# US 11,999,368 B2 · Systems and methods for automated vehicle fleet management according to dynamic pedagogical behavior reinforcement

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- Number: US 11,999,368 B2
- Document ID: US11999368B2
- Status: Granted
- Kind: B2
- Application number: 17/706,025
- Filed: 2022-03-28
- Published: 2023-09-28
- Granted: 2024-06-04
- Earliest claimed: 2022-03-28
- Assignees: Bendix Commercial Vehicle Systems LLC
- Applicants: Bendix Commercial Vehicle Systems LLC
- Inventors: Andreas U. Kuehnle; Shaun M. Howard
- Web page: https://patentgrep.com/patent/US11999368B2

## CPC

- B60W 50/087: {where the control system corrects or modifies a request from the driver}
- G06V 10/778: Active pattern-learning, e.g. online learning of image or video features
- G06V 20/41: {Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items (segmenting video sequences G06V20/49)}
- G06V 20/44: {Event detection}
- G06V 20/56: exterior to a vehicle by using sensors mounted on the vehicle
- G08G 1/20: {Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles}
- G09B 19/167: {Control of land vehicles}
- B60W 2540/30: Driving style
- 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
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93})
- G09B: EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS (devices for psychotechnics or for testing reaction times A61B5/16; games, sports, amusements A63; projectors, projector screens G03B)

## Abstract

Vehicle fleet management includes processing event-based data corresponding to detected events to generate event data sets that include: at least video corresponding to the event and data identifying an event-type for the event. One or more pedagogical event pairs are identified, each of which includes a first event data set reflecting a “good” response to an event-type and a second event data set reflecting a “bad” response to the event-type. At least one cluster of drivers having similar occurrence rates for the event-type is identified. One or more training lessons are generated for the cluster based on the event type, which training lessons include at least the video of the first event data set and the video of the second event data set. The training lessons are administered to a driver of the cluster via a computing device. The autonomous control of one or more vehicle systems is instituted based on the administered training lessons.

## Claims

### Claim 1

A system for vehicle fleet management and dynamic pedagogical behavior reinforcement, comprising:
an event detection module configured to process event-based data corresponding to detected driver and/or vehicle related events so as to generate event data sets therefrom, wherein the event data sets include at least: video corresponding to the event and data identifying an event-type for the event;
an event pairing module configured to process the event data sets so as to identify one or more pedagogical event pairs, wherein each pedagogical event pair includes a first event data set reflecting a good response to an event-type and a second event data set reflecting a bad response to the event-type;
a clustering module configured to process the event data sets so as to identify at least one cluster of drivers, wherein the cluster includes one or more drivers with respective occurrence rates for the event-type that are approximately a first occurrence rate;
a lesson administration module configured to:
generate one or more training lessons for the cluster of drivers based on the event type, wherein the one or more training lessons include at least the video of the first event data set and the video of the second event data set, and
administer the generated training lessons to a driver of the cluster of drivers via a computing device; and
a vehicle control module configured to control one or more vehicle systems based on the administered training lessons.

### Claim 2 (depends on 1)

The system of claim 1, wherein the event data sets further include data identifying event attributes, and wherein the pedagogical event pair includes similar event attributes.

### Claim 3 (depends on 1)

The system of claim 1, wherein the one or more training lessons include: the video of a first set of pedagogical event pairs corresponding to a first event-type, and a second set of pedagogical event pairs corresponding to a second event-type.

### Claim 4 (depends on 1)

The system of claim 1, wherein the lesson administration module is further configured to generate individualized training lessons for the driver based on the driver's occurrence rates for another event-type.

### Claim 5 (depends on 1)

The system of claim 1, wherein the lesson administration module is further configured to maintain a training profile for each driver, and wherein the control of the one or more vehicle systems is based on the training profile.

### Claim 6 (depends on 5)

The system of claim 5, wherein the training profile includes an improvement metric for the driver with respect to the event-type.

### Claim 7 (depends on 1)

The system of claim 1, wherein the control of the one or more vehicle systems includes at least one of: providing warnings, intervening in the operation of the vehicle, and alerting remote locations/devices of detected events.

### Claim 8

A method for vehicle fleet management and dynamic pedagogical behavior reinforcement, comprising:
processing event-based data corresponding to detected driver and/or vehicle related events so as to generate event data sets therefrom, wherein the event data sets include at least: video corresponding to the event and data identifying an event-type for the event;
identifying, from the event data sets, one or more pedagogical event pairs, wherein each pedagogical event pair includes a first event data set reflecting a good response to an event-type and a second event data set reflecting a bad response to the event-type;
analyzing the event data sets so as to identify at least one cluster of drivers, wherein the cluster includes one or more drivers with respective occurrence rates for the event-type that are approximately a first occurrence rate;
generating one or more training lessons for the cluster of drivers based on the event type, wherein the one or more training lessons include at least the video of the first event data set and the video of the second event data set;
administering the generated training lessons to a driver of the cluster of drivers via a computing device; and
initiating automated control over one or more vehicle systems based on the administered training lessons.

### Claim 9 (depends on 1)

The method of claim 1, wherein the event data sets further include data identifying event attributes, and wherein the pedagogical event pair includes similar event attributes.

### Claim 10 (depends on 1)

The method of claim 1, wherein the one or more training lessons include: the video of a first set of pedagogical event pairs corresponding to a first event-type, and a second set of pedagogical event pairs corresponding to a second event-type.

### Claim 11 (depends on 1)

The method of claim 1, further comprising:
generating individualized training lessons for the driver based on the driver's occurrence rates for another event-type.

### Claim 12 (depends on 1)

The method of claim 1, further comprising:
maintaining a training profile for each driver, and wherein the control of the one or more vehicle systems is based on the training profile.

### Claim 13 (depends on 12)

The method of claim 12, wherein the training profile includes an improvement metric for the driver with respect to the event-type.

### Claim 14 (depends on 1)

The method of claim 1, wherein the control of the one or more vehicle systems includes at least one of: providing warnings, intervening in the operation of the vehicle, and alerting remote locations/devices of detected events.