Methods and systems for tracking a mover's lane over time
Systems and methods for monitoring the lane of an object in an environment of an autonomous vehicle are disclosed. The methods include receiving sensor data corresponding to the object, and assigning an instantaneous probability to each of a plurality of lanes based on the sensor data as a measure of likelihood that the object is in that lane at a current time. The methods also include generating a transition matrix for each of the plurality of lanes that encode one or more probabilities that the object transitioned to that lane from another lane in the environment or from that lane to another lane in the environment at the current time. The methods then include determining an assigned probability associated with each of the plurality of lanes based on the instantaneous probability and the transition matrix as a measure of likelihood of the object occupying that lane at the current time.
1. A method of monitoring a lane in which an object is moving in an environment of an autonomous vehicle, the method comprising:
receiving real-time sensor data corresponding to the object;
assigning, based on the sensor data, an instantaneous probability to each of a plurality of lanes in the environment of the autonomous vehicle, the instantaneous probability being a measure of likelihood that the object is in that lane at a current time t;
generating a transition matrix for each of the plurality of lanes, the transition matrix encoding one or more probabilities that the object transitioned to that lane from another lane in the environment or from that lane to another lane in the environment at the current time t; and
determining an assigned probability associated with each of the plurality of lanes based on the instantaneous probability and the transition matrix, the assigned probability being a measure of likelihood of the object occupying that lane at the current time t.
2. The method of claim 1 , further comprising identifying the lane in which the object is moving as a lane that has the highest assigned probability.
3. The method of claim 1 , further comprising using the assigned probability associated with each lane to control navigation of the autonomous vehicle in the environment.
4. The method of claim 1 , wherein generating the transition matrix for each of the plurality of lanes comprises:
receiving a Hidden Markov Model (HMM) for each of the plurality of lanes; and
using the sensor data and the HMM to generate an initial transition matrix encoding one or more probabilities that the object transitioned to or from that lane from or to another lane in the environment at the current time t.
5. The method of claim 4 , further comprising using relationships between the plurality of lanes for updating the initial transition matrix and generating the transition matrix for each of the plurality of lanes.
6. The method of claim 5 , wherein the relationships between the plurality of lanes include information relating to valid paths for transitioning between each of the plurality of lanes.
7. The method of claim 5 , further comprising determining the relationships between the plurality of lanes using a road network map.
8. The method of claim 4 , further comprising identifying one or more parameters for generating the HMM for each of the plurality of lanes using training data, the training data comprising observed states of a plurality of objects associated with known information relating to lanes occupied by the plurality of objects.
9. The method of claim 1 , wherein assigning the instantaneous probability to each of the plurality of lanes in the environment of the autonomous vehicle comprises determining, using the sensor data, at least one of the following: percentage of overlap of the object with that lane; alignment of the object with that lane; object classification; direction of travel of the object; speed of the object; acceleration of the object; or pose of the object.
10. The method of claim 1 , wherein assigning the instantaneous probability to each of the plurality of lanes in the environment of the autonomous vehicle comprises using a random forest classifier for assigning the instantaneous probabilities.
11. The method of claim 1 , further comprising, determining the assigned probability associated with each of the plurality of lanes based on a previously assigned probability that is a measure of likelihood of the object occupying that lane at a previous time step.
12. The method of claim 11 , further comprising determining the assigned probability associated with each of the plurality of lanes by multiplying the instantaneous probability associated with that lane, the previously assigned probability associated with that lane, and a probability of the object transitioning from any of the plurality of lanes into that lane as determined from the transition matrix.
13. A system for monitoring a lane in which an object is moving in an environment of an autonomous vehicle, the system comprising:
an autonomous vehicle comprising one or more sensors;
a processor; and
a non-transitory computer readable medium comprising one or more instructions that when executed by the processor, cause the processor to:
receive real-time sensor data corresponding to the object from the one or more sensors,
assign, based on the sensor data, an instantaneous probability to each of a plurality of lanes in the environment of the autonomous vehicle, the instantaneous probability being a measure of likelihood that the object is in that lane at a current time t,
generate a transition matrix for each of the plurality of lanes, the transition matrix encoding one or more probabilities that the object transitioned to that lane from another lane in the environment or from that lane to another lane in the environment at the current time t, and
determine an assigned probability associated with each of the plurality of lanes based on the instantaneous probability and the transition matrix, the assigned probability being a measure of likelihood of the object occupying that lane at the current time t.
14. The system of claim 13 , further comprising programming instructions that when executed by the processor, cause the processor to identify the lane in which the object is moving as a lane that has the highest assigned probability.
15. The system of claim 13 , further comprising programming instructions that when executed by the processor, cause the processor to use the assigned probability associated with each lane to control navigation of the autonomous vehicle in the environment.
16. The system of claim 13 , wherein the one or more programming instructions that when executed by the processor, cause the processor to generate the transition matrix for each of the plurality of lanes comprise programming instructions to cause the processor to:
receiving a Hidden Markov Model (HMM) for each of the plurality of lanes; and
using the sensor data and the HMM to generate an initial transition matrix encoding one or more probabilities that the object transitioned to or from that lane from or to another lane in the environment at the current time t.
17. The system of claim 16 , further comprising programming instructions that when executed by the processor, cause the processor to use relationships between the plurality of lanes for updating the initial transition matrix and generating the transition matrix for each of the plurality of lanes.
18. The system of claim 17 , wherein the relationships between the plurality of lanes include information relating to valid paths for transitioning between each of the plurality of lanes.
19. The system of claim 17 , further comprising programming instructions that when executed by the processor, cause the processor to determine the relationships between the plurality of lanes using a road network map.
20. The system of claim 16 , further comprising programming instructions that when executed by the processor, cause the processor to identify one or more parameters for generating the HMM for each of the plurality of lanes using training data, the training data comprising observed states of a plurality of objects associated with known information relating to lanes occupied by the plurality of objects.
21. The system of claim 13 , wherein the one or more programming instructions that when executed by the processor, cause the processor to assign the instantaneous probability to each of the plurality of lanes in the environment of the autonomous vehicle comprise programming instructions to cause the processor to determine, using the sensor data, at least one of the following: percentage of overlap of the object with that lane; alignment of the object with that lane; object classification; direction of travel of the object; speed of the object; acceleration of the object; or pose of the object.
22. The system of claim 13 , wherein the one or more programming instructions that when executed by the processor, cause the processor to assign the instantaneous probability to each of the plurality of lanes in the environment of the autonomous vehicle comprise programming instructions to cause the processor to use a random forest classifier for assigning the instantaneous probabilities.
23. The system of claim 13 , further comprising programming instructions that when executed by the processor, cause the processor to determine the assigned probability associated with each of the plurality of lanes based on a previously assigned probability that is a measure of likelihood of the object occupying that lane at a previous time step.
24. The system of claim 23 , further comprising programming instructions that when executed by the processor, cause the processor to determine the assigned probability associated with each of the plurality of lanes by multiplying the instantaneous probability associated with that lane, the previously assigned probability associated with that lane, and a probability of the object transitioning from any of the plurality of lanes into that lane as determined from the transition matrix.