Presented by: Trung Ngo Class: ICS 280 Donald Bren School of Information and Computer Sciences

1. Presented by: Trung Ngo Class: ICS 280 Donald Bren School of Information and Computer Sciences Department of Informatics University of California, Irvine Autonomous Vehicle Positioning with GPS in Urban Canyon Environments By: Youjing Cui and Shuzhi Sam Ge

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 eliminatedwith 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 60-180m • Vehicle travel from point A to point I

10. Results Error (m) 1 1 2 5 3 2 3 15 5 15 Mean error = 0.436m Mean error = 1205 m(???)

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