Traffic aware lane determination for human driver and autonomous vehicle driving system

• US 9,672,734 B1
• Filed: 04/08/2016
• Issued: 06/06/2017
• Est. Priority Date: 04/08/2016
• Status: Active Grant

• Current Assignee: Sivalogeswaran Ratnasingam
• Original Assignee: Sivalogeswaran Ratnasingam
• Inventors: Ratnasingam, Sivalogeswaran
• Primary Examiner(s): Amin, Bhavesh V
• Application Number:
  US15/093,761
• Time in Patent Office: 424 Days
• Field of Search: None
• CPC Class Codes: G01C 21/3658 Lane guidance
• CPC Class Codes: G06V 20/588 Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/012 from other sources than vehicle or roadside beacons, e.g. mobile networks
• CPC Class Codes: G08G 1/0133 for classifying traffic situation
• CPC Class Codes: G08G 1/0145 for active traffic flow control
• CPC Class Codes: G08G 1/015 with provision for distinguishing between two or more types of vehicles, e.g. between motor-cars and cycles
• CPC Class Codes: G08G 1/096716 where the received information does not generate an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096725 where the received information generates an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096741 where the source of the transmitted information selects which information to transmit to each vehicle
• CPC Class Codes: G08G 1/096775 where the origin of the information is a central station
• CPC Class Codes: G08G 1/0968 Systems involving transmission of navigation instructions to the vehicle
• CPC Class Codes: G08G 1/096811 where the route is computed offboard
• CPC Class Codes: G08G 1/096844 where the complete route is dynamically recomputed based on new data
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04W 4/44 for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]

Abstract

A system, method, and computer program product for determining lane information in a road segment to drive a first vehicle to minimize travel time. According to an embodiment, navigation data of the first vehicle and at least one other vehicle in a road segment is sent to a computer server system via their respective clique leaders through a communication network. The lane information may include whether a change of lane is required, a lane to avoid, an optimum lane, and rank order of drivable lanes according to increasing order of travel time for the first vehicle to minimize travel time. The determined lane information is sent to the appropriate user device through its clique leader. The user device presents the lane information to a human driver and/or autonomous vehicle driving system of the first vehicle appropriately.

Claims

1. A method for determining lane congestion information among a plurality of vehicles on a multi-lane road, comprising:

receiving, through a user input of a user device, a driving destination;

sensing, with a sensor on a first vehicle containing the user device, the presence of a user device in a second vehicle within a vehicle awareness zone relative to the first vehicle;

receiving, through a wireless radio of the user device of the first vehicle, vehicle data of the second vehicle;

transmitting the vehicle data of the second vehicle to a remote server using the wireless radio of the user device in the first vehicle; and

switching, in at least one of the user device and the remote server, the mode of operation between a first mode that provides a lane recommendation based on traffic conditions ahead of the first vehicle within the lane the first vehicle is traveling, and a second mode that provides a lane recommendation based on traffic conditions ahead of the first vehicle in a plurality of lanes, the lane recommendation in the second mode including a ranking of lanes based on a travel time estimate for each drivable lane.

2. The method of claim 1, further comprising forming a clique between the first vehicle and the second vehicle based on at least one of a predetermined strength-of-signal threshold between the user device of the first vehicle and the user device of the second vehicle, a predetermined distance sensed between the user device of the first vehicle and the user device of the second vehicle, and a direction of travel of the first vehicle and the second vehicle.

3. The method of claim 1, wherein the user device of the first vehicle is designated as a clique leader configured to interact with the remote server, the designation being based on a comparison of available processing resources amongst user devices in the first and second vehicles.

4. The method of claim 1, wherein the vehicle data of the second vehicle is received using a Wi-Fi or a peer-to-peer communications protocol.

5. The method of claim 1, wherein the vehicle data of the second vehicle includes navigation data.

6. The method of claim 5, wherein the navigation data of the second vehicle is transmitted to the remote server without storage in the user device of the first vehicle.

7. The method of claim 5, wherein the navigation data of the second vehicle transmitted to the remote server is predetermined prior to commencement of a trip being undertaken by the second vehicle.

8. The method of claim 5, further comprising receiving a lane recommendation based on a comparison of navigation data of the first vehicle and the navigation data of the second vehicle.

9. The method of claim 8, wherein the lane recommendation is based on a density of vehicles in the lane in which the first vehicle is traveling.

10. The method of claim 8, wherein the lane recommendation is based on a calculated minimum time of travel to the destination.

11. The method of claim 1, further comprising filtering data received from the second vehicle prior to transmitting the vehicle data of the second vehicle to the remote server.

12. The method of claim 1, further comprising receiving, from at least one of the user device, another vehicle, and the remote server, a lane recommendation based on the second vehicle being classified as a non-automobile vehicle, wherein the non-automobile classification is based on at least one of user input, navigational data, characteristics of navigation, stored data, sensor data, and data from the second vehicle.

13. The method of claim 1, further comprising receiving, from at least one of the user device, another vehicle, and the remote server, a lane recommendation based on a vehicle ahead of the first vehicle being driven in a manner causing a bottleneck in traffic ahead of the first vehicle.

14. The method of claim 1, further comprising receiving a lane recommendation, from at least one of the user device, another vehicle, and the remote server, without providing GPS data to the remote server after sensing the presence of the user device in the second vehicle.

15. The method of claim 1, further comprising selectively transmitting a lane recommendation, from at least one of the user device, another vehicle, and the remote server, amongst a plurality of vehicles, including the first and second vehicles, to minimize traffic oscillation between lanes.

16. The method of claim 1, wherein the switching, in at least one of the user device and the remote server, between the first mode and the second mode is further based on at least one from a group including map data, traffic condition, road condition, weather condition, surrounding condition, and driver state.

17. The method of claim 1, further comprising storing the vehicle data of at least the second vehicle to generate a traffic profile, the traffic profile being based on a time of day.

18. The method of claim 17, further comprising time-stamping the vehicle data of at least the second vehicle to generate the traffic profile.

19. The method of claim 17, wherein the traffic profile is a profile of a predefined segment of road.

20. The method of claim 17, wherein the traffic profile is a profile of a driver.

21. The method of claim 1, further comprising ranking each lane of a predefined segment of road on which the first vehicle is traveling, the ranking being based on at least one of whether the first vehicle is an automobile, the presence of a travel restriction in a lane of the road being travelled by the first vehicle, and average lane speed of each lane within the predefined segment of road.

22. The method of claim 1, further comprising receiving, through the wireless radio of the user device of the first vehicle, an estimated time for an obstructing vehicle ahead of the first vehicle to clear a lane in which the first vehicle is traveling, and receiving a lane recommendation if the estimated time exceeds an estimated travel time for the first vehicle to reach a location of the obstructing vehicle.

23. A method for determining lane congestion information among a plurality of vehicles on a multi-lane road, comprising:

receiving, through a user input of a user device, a driving destination;

receiving, through a wireless radio of the user device, vehicle data of a second vehicle within a vehicle awareness zone relative to a first vehicle containing the user device, wherein the vehicle data includes vehicle type data and navigation data, wherein the navigation data is based on at least one of a driving destination, road map data, and lane obstruction data; and

determining whether a lane recommendation is needed for the first vehicle based on the vehicle data of the second vehicle.

24. The method of claim 23, wherein the vehicle awareness zone is defined by at least one of a predetermined strength-of-signal threshold between the user device of the first vehicle and a user device of the second vehicle, a predetermined distance sensed between the user device of the first vehicle and the user device of the second vehicle, and a direction of travel of the first vehicle and the second vehicle.

25. The method of claim 23, further comprising sensing, with a sensor in the first vehicle, the presence of a user device in the second vehicle and obtaining the navigation data of the second vehicle from the user device in the second vehicle the presence sensed by the sensor meets a predetermined threshold, and wherein the lane recommendation is based on at least the navigation data of the second vehicle and determined to reduce at least one of traffic bottleneck and traffic oscillation between lanes.

26. A system for determining lane congestion information among a plurality of vehicles on a multi-lane road, comprising:

a first database including navigation data pertaining to a geographical area;

a processor configured to receive navigation data from a user device in a first vehicle; and

a non-transitory computer readable medium encoded with a computer program coupled to said processor to;

compare the navigation data of the first vehicle with the navigation data stored in said first database;

switch, in at least one of the user device and a remote server, between a first mode that provides a lane recommendation based on vehicle data of the vehicles ahead of the first vehicle within the lane the first vehicle is traveling, and a second mode that provides a lane recommendation based on traffic conditions ahead of the first vehicle in a plurality of lanes, the lane recommendation in the second mode including a ranking of lanes based on a travel time estimate for each drivable lane wherein vehicle data includes at least navigation data; and

send the lane recommendation to the user device in the first vehicle.

27. The system of claim 26, further comprising the non-transitory computer readable medium encoded with a computer program coupled to said processor to:

receive navigation data of a second vehicle within a vehicle awareness zone relative to the first vehicle; and

compare the navigation data of the first vehicle with the navigation data of the second vehicle;

wherein the computer program encoded on said non-transitory computer readable medium includes instructions that when executed by said processor cause said processor to generate a plurality of lane recommendations deliverable to a respective number of vehicles to reduce at least one of traffic bottleneck and traffic oscillation between lanes.

28. A computer program product embodied in a non-transitory computer readable storage medium for providing a lane recommendation to a user in a first vehicle, the computer program product comprising programming instructions causing a coupled processor to:

receive navigation data of a first vehicle;

sense a presence of a user device in a second vehicle within a vehicle awareness zone relative to the first vehicle;

receive vehicle data of the second vehicle;

transmit the vehicle data of the second vehicle to a remote server using a coupled wireless radio; and

determine, in at least one of the user device and the remote server, a lane recommendation in a first mode based on vehicle data of the vehicles ahead of the first vehicle within the lane the first vehicle is travelling or in a second mode based on vehicle data of the vehicles ahead of the first vehicle in a plurality of lanes wherein each respective lane is ranked based on a travel time for each lane wherein vehicle data includes at least navigation data.

29. The computer program product of claim 28, further comprising programming instructions causing the coupled computer to receive a lane recommendation from the remote server and transmit the lane recommendation to the second vehicle.

30. The computer program product of claim 28, further comprising programming instructions causing the coupled computer to receive a plurality of lane recommendations from the remote server and multicast the lane recommendations to a plurality of vehicles within the vehicle awareness zone.