Methods and systems for spatio-temporal regular expression matching
Systems and methods are provided that implement spatio-temporal query of perception data streams. A system is distinctly designed to implement efficient and flexible spatio-temporal queries, which conduct pattern matching over perception data streams for automotive applications, particularly autonomous vehicles. The system is also designed to enable the spatio-temporal queries to be expressed in SpREs (Spatial Regular Expressions), which is implemented as a querying language that combines the ease of REs (regular expressions) with the enhanced capabilities of spatial logic. Additionally, a method includes receiving a command associated with a spatio-temporal query, wherein the expression comprises spatial regular expressions (SpREs). Thereafter, performing the spatio-temporal query of a plurality of perception data streams using the SpREs, where the SpREs describe spatio-temporal patterns to be searched within the plurality of perception data streams.
1. A method, comprising:
receiving a command associated with a spatio-temporal query, wherein the spatio-temporal query comprises spatial regular expressions (SpREs) comprising a pattern-based semantic component and a spatial component; and
performing the spatio-temporal query of a plurality of perception data streams using the SpREs, wherein the spatial component of the SpREs describe spatio-temporal patterns between objects to be searched within the plurality of perception data streams.
2. The method of claim 1 , further comprising converting the SpREs into an equivalent Deterministic Finite Automata (DFA).
3. The method of claim 2 , further comprising generating a corresponding symbolic map to map topological events to symbols in a regular expression (RE).
4. The method of claim 1 , further comprising determining whether there is a match of the spatio-temporal patterns within the plurality of perception data streams; and
returning a set of matches comprising each determined match as a result of the spatio-temporal query.
5. The method of claim 1 , further comprising receiving the plurality of perception data streams from one or more vehicles comprising sensors.
6. The method of claim 5 , wherein the spatio-temporal query comprises searching an offline database storing the plurality of perception data streams.
7. The method of claim 6 , wherein the spatio-temporal query returns a set of frames matching the spatio-temporal query or a set of objects in each frame matching the spatio-temporal query.
8. The method of claim 5 , wherein the plurality of perception data streams comprises at least one of video data stream, lidar data stream, sonar data stream, and radar data stream.
9. The method of claim 5 , wherein the one or more vehicles comprise: an autonomous vehicle, a semi-autonomous vehicle, an automated driving system, or a non-autonomous vehicle.
10. The method of claim 1 , wherein the spatio-temporal query comprises searching the plurality of perception data streams from an online streaming source.
11. The method of claim 10 , wherein the online streaming source comprises one or more of: an autonomous vehicle, a robotic device, an unmanned ariel vehicle, and an advanced driver assistance system (ADAS).
12. The method of claim 1 , further comprising performing an action associated with an automotive application based on the spatio-temporal query.
13. The method of claim 12 , wherein the automotive application comprises one or more of: executing control of an autonomous vehicle, determining scenarios of interest from searching an offline database storing the plurality of perception data streams from vehicles, executing testing of autonomous vehicle software in simulation or in a real-world deployment, and executing diagnostics of autonomous vehicle perception systems.
14. A vehicle comprising:
sensors capturing a plurality of perception data, wherein the plurality of perception data is associated with a driving environment of the vehicle; and
a controller performing spatio-temporal queries of the plurality of perception data to determine a presence of a spatio-temporal pattern between objects within the driving environment of the vehicle, wherein the spatio-temporal queries comprise a pattern-based semantic component and a spatial component and executes spatio-temporal searches within the plurality of perception data and returns instances of perception data matching the spatio-temporal pattern between the objects, and
executing an autonomous control of the vehicle based on the spatio-temporal query of the searched pattern, wherein the autonomous control is associated with the driving environment.
15. The vehicle of claim 14 , wherein the spatio-temporal queries comprise spatial regular expressions (SpREs).
16. The vehicle of claim 15 , wherein the controller receives additional perception data from a database storing the additional perception data.
17. The vehicle of claim 16 , wherein the additional perception data is collected by a plurality of vehicles having sensor capabilities.
18. The vehicle of claim 16 , wherein the controller performs spatio-temporal queries of the additional perception data to detect scenarios to test autonomous vehicle functions of the vehicle.
19. The vehicle of claim 14 , wherein the vehicle comprises an autonomous vehicle.
20. A computer system, comprising:
one or more processors; and
a memory having instructions stored thereon, which when executed by the one or more processors cause the processors to perform:
receiving a command associated with a spatio-temporal query, wherein the spatio-temporal query comprises spatial regular expressions (SpREs), wherein the SpREs comprise a pattern-based semantic component and a spatial component; and
performing the spatio-temporal query of a plurality of perception data streams using the SpREs, wherein the SpREs describe spatio-temporal patterns between objects to be searched within the plurality of perception data streams.