1. 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

2. 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

3. 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 -> need 4 equations
Can be eliminated with DGPS
Clock diff.
Actual range
Known value

4. 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

5. New Pseudorange equation
pi = f(x, y, z) + Br + vi (n equations)
y = f1(x)
Z = f2(y)
Total we have n+2 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

6. 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

7. 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

8. 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

9. Simulations Grey horizontal plane frames represent buildings
Building heights ranged from m
Vehicle travel from point A to point I

10. Results Error (m) 1 1 2 5 3 2 15 3 15 5 Mean error = 0.436m

11. 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