# US 2023/0056475 A1 · System and method for a scenario-based event trigger

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- Number: US 2023/0056475 A1
- Document ID: US20230056475A1
- Status: Published application
- Kind: A1
- Application number: 17/408,274
- Filed: 2021-08-20
- Published: 2023-02-23
- Earliest claimed: 2021-08-20
- Assignees: Toyota Research Institute, Inc.
- Applicants: Toyota Research Institute, Inc.
- Inventors: Xiongyi CUI; Stephen G. MCGILL; Guy ROSMAN; Simon A. I. STENT
- Web page: https://patentgrep.com/patent/US20230056475A1

## CPC

- B60W 50/06: Improving the dynamic response of the control system, e.g. improving the speed of regulation or avoiding hunting or overshoot
- B60W 60/001: {Planning or execution of driving tasks}
- G06N 3/045: Combinations of networks
- G06N 20/00: Machine learning
- G06V 10/82: using neural networks
- G06V 20/44: {Event detection}
- G06V 20/56: exterior to a vehicle by using sensors mounted on the vehicle
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- 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
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

A method for scenario-based event triggers is described. The method includes generating, by a first machine-learning (ML) model, feature vectors encoding driving scenarios surrounding an ego vehicle. The method also includes detecting, by a second machine-learning (ML) model, a unique driving scenario outside of pre-programmed event triggers corresponding to one of the feature vectors encoding driving scenarios surrounding the ego vehicle. The method further includes triggering uploading of the unique driving scenario outside of pre-programmed event triggers to a central scenario-based event control server.

## Claims

### Claim 1

A method for scenario-based event triggers, comprising:
generating, by a first machine-learning (ML) model, feature vectors encoding driving scenarios surrounding an ego vehicle;
detecting, by a second machine-learning (ML) model, a unique driving scenario outside of pre-programmed event triggers corresponding to one of the feature vectors encoding driving scenarios surrounding the ego vehicle; and
triggering uploading of the unique driving scenario outside of pre-programmed event triggers to a central scenario-based event control server.

### Claim 2 (depends on 1)

The method of claim 1, in which generating the feature vectors encoding driving scenarios comprises:
feeding sensor data captured by the ego vehicle to the first ML model to generate vectors of hash values encoding driving scenarios surrounding the ego vehicle; and
determining whether a current driving scenario surrounding the ego vehicle corresponds to a rule-based event trigger.

### Claim 3 (depends on 1)

The method of claim 1, further comprising recording of the unique driving scenario outside of pre-programmed event triggers.

### Claim 4 (depends on 1)

The method of claim 1, in which the detecting a unique driving scenario comprises:
receiving an output of a perception model of the ego vehicle;
identifying a detection result of the perception model for which the perception model indicates a lack of confidence; and
triggering an event in response to the identifying the detection result of the perception model for which the perception model indicates the lack of confidence.

### Claim 5 (depends on 1)

The method of claim 1, further comprising receiving, from the central scenario-based event control server, additional instructions regarding trigger events according to a geolocation, model number, and/or a sensor package version of the ego vehicle.

### Claim 6 (depends on 5)

The method of claim 5, in which the additional instructions comprises a length of an event recorded, a type of sensor to record, an event trigger priority, and/or an identification of logs deleted when an event data exceeds a vehicle data storage.

### Claim 7 (depends on 1)

The method of claim 1, in which the ego vehicle is part of a fleet of vehicles.

### Claim 8 (depends on 1)

The method of claim 1, further comprising categorizing and detecting driving scenarios identified as error pruning, complex and/or unique.

### Claim 9

A non-transitory computer-readable medium having program code recorded thereon for scenario-based event triggers, the program code being executed by a processor and comprising:
program code to generate, by a first machine-learning (ML) model, feature vectors encoding driving scenarios surrounding an ego vehicle;
program code to detect, by a second machine-learning (ML) model, a unique driving scenario outside of pre-programmed event triggers corresponding to one of the feature vectors encoding driving scenarios surrounding the ego vehicle; and
program code to trigger an upload of the unique driving scenario outside of pre-programmed event triggers to a central scenario-based event control server.

### Claim 10 (depends on 9)

The non-transitory computer-readable medium of claim 9, in which the program code to generate the feature vectors encoding driving scenarios comprises:
program code to feed sensor data captured by the ego vehicle to the first ML model to generate vectors of hash values encoding driving scenarios surrounding the ego vehicle; and
program code to determine whether a current driving scenario surrounding the ego vehicle corresponds to a rule-based event trigger.

### Claim 11 (depends on 9)

The non-transitory computer-readable medium of claim 9, further comprising program code to record the unique driving scenario outside of pre-programmed event triggers.

### Claim 12 (depends on 9)

The non-transitory computer-readable medium of claim 9, in which the program code to detect a unique driving scenario comprises:
program code to receive an output of a perception model of the ego vehicle;
program code to identify a detection result of the perception model for which the perception model indicates a lack of confidence; and
program code to trigger the event in response to the identifying the detection result of the perception model for which the perception model indicates the lack of confidence.

### Claim 13 (depends on 9)

The non-transitory computer-readable medium of claim 9, further comprising program code to receive, from the central scenario-based event control server, additional instructions regarding trigger events according to a geolocation, model number, and/or a sensor package version of the ego vehicle.

### Claim 14 (depends on 13)

The non-transitory computer-readable medium of claim 13, in which the additional instructions comprises a length of the event recorded, a type of sensor to record, an event trigger priority, and/or an identification of logs deleted when an event data exceeds a vehicle data storage.

### Claim 15 (depends on 9)

The non-transitory computer-readable medium of claim 9, in which the ego vehicle is part of a fleet of vehicles.

### Claim 16 (depends on 9)

The non-transitory computer-readable medium of claim 9, further comprising program code to categorize and detecting driving scenarios identified as error pruning, complex and/or unique.

### Claim 17

A system for scenario-based event triggers, comprising:
a feature vector generator to generate feature vectors encoding driving scenarios surrounding an ego vehicle using a first machine-learning (ML) model;
a scenario-based event trigger to detect a unique driving scenario outside of pre-programmed event triggers corresponding to one of the feature vectors encoding driving scenarios surrounding the ego vehicle using a second machine-learning (ML) model; and
a driving-scenario upload trigger module to trigger an upload of the unique driving scenario outside of pre-programmed event triggers to a central scenario-based event control server.

### Claim 18 (depends on 17)

The system of claim 17, in which the feature vector generator is further to feed sensor data captured by the ego vehicle to the first ML model to generate vectors of hash values encoding driving scenarios surrounding the ego vehicle, and to determine whether a current driving scenario surrounding the ego vehicle corresponds to a rule-based event trigger.

### Claim 19 (depends on 17)

The system of claim 17, further comprising the central scenario-based event control server to provide additional instructions regarding trigger events according to a geolocation, model number, and/or a sensor package version of the ego vehicle.

### Claim 20 (depends on 17)

The system of claim 17, in which the driving-scenario upload trigger module is further configured to categorize and detect driving scenarios identified as error pruning, complex and/or unique.