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ForAutomobiles
Submitted bySobin Chandras Jayaram
(7946)
Guide:Mrs. Sheeba.O
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Introduction
A smart car should primarily be able of sensing itsstate.
Making cars safer is one major challenges of theautomotive industry.
Advanced driver assistance systems are build intocommercial cars to assist the driver and preventaccidents from happening.
A large part of these systems is vision based.
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ADAS an Insight
Advanced Driver Assistance Systems areembedded computing systems whichperforms actions which can assist the
driver e.g. Lane Detection
Driver Drowsiness Detection
Sign Board Recognition
Collision Detection
Night Vision
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All about ADAS
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ADAS : Lacking Something?
Development of vision for automobiles isslowed down by the challenges in analysisof rapidly changing image flows in real-
time.
Huge amount of data images contain.
Large computational and memoryresources needed for real-time completionof even conceptually simple task.
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Do you think we are Lacking an highend reference system to compare
with?
Definitely
Where Man fails Mother Nature Wins.
Natural Vision systems havebeen improved throughmillennia of evolution and aremore robust, compact, andefficient than artificialcounterparts.
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Why Biological Models?
Vision systems improved throughmillennia of evolution.
Insects rely on vision for cruise control,navigation and collision avoidance.
Why not to take advantage of theknowledge about these systems?
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Underlying Biological Models
Lobula GiantMovement Detector(LGMD)
Topological FeatureEstimator (TpFE)
Attention FocusingA
lgorithm (A
FA
)
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LGMD Behavior and Emulation
Basic components of the proposed bio-inspiredsystem contain input visual information.
Input Visual information is passed to the LGMDmodule for evaluation.
Spatial attention of that LGMD is driven by the AFAmodule.
Finally, information regarding the nature of theinput object is extracted by the TpFE module.
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Computational LGMD Module
The model parameters and state variablesare tuned such that everything can besatisfactorily executed.
The LGMD threshold is made adaptiveinstead of constant
Threat scenarios are classified accordingto the firing rate of a neuron whichreceives information from LGMD andThreshold Cell.
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Attention Focusing Algorithm
Heuristic search showsinformation is containedwithin reduced regions of thelast model layers
Focusing of region of interestreduces amount ofinformation to be processed.
The AFE divides the inputframe by defining anattention grid formed by thegroup of cells, some of whichare enabled other disabled.
Modules are used foractivation of attention cells.
Attention, Activating andDisabling Modules
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Topological Feature Estimator
LGMD potential calculationinvolves loss of informationabout shape of object, howeverit is vital in many cases.
Sharp turning or bulge in roadcan shake the camera makingthe background move rapidly.
TpFE module overcomes theseproblems by early classificationof approaching objects.
Vertical-shaped, Horizontal-shaped, Spatially Global,Square-shaped class.
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Augmented Features of
Intelligent Vehicles
Lane Departure Warning
Driver Drowsiness Detection
Sign Board Recognition
Collision Detection and Warning
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Lane Departure Warning
To navigate autonomously or follow a road,
intelligent vehicles need to detect lanes.
It seems that the best cue for lane detection is touse the lane markings painted on roads
The video sensors are the best candidate for
finding lane markings.
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Parameters Considered in
Lane Detection
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Driver Drowsiness Detection
Drowsiness slows reaction time, decreasesawareness, and impairs judgment, just like drugsor alcohol.
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Continued..
A camera will be monitoring drivers eye.
If situation arises the warning system will be activated,alerting the driver
The eye regions in the face present great intensitychanges, the eyes are located using intensity changes inthe face.
Once the eyes are located, measuring the distances
between the intensity changes in the eye areadetermine whether the eyes are open or closed.
If the eyes are found closed for 5 consecutive frames,the system issues a warning signal.
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Parametric Variation
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Sign-Board Recognition
The principle of Pattern Recognition is employed inSign-board classification.
Traffic signals and Sign boards are employed so as
to direct the driver.
A database of various signs to be recognised is
made.
The real-time image is compared with the
averaged image present in the database
Decision are made with image having highestcorrelation
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Components of Pattern
Recognition System
A Sensor
A Pre-processing mechanism
A Feature extraction mechanism
A Classification Algorithm A Set of examples already classified or described
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Thrust Areas of Research
Adaptive Cruise Control
Lane Keeping Assistance
Pre-Crash Safety
Blind spot Detection/Lane ChangeAssistant
Night Vision
Motion Stabilization
Parking Assistant
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Benefits vs. Disadvantages
More safety and comfort for the driver and other
traffic participants.
Better utilization of the infrastructure, lowerenergy consumption.
Users of advanced driver assistance systems havethe tendency to perform other activities, because
they have a false sense of security.
The driver can intervene when the system makes amistake, but also a disadvantage, because the
driver can make a wrong intervention.
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Conclusion
Conventional engineering methodologies follow atop-down approach. Alternatively, biology hasevolved to solve complex sensory-perception-actuation problems by following what can be
considered as a bottom-up approach.
Future systems will be integrated with othersystems and these future systems will requiremore computational power.
More Research is needed to develop an practicallyfail-safe assistance system.
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References
[1] Gustavo Linan-Cembrano, Luis Carranza, Clair Rind,Akos Zarandy, Martti Soininen, and Angel Rodriguez-Vasquez, Insect-Vision Inspired Collision Warning VisionProcessor for Automobiles, IEEE Circuits and Systems, Vol.8, pp. 6-24, 2008
[2]R.Bishop , A survey of intelligent vehicle applicationsworldwide. Proc. of the IEEE 2000 Intelligent Symposium,2006, pp. 25-30.
[3]M.Bertozzi, et al., Artificial vision in road vehicles,Proceedings of the IEEE, vol. 90, July 2006, pp. 1258-1271.
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