Presented by: Trung Ngo

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Autonomous Vehicle Positioning with GPS in Urban
Canyon Environments
By Youjing Cui and Shuzhi Sam Ge

• Presented by Trung Ngo
• Class ICS 280
• Donald Bren School of Information and Computer
  Sciences
• Department of Informatics
• University of California, Irvine

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Motivation

• GPS signals in urban canyon environments
• Blocked by high rise buildings
• Not enough available satellite signals
• Existing approaches
• Increase the number of visible satellites
• Example GLONASS Make eight or more satellites
  available
• Integrate receivers with sensors
• Inertial navigation system (INS)
• Use external references
• Such as altimeter or a precise clock
• Find a constrained solution
• In some cases, the altitude can be considered
  constant and assumed to be known

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Pseudorange equation

• At least for satellites are needed to solve the
  standard pseudo-range equation
• User position (x, y, z)
• N satellites (i 1.. N)
• Pseudorange measurement pi f(x, y,
  z) Br vi
• 4 unknowns -gt need 4 equations

Can be eliminatedwith DGPS
Clock diff.
Actual range
Known value
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New approach

• Observation
• Most pieces roads are straight lines, arcs, or
  other simple smooth curves
• Thus, the user position (x, y, z) can be simply
  modeled as (x, f1(x), f2(x)) where f1 and f2 are
  known based the road models
• In this paper, a new constrained solution is
  provided to solve the problem by approximately
  modeling the path of vehicle by pieces of curves
  in the urban canyon environments

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New Pseudorange equation

• pi f(x, y, z) Br vi (n
  equations)
• y f1(x)
• Z f2(y)
• Total we have n2 equations
• There are 4 unknown variables, thus with n 2 we
  can solve the problem.
• Two mathematical approaches proposed in the paper
  for this, including an extended version of Kalman
  Filtering technique (EKF)
• Thus, the minimum number of satellites required
  drops to two

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Road intersection problem

• If user travel in only one road, the problem
  becomes simpler.
• However, in real urban environments, the road
  segments are connected by intersections

• It is important to know which road the vehicle
  takes when crossing road intersections

Vehicle comes to an intersection
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Map Representation

• Road segments are connected by intersections

• Road segments can be known based on city maps
• For each road, the following information need to
  be stored
• Road shape (e.g., line, arc )
• Positions of intersections on the road
• Indexes of roads connected at each intersection
• Additional parameters of the road model

Roads segment and intersection representation
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Determine next road at intersections

• In this paper, the interacting multiple model
  (IMM) algorithm employed to solve the problem.
• This algorithm has been widely used in
  multi-target tracking applications.
• Other statistical techniques commonly used in
  robot navigation applications can be employed
  also
• Neighbor algorithm
• Tracking-splitting filter
• Join-likelihood algorithms
• Markov approaches

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Simulations

• Grey horizontal plane frames represent buildings
• Building heights ranged from 60-180m
• Vehicle travel from point A to point I

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Results
Error (m)
1
1
2
5
3
2
3
15
5
15
Mean error 0.436m
Mean error 1205 m(???)
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Conclusion

• A constrained method proposed
• Approximately modeling the path of the vehicle in
  the urban canyon environment as pieces of curves
• Helps to reduce the minimum number of available
  satellites reduces to two