# US 11,897,511 B2 · Multi-hypothesis object tracking for automated driving systems

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- Number: US 11,897,511 B2
- Document ID: US11897511B2
- Status: Granted
- Kind: B2
- Application number: 16/888,227
- Filed: 2020-05-29
- Published: 2020-12-03
- Granted: 2024-02-13
- Earliest claimed: 2019-05-30
- Assignees: Robert Bosch GmbH
- Applicants: Robert Bosch GmbH
- Inventors: Theresa Veronika Kienle; Thomas Benjamin Gussner; Oliver F. Schwindt; Stephan Reuter; Andreas Heyl
- Web page: https://patentgrep.com/patent/US11897511B2

## CPC

- B60W 60/001: {Planning or execution of driving tasks}
- B60W 60/0025: {specially adapted for specific operations}
- B60W 60/0027: {using trajectory prediction for other traffic participants}
- G01S 7/4808: {Evaluating distance, position or velocity data}
- G01S 13/723: {by using numerical data}
- G01S 13/865: {Combination of radar systems with lidar systems}
- G01S 13/87: Combinations of radar systems, e.g. primary radar and secondary radar
- G01S 13/931: of land vehicles
- G01S 17/931: of land vehicles
- B60W 2552/50: Barriers
- B60W 2552/53: Road markings, e.g. lane marker or crosswalk
- B60W 2554/00: Input parameters relating to objects
- B60W 2554/4029: Pedestrians
- G01S 13/862: {Combination of radar systems with sonar systems}
- G01S 13/867: {Combination of radar systems with cameras}
- G01S 15/931: of land vehicles
- G01S 2013/9323: {Alternative operation using light waves}
- G01S 2013/9324: {Alternative operation using ultrasonic waves}
- G01S 2013/93272: {in the back of the vehicles}
- G01S 2013/93274: {on the side of the vehicles}
- B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES

## Abstract

Methods and systems for multi-hypothesis object tracking for automated driving systems. One system includes an electronic processor configured to receive environment information and generate pseudo-measurement data associated with an object within an environment of the vehicle. The electronic processor is also configured to determine, based on the environment information and the pseudo-measurement data, a set of association hypotheses regarding the object. The electronic processor is also configured to determine, based on the set of association hypotheses, an object state of the object. The electronic processor is also configured to control the vehicle based on the determined object state.

## Claims

### Claim 1

An automated driving system for a vehicle, the system comprising:
an electronic processor configured to
receive environment information,
generate pseudo-measurement data associated with an object within an environment of the vehicle including generating a plurality of measurement model hypotheses for each measurement included in the environment information as the pseudo- measurement data, wherein each of the plurality of measurement model hypotheses is based on a different assumption and includes a first measurement model hypothesis based on a first assumption that the object is of a first object type and a second measurement model hypothesis based on a second assumption that the object is of a second object type,
determine, based on the environment information and the pseudo-measurement data, a set of association hypotheses regarding the object,
determine, based on the set of association hypotheses, an object state of the object, and
control the vehicle based on the determined object state.

### Claim 2 (depends on 1)

The system of claim 1, wherein at least one of the plurality of measurement model hypotheses is a generic measurement model hypothesis.

### Claim 3 (depends on 1)

The system of claim 1, wherein the electronic processor is configured to update the set of association hypotheses in response to receiving new environment information.

### Claim 4 (depends on 1)

The system of claim 1, wherein each association hypothesis included in the set of association hypotheses is associated with a possible object state of the object.

### Claim 5 (depends on 1)

The system of claim 1, wherein the electronic processor is configured to receive the environment information from at least two sensors, the environment information associated with a common field of view of the at least two sensors.

### Claim 6 (depends on 5)

The system of claim 5, wherein the pseudo-measurement data includes negative information associated with the common field of view.

### Claim 7 (depends on 1)

The system of claim 1, wherein the electronic processor is configured to determine the object state of the object to be one of the association hypotheses included in the set of association hypotheses.

### Claim 8 (depends on 7)

The system of claim 7, wherein the determination of the object state to be the one of the association hypotheses is based on the one of the association hypotheses having a higher probability than other association hypotheses included in the set of association hypotheses.

### Claim 9

A method for multi-hypothesis object tracking, the method comprising:
receiving environment information;
generating, with an electronic processor, pseudo-measurement data by generating a first measurement model hypothesis based on a first assumption that the object is of a first object type and generating a second measurement model based on a second assumption that the object is of a second object type different from the first object type;
generating, based on the environment information and the pseudo-measurement data, a set of association hypotheses for an object within an environment surrounding a vehicle;
determining, with the electronic processor, an object state for the object based on the set of association hypotheses; and
controlling the vehicle based on the determined object state.

### Claim 10 (depends on 9)

The method of claim 9, further comprising:
receiving new environment information; and
updating the set of association hypotheses based on the new environment information.

### Claim 11 (depends on 10)

The method of claim 10, wherein updating the set of association hypotheses includes removing at least one association hypothesis from the set of association hypotheses.

### Claim 12 (depends on 9)

The method of claim 9, wherein receiving the environment information includes receiving environment information from at least two sensors, wherein the environment information is associated with a common field of view of the at least two sensors.

### Claim 13 (depends on 12)

The method of claim 12, wherein generating the pseudo-measurement data includes generating negative information associated with the common field of view.

### Claim 14 (depends on 9)

The method of claim 9, wherein generating the pseudo-measurement data includes generating a measurement model hypothesis.

### Claim 15 (depends on 9)

The method of claim 9, wherein determining the object state includes determining the object state to be one of the association hypotheses included in the set of association hypotheses.

### Claim 16 (depends on 15)

The method of claim 15, wherein determining the object state to be the one of the association hypotheses includes determining the one of the association hypotheses that has a higher probability than other association hypotheses included in the set of association hypotheses.

### Claim 17

A non-transitory, computer-readable medium storing instructions that, when executed by an electronic processor, perform a set of functions, the set of functions comprising:
receiving, from at least one sensor, environment information of a field of view, the field of view including an object;
generating pseudo-measurement data by generating a first measurement model hypothesis based on a first assumption that the object is of a first object type and generating a second measurement model based on a second assumption that the object is of a second object type different from the first object type;
generating, based on the environment information and the pseudo-measurement data, a set of association hypotheses for the object within the field of view;
determining an object state for the object based on the set of association hypotheses;
determining a vehicle maneuver based on the determined object state, and
controlling a vehicle based on the vehicle maneuver.