Navigation system with traffic state detection mechanism and method of operation thereof

1. A navigation system comprising: a control circuit configured to: generate a video clip by parsing an interval of a sensor data stream for a region of travel; analyze the video clip submitted to a deep learning model, already trained, including identifying a traffic flow estimate; access a position coordinate for calculating a distance to intersection; generate a traffic flow state by fusing a corrected speed, the traffic flow estimate, and the distance to intersection; merge a vehicle maneuvering instruction into the traffic flow state for maneuvering through the region of travel; and a communication circuit, coupled to the control circuit, configured to: communicate the traffic flow state for displaying on a device.

2. The system as claimed in claim 1 wherein the control circuit is configured to: generate the corrected speed by submitting a speed and a gyroscope angular rate to a filter/correction unit; and detect a turning state when the speed is decreasing and the gyroscope angular rate exceeds 5 degree per second.

3. The system as claimed in claim 1 wherein the control circuit is configured to load the traffic flow estimate in a multiple information fusion model to detect a free traffic state when a distance to a vehicle in a same traffic lane is 100 meters or more.

4. The system as claimed in claim 1 wherein the control circuit is configured to detect a slow traffic state when a multiple information fusion model identifies other vehicles travelling at less than half of a posted speed limit provided by a background map database.

5. The system as claimed in claim 1 wherein the control circuit is configured to detect a congested traffic state when a multiple information fusion model identifies a vehicle travelling in an adjacent traffic lane that comes to a near or complete stop.

6. The system as claimed in claim 1 wherein the control circuit is configured to generate a traffic state update by compiling the traffic flow state over a fixed period of time including counting an occurrence of a congested traffic state, a slow traffic state and a free traffic state.

7. The system as claimed in claim 1 wherein the control circuit is configured to generate the vehicle maneuvering instruction based on the position of a vehicle and an occupancy of an adjacent lane.

8. The system as claimed in claim 1 wherein the communication circuit is configured to send a traffic state update, through a network for updating a background map database, a real-time traffic model, and a hazardous warning model.

9. A method of operation for a navigation system comprising: generating a video clip by parsing an interval of a sensor data stream for a region of travel; analyzing the video clip submitted to a deep learning model, already trained, including identifying a traffic flow estimate; accessing a position coordinate for calculating a distance to intersection; generating a traffic flow state by fusing a corrected speed, the traffic flow estimate, and the distance to intersection; merging a vehicle maneuvering instruction into the traffic flow state for maneuvering through the region of travel; and communicating the traffic flow state for displaying on a device.

10. The method as claimed in claim 9 further compromising: generating the corrected speed by submitting a speed and a gyroscope angular rate to a filter/correction unit; and detecting a turning state when the speed is decreasing and the gyroscope angular rate exceeds 5 degree per second.

11. The method as claimed in claim 9 further comprising loading the traffic flow estimate in a multiple information fusion model for detecting a free traffic state when a distance to a vehicle in a same traffic lane is 100 meters or more.

12. The method as claimed in claim 9 further comprising detecting a slow traffic state when a multiple information fusion model identifies other vehicles travelling at less than half of a posted speed limit provided by a background map database.

13. The method as claimed in claim 9 further comprising detecting a congested traffic state when a multiple information fusion model identifies a vehicle travelling in an adjacent traffic lane coming to a near or complete stop.

14. The method as claimed in claim 9 further comprising generating a traffic state update by compiling the traffic flow state over a fixed period of time including counting an occurrence of a congested traffic state, a slow traffic state and a free traffic state.

15. A non-transitory computer readable medium including instructions for a navigation system comprising: generating a video clip by parsing an interval of a sensor data stream for a region of travel; analyzing the video clip submitted to a deep learning model, already trained, including identifying a traffic flow estimate; accessing a position coordinate for calculating a distance to intersection; generating a traffic flow state by fusing a corrected speed, the traffic flow estimate, and the distance to intersection; merging a vehicle maneuvering instruction into the traffic flow state for maneuvering through the region of travel; and communicating the traffic flow state for displaying on a device.

16. The non-transitory computer readable medium including the instructions as claimed in claim 15 further compromising: generating the corrected speed by submitting a speed and a gyroscope angular rate to a filter/correction unit; and detecting a turning state when the speed is decreasing and the gyroscope angular rate exceeds 5 degree per second.

17. The non-transitory computer readable medium including the instructions as claimed in claim 15 further comprising loading the traffic flow estimate in a multiple information fusion model for detecting a free traffic state when a distance to a vehicle in a same traffic lane is 100 meters or more.

18. The non-transitory computer readable medium including the instructions as claimed in claim 15 further comprising detecting a slow traffic state when a multiple information fusion model identifies the vehicle travelling at less than half of a posted speed limit provided by a background map database.

19. The non-transitory computer readable medium including the instructions as claimed in claim 15 further comprising detecting a congested traffic state when a multiple information fusion model identifies a vehicle travelling in an adjacent traffic lane coming to a near or complete stop.

20. The non-transitory computer readable medium including the instructions as claimed in claim 15 further comprising generating a traffic state update by compiling the traffic flow state over a fixed period of time including counting an occurrence of a congested traffic state, a slow traffic state and a free traffic state.