This document presents an approach for context-aware driver behavior detection using pervasive computing. It aims to reduce road accidents caused by driver errors by alerting drivers in a timely manner. The approach uses a three-tier network to gather context data from sensors using wireless sensor networks. Swarm intelligence and ant colony optimization are then used to infer driver behavior from the collected context data and detect unacceptable behaviors like fatigue or intoxication. The approach integrates wireless sensor networks, vehicle ad hoc networks, and swarm intelligence for comprehensive and reliable driver behavior monitoring.

1. Pervasive Computing:
Intelligent Transportation System
Presented by
Rajani Suryavanshi
March 16, 2015
Guided By
Prof. Shiv Sutar
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2. CONTEXT-AWARE DRIVER BEHAVIOUR DETECTION
USING PERVASIVE COMPUTING
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3. Agenda
Problem Statement
Objective
Literature Survey
Main Reason of Unacceptable Behaviour.
Context Information
Mechanism of Driver Behaviour Detection
Swarm Intelligence Optimization
Ant-Colony Optimization Algorithm
Advantages
Drawbacks
Future Scope
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4. Problem Statement
Road accidents and traffic problems related to
bad driving behaviour.
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5. Objective
1 To reduce the number of road accidents caused due to
driver’s errors.
2 To alert the driver on time and corrective measures to
be taken as per requirement. requirement
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6. Literature Survey
Paper Name Implementation Limitation Year Journal
1
Mobile Application For
Safe Driving
Using accelerometer of a
smartphone
Only vehicle analysis done 2014 IEEE
2
An Efficient System to
Identify User Attentive-
ness Based on Fatigue De-
tection
Blinking rate,nap and
yawning detection
Vehicle and environtment
factors not considered
2014 IEEE
3
Head Pose and Gaze Di-
rection Tracking for De-
tecting a Drowsy Driver
Using Cumulative Distri-
bution Function analysis
and corner detection algo-
rithm
Vehicle and environt con-
ditions not taken into con-
sideration
2014 IEEE
4
Detection of Intoxicated
Drivers Using Online
System Identification of
Steering Behavior .
Fatal Vision goggles to
generate drunk behaviour
dataset
Level of uncertainty, use
of generic models
2014 IEEE
5
An Effective Variable
Selection Algorithm for
Aggressive/Calm Driving
Detection via CAN Bus
Steering wheel analysis
Other types of driver be-
haviour not detected.
2013 IEEE
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7. Survey
Every year, people dying cause of road accidents is more
than people dying cause of natural disasters or terrorist
attacks.
A road accident is an outcome of either the bad road
quality or a break in the balance between the
environments demand and the driver’s ability to act
accordingly.
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8. Survey
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9. Pervasive Computing 9/23

10. WAYWARD
FATIGUE
RASH DRUNK
ACCEPTABLE
TYPES OF DRIVERS
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11. Main Resons of Unacceptable Behaviour
The main reason behind the unacceptable behaviour of the
drivers:
1. Fatigue 2. Drunk
Fatigue can be further classified as:
1. Sleep Related
1 sleep deprivation
2 extended wakefulness
3 time of the day (circadian rythm effect)
2. Task Related
1 task demand
2 task duration
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12. Context Information
Context information varies as per the requirement of the
application
A few early approaches used the following information
average speed choice
No of traffic rule violation
frequent attention lapses
No of overtakes
Measures taken by driver to counter SR fatigues
rolling down windows
blasting radios
switching on AC
SR fatigue can be reduced only by a nap or caffeine.
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13. Our Approach
There is a 3 tier network used for simplicity
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14. Mechanism of Driver Behaviour Detection System
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15. Driver Behaviour Inference From Gathered Context-Data
Using Swarm Intelligence Optimization
Optimization Problem: Problem inferring least cost path
solution vector by processing available or gathered
context information
Search Space: Contains local best positions,forward
nodes
Solution Space: The 5 possible states also known as
Global best positions. Use of automated intelligent tools
like Swarm Intelligence.
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16. Inference from context information
1 Gather data from sensors using WSN
2 Form if-then inference cycle clusters hence narrowing
search space.
3 Clusters classified based on inference decisions
Decision levels depend on rule-decisons patterns
Each classifier traversed the search space
Swarm Intelligence takes individual bit ofcontext
information and does collaborative analysis resulting into
POPULATIONS.
Population’s history is searched and updated to make the
algorithm more efficient after every iteration and closer
towards optimal solution.
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17. Ant Colony Optimization Algorithm
1 Initialize pheromone trails;
2 repeat at this stage each loop is called an iteration
3 Each ant is positioned on a starting node;
4 repeat at this level each loop is called a step
5 Each ant applies a state transition rule like rule (2) to
incrementally build a solution and a local
pheromone-updating rule like rule (4);
6 until all ants have built a complete solution
7 global pheromone-updating rule like rule (5) is applied.
8 until terminating condition is reached
Link
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18. Working of Algorithm
1 Collecting a set of driver-behavior info[POPULATION].
2 Forward ants are created periodically.
3 Travel from a source (raw context data) to destination
node (appropriate driver behavior).
4 The forward ant packets act concurrently, independently
and asynchronously where each forward ant searches for
a minimum delay path.
5 The forward ants then become intermediate nodes, with
each of them having nearest neighbors.
6 While traversing the search space, the forward ants
collect information about the traveling time and the
node identifiers along the path.
7 The forward ant becomes a backward ant and goes back
to its source node by moving along the same path in the
opposite direction.
8 If the same path is followed for a few iterations, then
that behavior is labeled as inferred.
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19. Advanatges of this Aproach
1 Node Centric
2 More comprehensive: WSN, VANET, Swarm
Intelligence.. All under one architecture
3 Self organising WSN
For sensing
For computing
For communication
4 Highly reliable
5 Unnecessary transmission of insignificant data is
prevented
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20. Drawbacks in this Approach
Faulty sensors and approximation errors
Limited battery life of WSN
Duplication of data
Implemented only on simulator
Training data-set not available.
Assumption about hardware
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21. Conclusion
Our objective of reducing accidents caused due to
driver’s errors can be achieved with the proper
implementation of the context-aware driver behaviour
detection mechanism.
The driver and the nearby vehicles will be notified on
time and corrective measures can be taken as per
requirement.
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22. Future Scope
A few possible future works are identified as follows:
Corrective actions can be disseminated using VANET
upon detection of driver in an undesirable state.
To build a prototype of the model by identifying
functional requirements and design parameters.
The work can be modified to predicting unsafe driving
behaviour, rather than just detection.
WSNs could be replaced with yet faster and compact
Micro-Electro Mechanical Systems (MEMS) based smart
dust, which is a micron scale wireless sensor.
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23. References
BASE PAPER : ”Context-awareness based intelligent
driver behavior detection: Integrating Wireless Sensor
networks and Vehicle ad hoc networks”,Abhishek Gupta,
Venimadhav Sharma, Naresh Kumar Ruparam, Surbhi
Jain,Abdulmalik Alhammad1, Md Afsar Kamal
Ripon.(For ref. 3 and 4)
P. Philip, P. Sagaspe, N. Moore, et al., “Fatigue, sleep
restriction and driving performance”. Accident Analysis
and Prevention, 37(3), pp.473–478 (2005).
R. C. Eberhart and J. Kennedy, Computational
Intelligence -Concepts to Implementations. Elsevier,
2007.
M. Baldauf, S. Dustdar, F. Rosenberg. A Survey on
context-aware systems. Int. J. Ad Hoc Ubiquitous
Computers. pp.263-277, 2007.
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