Claims:We claim:
1. A method of providing recommendations during vehicle navigation, the method comprising:
receiving, by a recommendation engine, data related to a vehicle and environment surrounding the vehicle, from a plurality of sensors while the vehicle is navigating a road;
generating, by the recommendation engine, a training set for a plurality of conditions of the road based on the plurality of data related to the environment, vehicle and a historical data set;
creating, by the recommendation engine, a multi-variate regression model using the training set for detecting a condition of the road from the plurality of conditions of road; and
providing, by the recommendation engine, one or more recommendations to one or more occupants of the vehicle based on the detected condition of the road, wherein the recommendations provided are used for navigating the road.

2. The method as claimed in claim 1, wherein the one or more recommendations comprises recommending an optimal route to navigate from a source to a destination, wherein the optimal route is updated based on a change in condition of the road during navigation, wherein the updated route and the detected condition of the road is communicated to at least one of, an occupant in the vehicle and one or more vehicles approaching the vehicle.

3. The method as claimed in claim 1, further comprising providing warnings about the condition of the road to a user through one of audio, visual or text modes.

4. The method as claimed in claim 1, wherein the data related to environment comprises notifications indicative of the condition of the road, sent by other vehicles.

5. The method as claimed in claim 1, wherein the plurality of sensors comprises vehicle body sensors and environmental monitoring sensors.

6. The method as claimed in claim 1, wherein the data related to the vehicle and the environment is calibrated before generating the training set.

7. The method as claimed in claim 1, wherein the data related to the vehicle is normalised based on predetermined normalization calibration charts depending on one of a type, a make, a model and a brand of the vehicle.

8. The method as claimed in claim 5, wherein the environment monitoring sensors comprise at least one of Long-Range Radar, Short-Range Radar, Laser, Infrared Sensor, Ultrasonic and image sensors.

9. The method as claimed in claim 8, wherein the vehicle body sensors comprise at least one of accelerometer, vehicle speed sensor, vehicle brake sensor, vehicle collision sensor, vehicle vibration sensor, and Global Positioning System (GPS).

10. A recommendation engine for providing recommendations during vehicle navigation, the system comprising:
a processor; and
a memory communicatively coupled to the processor, wherein the memory stores processor instructions, which, on execution, causes the processor to:
receive data related to the vehicle and environment surrounding the vehicle from a plurality of sensors in real-time;
generate a training set for a plurality of conditions of the road based on the plurality of data related to the environment, vehicle and a historical data set;
create a multi-variate regression model using the training set for detecting a condition from the plurality of conditions of the road; and
provide one or more recommendations to one or more occupants of the vehicle based on the detected condition of the road, wherein the recommendations provided are used for navigating the road.

11. The recommendation engine as claimed in claim 10, wherein the processor provides recommends an optimal route for the vehicle based on the condition of the road to navigate from a source to a destination, wherein the generated route is updated based on change in condition of the road during navigation, wherein the updated route and the detected condition of the road is communicated to at least one of, an occupant in the vehicle and one or more vehicles approaching the vehicle.

12. The recommendation engine as claimed in claim 10, wherein the plurality of sensors comprises vehicle body sensors and environmental monitoring sensors.

13. The recommendation engine as claimed in claim 10, wherein the processor is configured to calibrate the data related to the vehicle and the environment before generating the training set.

14. The recommendation engine as claimed in claim 10, wherein the processor is configured to normalise the data related to the vehicle based on predetermined normalization calibration charts depending on one of a type, a make, a model and a brand of the vehicle.

15. The recommendation engine as claimed in claim 11, wherein the processor is configured to provide warnings about the condition of the road to the occupant through one of audio, visual or text modes.

16. The recommendation engine as claimed in claim 12, wherein the environment monitoring sensors comprise at least one of Long-Range Radar, Short-Range Radar, Laser, Infrared Sensor, Ultrasonic and image sensors.

17. The recommendation engine as claimed in claim 12, wherein the vehicle body sensors comprise at least one of accelerometer, vehicle speed sensor, vehicle brake sensor, vehicle collision sensor, vehicle vibration sensor, and Global Positioning System (GPS).
Dated this 30th day of December, 2018

R Ramya Rao
Of K&S Partners
Agent for the Applicant
IN/PA-1607
, Description:TECHNICAL FIELD
[0001] The present disclosure relates in general to driver assistance. Particularly, but not exclusively, the present disclosure relates to providing recommendations to a driver during vehicle navigation.