Systems and Methods for Handling Traffic Signs

1 . A method, comprising:

receiving sensor data from a sensor of a vehicle;

identifying, using one or more computer devices, a traffic sign within a field of view of the vehicle based on the sensor data;

assigning, using the one or more computer devices, a probable class to the identified sign based on a determination that the identified sign is not mapped in a priori map and a determination that a probability of existence of the sign is greater than a threshold; and

modifying, by the one or more computer devices, a behavior of the vehicle based on the assigned probable class.

2 . The method of claim 1 , wherein assigning the probable class further comprises:

identifying a probability of a type of the identified sign, wherein the type is at least one of a stop sign or a yield sign; and

identifying a position and orientation of the identified sign.

3 . The method of claim 1 , further comprising:

comparing data associated with the identified sign with a record of false positive detections; and

disregarding the identified sign based on a determination that the identified sign is a false positive.

4 . The method of claim 1 , wherein modifying the behavior of the vehicle comprises:

performing a map update, wherein the map update comprises a type of the identified sign, one or more lane segments associated with the identified sign, a stop line associated with the identified sign, and one or more legal requirements for the one or more lane segments associated with the identified sign.

5 . The method of claim 4 , further comprising:

identifying a proximate lane segment within a position and orientation threshold of the identified sign;

identifying a neighbor segment of the proximate lane segment and a successor of the neighbor segment, wherein the neighbor segment has a same travel direction as the proximate lane segment; and

associating the identified sign with the neighbor segment of the proximate lane and with the successor when the successor satisfies at least a distance criterion.

6 . The method of claim 4 , further comprising:

propagating the map update to one or more vehicles.

7 . The method of claim 1 , wherein modifying the behavior of the vehicle comprises:

transmitting to a remote guidance operator information associated with the identified sign;

receiving from the remote guidance operator a first indication whether the identified sign is valid; and

receiving from the remote guidance operator a second indication to proceed after the vehicle autonomously determines it is appropriate to proceed.

8 . The method of claim 7 , further comprising:

performing a timed stop when a type of the identified sign is a stop sign; and

proceeding, after receiving the second indication to proceed, in response to determining that the path of the vehicle is clear of other road users.

9 . The method of claim 1 , wherein modifying the behavior of the vehicle comprises:

predicting an action of an actor that is proximate to the vehicle based on the identified sign; and

adjusting a vehicle trajectory for the vehicle based on the predicted action and the identified sign.

10 . The method of claim 1 , further comprising:

logging an annotation for the identified sign when the identified sign is an unmapped sign; and

updating the a priori map based on a determination that the unmapped sign is a permanent traffic sign.

11 . The method of claim 1 , further comprising:

determining whether the traffic sign is mapped by comparing the identified sign to signs in the a priori map, wherein the identified sign is mapped when a type of the identified sign matches a type of the mapped sign in the a priori map and is within a position and orientation tolerance of the mapped sign.

12 . The method of claim 1 , further comprising:

updating a record of false positive detections in response to determining that the identified sign is a false positive detection.

13 . A system, comprising:

a memory; and

at least one processor coupled to the memory and configured to:

receive sensor data from a sensor of a vehicle;

identify a traffic sign within a field of view of the vehicle based on the sensor data;

assign a probable class to the identified sign based on a determination that the identified sign is not mapped in a priori map and a determination that a probability of existence of the sign is greater than a threshold; and

modify a behavior of the vehicle based on the assigned probable class.

14 . The system of claim 13 , wherein the at least one processor is further configured to:

identify a probability of a type of the identified sign, wherein the type is at least one of a stop sign or a yield sign; and

identify a position and orientation of the identified sign.

15 . The system of claim 13 , wherein the at least one processor is further configured to:

compare data associated with the identified sign with a record of false positive detections; and

disregard the identified sign based on a determination that the identified sign is a false positive.

16 . The system of claim 13 , wherein the at least one processor is further configured to:

perform a map update, wherein the map update comprises a type of the identified sign, one or more lane segments associated with the identified sign, a stop line associated with the identified sign, and one or more legal requirements for the one or more lane segments associated with the identified sign.

17 . The system of claim 16 , wherein the at least one processor is further configured to:

identify a proximate lane segment within a position and orientation threshold of the identified sign;

identify a neighbor segment of the proximate lane segment and a successor of the neighbor segment, wherein the neighbor segment has a same travel direction as the proximate lane segment; and

associate the identified sign with the neighbor segment of the proximate lane and with the successor when the successor satisfies at least a distance criterion.

18 . A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:

receiving sensor data from a sensor of a vehicle;

detecting a traffic sign within a field of view of the vehicle based on the sensor data;

assigning a probable class to the identified sign based on a determination that the identified sign is not mapped in a priori map and a determination that a probability of existence of the sign is greater than a threshold; and

modifying a behavior of the vehicle based on the assigned probable class.

19 . The non-transitory computer-readable medium of claim 18 , the operations further comprising:

performing a map update, wherein the map update comprises a type of the identified sign, one or more lane segments associated with the identified sign, a stop line associated with the identified sign, and one or more legal requirements for the one or more lane segments associated with the identified sign.

20 . The non-transitory computer-readable medium of claim 19 , the operations further comprising:

identifying a proximate lane segment within a position and orientation threshold of the identified sign;

identifying a neighbor segment of the proximate lane segment and a successor of the neighbor segment, wherein the neighbor segment has a same travel direction as the proximate lane segment; and

associating the identified sign with the neighbor segment of the proximate lane and with the successor when the successor satisfies at least a distance criterion.