1. A low-maintenance coating on a substrate, the coating comprising a base film positioned directly over a first major surface of the substrate and a film comprising titania positioned directly over the base film, wherein the base film has a thickness of less than about 100 angstroms, and wherein the film comprising titania has a thickness of less than 50 angstroms.
2. The low-maintenance coating of claim 1 wherein the film comprising titania includes titanium dioxide or substoichiometric titanium oxide.
3. The low-maintenance coating of claim 1 wherein the base film comprises silica.
4. The low-maintenance coating of claim 3 wherein the base film consists essentially of silicon dioxide.
5. The low-maintenance coating of claim 1 wherein the film comprising titania has a thickness of less than 35 angstroms.
6. The low-maintenance coating of claim 1 wherein the film comprising titania has a thickness of between about 5 angstroms and about 30 angstroms.
7. The low-maintenance coating of claim 1 wherein the substrate is a window pane mounted in a window frame, and wherein the coated first major surface is exposed to an outdoor environment.
8. The low-maintenance coating of claim 1 wherein the base film and film comprising titania are both sputtered films.
9. A low-maintenance coating on a glass sheet, the coating comprising a base film positioned over a first major surface of the glass sheet and a film comprising titania positioned directly over the base film, wherein the base film comprises silica and has a thickness of less than 100 angstroms, and wherein the film comprising titania has a thickness of less than 100 angstroms.
10. The low-maintenance coating of claim 9 wherein the film comprising titania comprises titanium dioxide or substoichiometric titanium oxide.
11. The low-maintenance coating of claim 9 wherein the base film consists essentially of silicon dioxide.
12. The low-maintenance coating of claim 9 wherein the film comprising titania has a thickness of less than 50 angstroms
13. The low-maintenance coating of claim 9 wherein the film comprising titania has a thickness of less than 35 angstroms.
14. The low-maintenance coating of claim 9 wherein the film comprising titania has a thickness of between about 5 angstroms and about 30 angstroms.
15. The low-maintenance coating of claim 9 wherein the glass sheet is a window pane mounted in a window frame, and wherein the coated first major surface is exposed to an outdoor environment.
16. The low-maintenance coating of claim 9 wherein the silica base film and film comprising titania are both sputtered films.
17. An insulating glass unit comprising two spaced-apart panes bounding a between-pane space, wherein at least one of the panes has a desired major surface bearing a low-maintenance coating, wherein said desired major surface is an exterior surface of the unit and faces away from the between-pane space, the coating comprising a base film positioned directly over said desired major surface and a film comprising titania positioned directly over the base film, wherein the base film comprises silica has a thickness of less than 100 angstroms and the film comprising titania has a thickness of less than 50 angstroms.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method for providing georeferenced predictive information to motor vehicles, the method comprising the acts of:
receiving, by one or more backend servers from a vehicle over a wireless network, vehicle route information corresponding to a vehicle route;
reconstructing the vehicle route on a server-side digital map, by the one or more backend servers, using the vehicle route information;
identifying, by the one or more backend servers, predictive information that is relevant to the vehicle route, wherein the predictive information is georeferenced to a particular location or area;
converting, by the one or more backend servers, the identified predictive information into a format corresponding to the vehicle; and
transmitting, by one or more backend servers over the wireless network, the identified predictive information to the vehicle, wherein the transmitted predictive information is configured to be used by the vehicle in constructing the vehicle route on a vehicle-side digital map.
2. The method of claim 1, wherein receiving the vehicle route information comprises receiving, by one or more backend servers over the wireless network, a planned vehicle route from a navigational system of the vehicle based on user-defined destination information.
3. The method of claim 1, wherein receiving the vehicle route information comprises receiving, by one or more backend servers over the wireless network, an estimated vehicle route from the vehicle based, at least in part, on driver habit information.
4. The method of claim 1, further comprising populating the server-side digital map with available predictive information which has been received and stored by the one or more backend servers.
5. The method of claim 4, wherein identifying the predictive information comprises identifying, by the one or more backend servers, a subset of the available predictive information that identifies one or more current conditions affecting at least a portion of the vehicle route.
6. The method of claim 1, further comprising receiving, by one or more backend servers over the wireless network, information from the vehicle corresponding to a version of the vehicle-side digital map.
7. The method of claim 6, wherein converting the identified predictive information comprises converting, by the one or more backend servers, the identified predictive information into the format corresponding to the vehicle based on said version of the vehicle-side digital map.
8. The method of claim 1, wherein the predictive information comprises information corresponding to one or more of traffic conditions, weather conditions, traffic light phase and timing information and approaching speed limits.
9. A system for providing georeferenced predictive information to motor vehicles, the system comprising:
a backend server system coupled to a wireless connection and configured to:
receive, from a vehicle, vehicle route information corresponding to a vehicle route,
reconstruct the vehicle route on a server-side digital map using the vehicle route information,
identify predictive information that is relevant to the vehicle route, wherein the predictive information is georeferenced to a particular location or area,
convert the identified predictive information into a format corresponding to the vehicle, and
transmit, over the wireless network, the identified predictive information to the vehicle, wherein the transmitted predictive information is configured to be used by the vehicle in constructing the vehicle route on a vehicle-side digital map.
10. The system of claim 9, wherein the vehicle route information is a planned vehicle route, and wherein the backend server system is further configured to receive the planned vehicle route, over the wireless network, from a navigational system of the vehicle based on user-defined destination information.
11. The system of claim 9, wherein the vehicle route information is an estimated vehicle route based, at least in part, on driver habit information.
12. The system of claim 9, wherein the backend server system is further configured to populate the server-side digital map with available predictive information which has been received and stored by the backend server system.
13. The system of claim 12, wherein the backend server system is further configured to identify the predictive information by identifying a subset of the available predictive information that identifies one or more current conditions affecting at least a portion of the vehicle route.
14. The system of claim 9, wherein the backend server system is further configured to receive, over the wireless network, information from the vehicle corresponding to a version of the vehicle-side digital map.
15. The system of claim 14, wherein the backend server system is configured to convert the identified predictive information by converting the identified predictive information into the format corresponding to the vehicle based on said version of the vehicle-side digital map.
16. The system of claim 9, wherein the predictive information comprises information corresponding to one or more of traffic conditions, weather conditions, traffic light phase and timing information and approaching speed limits.
17. A computer program product comprising:
a processor storage medium having computer executable program code embodied therein to provide georeferenced predictive information to motor vehicles, the processor storage medium having:
computer executable program code to reconstruct a vehicle route on a server-side digital map based on vehicle route information that was provided by a vehicle,
computer executable program code to identify predictive information that is relevant to the vehicle route, wherein the predictive information is georeferenced to a particular location or area,
computer executable program code to convert the identified predictive information into a format corresponding to the vehicle, and
computer executable program code to provide the identified predictive information to the vehicle, wherein the transmitted predictive is configured to be used by the vehicle in constructing the vehicle route on a vehicle-side digital map.
18. The computer program product of claim 17, wherein the vehicle route information comprises a planned vehicle route provided by a navigational system of the vehicle based on user-defined destination information.
19. The computer program product of claim 17, wherein the vehicle route information comprises an estimated vehicle route based, at least in part, on driver habit information.
20. The computer program product of claim 17, further comprising populating the server-side digital map with available predictive information which has been received and stored by the one or more backend servers.
21. The computer program product of claim 20, wherein the computer executable program code to identify the predictive information comprises computer executable program code to identify a subset of the available predictive information that identifies one or more current conditions affecting at least a portion of the vehicle route.
22. The computer program product of claim 22, wherein the computer executable program code to convert the identified predictive information comprises computer executable program code to convert the identified predictive information into the format corresponding to the vehicle based on a version of the vehicle-side digital map provided by the vehicle.
23. The computer program product of claim 17, wherein the predictive information comprises information corresponding to one or more of traffic conditions, weather conditions, traffic light phase and timing information and approaching speed limits.