Presented by Rob Schulz
–
CSC485
–
Spring 2012
–
SUNY Plattsburgh ~ rschu003@gmail.com
1)
Terms / Keywords
2)
Problems
3)
Solutions
4)
Results
5)
Issues
6)
Conclusion
7)
References
Sections
Artificial Neural Network (ANN
)
–
Prediction system which uses input to
determine output by constant updates and reconfigurations. Often called
“feed
-
forward”
Autonomous Vehicle
–
Vehicles which use external data to drive itself with
no human intervention
Intelligent Transportation
Systems
–
the infrastructure, vehicles, users, and
devices networked together to provide better/more advanced driving
abilities and prevention of accidents
Keywords
Vehicle
Trajectory
–
Where a vehicle is planning on
going
Driver
Behavior
–
How a driver drives based on their experiences
& training
VANETs
–
Vehicle Ad
-
Hoc
Networks
Traffic Simulation &
Communication Network
Simulation
–
simulations on
how the network and traffic will act/react
Performance
Evaluation
–
Evaluation how well networks and devices work
Dynamic
Route
Planning
–
Constantly updated traffic routes
Keywords
1)
Planning
2)
Preparation
3)
Crossover
4)
Adjustment
Lane Change Process
What is not a lane change
What is a lane change
Lane Changes
Yi Yang,
Rajive
Bagrodia
1)
Different trainings
2)
Human errors
3)
A
lane change process may cause disturbance
in the
traffic
4)
Costs are high
Problems
-
Collision Avoidance Systems
-
Artificial Neural Networks
-
Intelligent Transport Systems
-
Cooperative Systems
Solutions
1)
Predictions
2)
Gathering of information
Collision Avoidance
Systems
1)
Information
obtained is
limited
A.
Blinded reality
I.
Inclusion of vehicles
II.
Sensor capabilities
Collision Avoidance Systems
-
Issues
Modeling the possible outcomes of lane changes
and their possibilities
1)
Prediction with hidden layers
2)
Learning neural network
Artificial Neural Networks
Input:
-
From the vehicle changing lanes and the vehicle(s) in the destination
lane
-
Positions (longitudinal and lateral)
-
Velocities
-
Acceleration and / or deceleration
Output:
-
Changes in the positions (longitudinal and lateral) of the vehicle
changing lanes
Artificial Neural Networks
Creating the model
-
Weighted Matrix
-
Populated by the inputs over time
-
Changes constantly
-
Back propagation network
-
Established ANN
-
Used extensively
-
Supervised learning
-
Has error thresholds
Artificial Neural Networks
Ranjeet
Singh
Tomar
* and
Shekhar
Verma
Results:
-
ANNs are practically useless
-
Results are not accurate enough
-
While predictions are not always impossible, more
must rely on non
-
impulsive information instead of
driver behavior
Artificial Neural Networks
Drivers (humans) and vehicles (information)
working collaboratively
-
There are some things that humans/drivers cannot predict or know
-
There are things that vehicles do not know
-
Until full autonomous vehicles, cooperative systems work best
Cooperative
systems
1.
Unseen events ahead
2.
Driving behavior
3.
Possible warnings/alerts and even (some) takeover
from the driver
Cooperative systems
•
We have the technology and resources to create
these systems
•
Some infrastructure is already in place
•
For the most part, savings outweigh costs in the
long run
Intelligent Transportation
Systems
Objects/nodes in system
-
Vehicles
-
Traffic lights
-
Traffic cameras
-
Roadside nodes
-
Roadside servers/computational systems
Intelligent Transportation Systems
I.
Honesty
II.
Security
III.
Accuracy
IV.
Reliability
V.
Over population
VI.
U
nder
population
Major Issues Overall
1.
No system is perfect, yet
2.
Hard to predict humans
3.
Computers aren’t human
4.
Fully autonomous vehicles are
the future
happening
now
CONCLUSION
Neural Network Based Lane Change
Trajectory
Prediction in Autonomous Vehicles
Ranjeet
Singh
Tomar
* and
Shekhar
Verma
Published in Transactions on Computational
Science XIII
–
2011
Springer Berlin / Heidelberg
http://www.springerlink.com/content/j6p81563n
7302666/fulltext.pdf?MUD=MP
Evaluation of VANET
-
based Advanced
Intelligent Transportation
Systems
Yi Yang,
Rajive
Bagrodia
Published in VANET ’09 Proceedings of
the sixth ACM international workshop on
VehiculAr
InterNETworking
http://dl.acm.org/ft_gateway.cfm?id=16
14273&ftid=685868&dwn=1&CFID=8048
4367&CFTOKEN=49494235
REFERENCES
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