Estimating a movement of a hybrid-behavior vehicle

1. A method for predicting a movement of a hybrid-behavior vehicle, the method comprises:

receiving, by a computerized system, video streams of driving scenes;

analyzing, by the computerized system, the video streams; wherein the analyzing comprises:

recognizing vehicles that appear in the video streams; wherein the vehicles comprise the hybrid-behavior vehicle and a non-hybrid-behavior vehicle; wherein the non-hybrid-behavior vehicle differs from the hybrid-behavior vehicle, wherein the hybrid-behavior vehicle is selected out of a group consisting of an unmotorized bicycle, a motorized bicycle, a motorized skateboard, a unicycle, a hoverboards and a kick scooter;

detecting the non-hybrid-behavior vehicle and the hybrid-behavior vehicle;

creating an environment-based behavioral model of the hybrid-behavior vehicle;

preventing from building behavioral models of the non-hybrid-behavior vehicle; and

responding, by the computerized system, to the creation of the environment-based behavioral model.

2. The method according to claim 1 comprising generating, by the computerized system, the environment-based behavioral model of the hybrid-behavior vehicle even without prior knowledge about the hybrid-behavior vehicle.

3. The method according to claim 1 wherein the vehicles that appear in the video stream comprise different classes of hybrid-behavior vehicles; wherein the method comprises creating different environment-based behavioral models for the different classes.

4. The method according to claim 3 wherein the analyzing comprises classifying the hybrid-behavior vehicles to the different classes based on visual attributes of the hybrid-behavior vehicles.

5. The method according to claim 3 wherein the analyzing comprises classifying the hybrid-behavior vehicles to the different classes based on movement parameters of the hybrid-behavior vehicles.

6. The method according to claim 3 wherein the analyzing comprises classifying the hybrid-behavior vehicles to the different classes based on risk levels associated with movements of the hybrid-behavior vehicles.

7. The method according to claim 3 wherein the analyzing comprises classifying the hybrid-behavior vehicles to the different classes based on at least two out of (a) visual attributes of the hybrid-behavior vehicles, (b) movement parameters of the hybrid-behavior vehicles, and (c) risk levels associated with movements of the hybrid-behavior vehicles.

8. A non-transitory computer readable medium for predicting a movement of a hybrid behavior vehicle, the non-transitory computer readable medium stores instructions for:

receiving, by a computerized system, video streams of driving scenes;

analyzing, by the computerized system, the video streams; wherein the analyzing comprises:

recognizing vehicles that appear in the video streams; wherein the vehicles comprise the hybrid-behavior vehicle and a non-hybrid-behavior vehicle; wherein the non-hybrid-behavior vehicle differs from the hybrid-behavior vehicle, wherein the hybrid-behavior vehicle is selected out of a group consisting of an unmotorized bicycle, a motorized bicycle, a motorized skateboard, a unicycle, a hoverboards and a kick scooter;

detecting the non-hybrid-behavior vehicle and the hybrid-behavior vehicle;

creating an environment-based behavioral model of the hybrid-behavior vehicle;

preventing from building behavioral models of the non-hybrid-behavior vehicle; and

responding, by the computerized system, to the creation of the environment-based behavioral model.

9. The non-transitory computer readable medium according to claim 8 that stores instructions for generating the environment-based behavioral model of the hybrid behavior vehicle even without prior knowledge about the hybrid behavior vehicle.

10. The non-transitory computer readable medium according to claim 8 wherein the vehicles that appear in the video stream comprise different classes of hybrid-behavior vehicles; wherein the non-transitory computer readable medium stores instructions for creating different environment-based behavioral models for the different classes.

11. The non-transitory computer readable medium according to claim 10 wherein the analyzing comprises classifying the hybrid-behavior vehicles to the different classes based on visual attributes of the hybrid-behavior vehicles.

12. The non-transitory computer readable medium according to claim 10 wherein the analyzing comprises classifying the hybrid-behavior vehicles to the different classes based on movement parameters of the hybrid-behavior vehicles.

13. The non-transitory computer readable medium according to claim 10 wherein the analyzing comprises classifying the hybrid-behavior vehicles to the different classes based on risk levels associated with movements of the hybrid-behavior vehicles.

14. The non-transitory computer readable medium according to claim 10 wherein the analyzing comprises classifying the hybrid-behavior vehicles to the different classes based on at least two out of (a) visual attributes of the hybrid-behavior vehicles, (b) movement parameters of the hybrid-behavior vehicles, and (c) risk levels associated with movements of the hybrid-behavior vehicles.

15. A method for driving an autonomous vehicle, the method comprises:

sensing an environment of the autonomous vehicle by one or more autonomous vehicle sensor;

detecting, in the environment of the autonomous vehicle, environmental elements and a hybrid-behavior vehicle;

predicting one or more predicted behaviors of the hybrid behavior vehicle based on the environmental elements and an environment-based behavioral model of the hybrid-behavior vehicle; wherein the environment-based behavioral model of the hybrid-behavior vehicle was generated by a computerized system configured to reduce consumption of memory and computational resources by preventing from building behavioral models of non-hybrid-behavior vehicles; wherein each one of the non-hybrid-behavior vehicles differs from the hybrid-behavior vehicle, wherein the hybrid-behavior vehicle is selected out of a group consisting of an unmotorized bicycle, a motorized bicycle, a motorized skateboard, a unicycle, a hoverboards and a kick scooter; and

driving the autonomous vehicle based, at least in part, on the one or more predicted behaviors of the hybrid behavior vehicle.

16. The method according to claim 15 comprising predicting probabilities related to the occurrence of the one or more predicted behaviors of the hybrid behavior vehicle.

17. The method according to claim 15 wherein the driving comprises slowing down or stopping.

18. The method according to claim 15 wherein the predicting is responsive to a current behavior pattern of the hybrid-behavior vehicle.

19. A method for assisting a driver of a vehicle, the method comprises:

sensing an environment of the vehicle by one or more vehicle sensor;

detecting, in the environment of the vehicle, environmental elements and a hybrid-behavior vehicle;

estimating an estimated future movement of the hybrid-behavior vehicle based on the environmental elements and an environment-based behavioral model of the hybrid-behavior vehicle; wherein the environment-based behavioral model of the hybrid-behavior vehicle was generated by a computerized system configured to reduce consumption of memory and computational resources by preventing from building behavioral models of non-hybrid-behavior vehicles; wherein each one of the non-hybrid-behavior vehicles differs from the hybrid-behavior vehicle, wherein the hybrid-behavior vehicle is selected out of a group consisting of an unmotorized bicycle, a motorized bicycle, a motorized skateboard, a unicycle, a hoverboards and a kick scooter; and

performing at least one out of:

providing to the driver an indication about the one or more predicted behaviors of the hybrid behavior vehicle to the driver; and

calculating a suggested propagation path of the vehicle, based on the one or more predicted behaviors of the hybrid behavior vehicle, and providing to the driver an indication about the suggested propagation path of the vehicle.

20. The method according to claim 19 comprising predicting probabilities related to the occurrence of the one or more predicted behaviors of the hybrid behavior vehicle.

21. A non-transitory computer readable medium for driving an autonomous vehicle, the non-transitory computer readable medium that stores instructions for:

sensing an environment of the autonomous vehicle by one or more autonomous vehicle sensor;

detecting, in the environment of the autonomous vehicle, environmental elements and a hybrid-behavior vehicle; wherein each one of the non-hybrid-behavior vehicles differs from the hybrid-behavior vehicle, wherein the hybrid-behavior vehicle is selected out of a group consisting of an unmotorized bicycle, a motorized bicycle, a motorized skateboard, a unicycle, a hoverboards and a kick scooter;

predicting one or more predicted behaviors of the hybrid behavior vehicle based on the environmental elements and an environment-based behavioral model of the hybrid-behavior vehicle; wherein the environment-based behavioral model of the hybrid-behavior vehicle was generated by a computerized system configured to reduce consumption of memory and computational resources by preventing from building behavioral models of non-hybrid-behavior vehicles; and

driving the autonomous vehicle based, at least in part, on the one or more predicted behaviors of the hybrid behavior vehicle.

22. The non-transitory computer readable medium according to claim 21 that stores instructions for predicting probabilities related to the occurrence of the one or more predicted behaviors of the hybrid behavior vehicle.

23. The non-transitory computer readable medium according to claim 21 wherein the driving comprises slowing down or stopping.

24. The non-transitory computer readable medium according to claim 21 , wherein the predicting is responsive to a current behavior pattern of the hybrid-behavior vehicle.

25. A non-transitory computer readable medium for assisting a driver of a vehicle, the non-transitory computer readable medium that stores instructions for:

sensing an environment of the vehicle by one or more vehicle sensor;

detecting, in the environment of the vehicle, environmental elements and a hybrid-behavior vehicle;

estimating an estimated future movement of the hybrid-behavior vehicle based on the environmental elements and an environment-based behavioral model of the hybrid-behavior vehicle; wherein the environment-based behavioral model of the hybrid-behavior vehicle was generated by a computerized system configured to reduce consumption of memory and computational resources by preventing from building behavioral models of non-hybrid-behavior vehicles; wherein each one of the non-hybrid-behavior vehicles differs from the hybrid-behavior vehicle, wherein the hybrid-behavior vehicle is selected out of a group consisting of an unmotorized bicycle, a motorized bicycle, a motorized skateboard, a unicycle, a hoverboards and a kick scooter; and

performing at least one out of:

providing to the driver an indication about the one or more predicted behaviors of the hybrid behavior vehicle to the driver; and

calculating a suggested propagation path of the vehicle, based on the one or more predicted behaviors of the hybrid behavior vehicle, and

providing to the driver an indication about the suggested propagation path of the vehicle.

26. The non-transitory computer readable medium according to claim 25 that stores instructions for predicting probabilities related to the occurrence of the one or more predicted behaviors of the hybrid behavior vehicle.