Innovative time series analysis tools.
– Used to classify and predict behaviors based on
statistical relations between different time series
variables.
– Used in different fields and models such as economics,
traffic modeling and speech recognition.
– Main frameworks are used for regime switching:
• Threshold models
• Smooth transition models
• Markov switching models
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10. Regime Switching Models II
• Markov switching models
– The most general and the most used models.
– Generalization of Hidden Markov Model (HMM)
– Modeling the stochastic behavior of a time series
into two or more states or regimes.
– Use stochastic models to represent each regime
and a Markov process for switching between
regimes.
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14. Experiment
• The Robert Bosch GmbH Research
Group data set is used.
• Three used data sets are sampled at
100 ms with duration 250,400 and
300 seconds of driving under stop-
and-go traffic conditions.
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15. Results I (Model Parameters)
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Regimes Transition Probabilities
16. Results II (Estimated Values)
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Fitted mean and standard deviation for velocity
observation estimated proposed model
Regimes filtered probabilities
17. Results III (Driving Situations)
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a) Driver behavior switching between
different regimes
b) b) Filtered probabilities of regimes
2 and 4
Stable followingRegime 1
• No abrupt change in velocity or acceleration and gap
• Occurs once for 7 seconds
• speed around 42 km/h reserving gap of 13 m
braking , standing and normal followingRegime 2
•Stable rate of change
•Follower takes the control to start the action
•Start the braking behavior while the distance gap is minimized
•Start the movement action after standing situation
approaching, accelerationRegime 4
• such as
• acceleration if the follower, leader velocities and the distance between
them are increased
• approaching if the follower, leader velocity and distance between them
are decreased
18. Results IV (Driving Situations)
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• Driver behavior affected by abrupt changes in
acceleration (regime 3,green points)
• and in time to collision (regime 5, yellow points)
Sudden Acceleration Change
Regime 3
•Capture situations of changing in behaviors like
•standing to acceleration
•acceleration to following
Sudden velocity difference
Change
Regime 5
•Ten times with expected time 1.02 s
•Gap about 7 meters
•Affects the safety factor time to collision (TTC)
•Driver takes strict decisions to accelerate and decelerate in
a short period
21. Driving Regime based Model Calibration
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The main objective of the calibration process is to minimize the gap between the
observed driving behavior and the car following model simulated driving behavior.
Velocity and gap distance are being used and the root mean squared error
percentage objective function used:
Two calibration scenarios.
• The first calibration is performed for each driver using all the data samples a.
• The second calibration is performed for each driving regime, and the
calibration parameters are used according to each regime probability.
• Results comparing the calibration between the real observed data and the
trajectories simulated by the IDM model.
23. The Calibration Results
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Regime switching has
introducing better fitting for the
data than the standard IDM
calibration.
The mean absolute error of the
IDM calibration is 1.78 m, and
the regime-based error reduced
to 1.11 m with a 38.2% of fitting
enhancements
24. Conclusion
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•The model assumed an automated procedure to distinguish car following regimes
based on trajectory data using Markov regime switching.
•Model able to capture and classify driver behavior patterns in real naturalistic
driving situations.
•Results presented show that the model is able to
•Moving from continuous lower-level modeling of driver behavior (i.e.
microscopic modeling) towards discrete event upper level modeling
•Classify normal car following behavior, rare events, short events
•Able to determine the switching dynamics between different regimes.
•Enhance Models calibration process
•The model can help in
•crash predication.
•Driver assisted.
•Assessment systems.
25. Your Questions & Comments Are Always Welcome
Contact :
Ahmed Bayoumy zaky
ahmed.zaky@feng.bu.edu.eg
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