# US 11,994,395 B2 · Method, machine readable medium, device, and vehicle for determining a route connecting a plurality of destinations in a road network, method, machine readable medium, and device for training a machine learning module

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- Number: US 11,994,395 B2
- Document ID: US11994395B2
- Status: Granted
- Kind: B2
- Application number: 16/937,610
- Filed: 2020-07-24
- Published: 2022-01-27
- Granted: 2024-05-28
- Earliest claimed: 2020-07-24
- Assignees: Bayerische Motoren Werke Aktiengesellschaft
- Applicants: Bayerische Motoren Werke Aktiengesellschaft
- Inventors: Jilei Tian; Yang Cao
- Web page: https://patentgrep.com/patent/US11994395B2

## CPC

- G01C 21/3407: {specially adapted for specific applications}
- G01C 21/343: {Calculating itineraries (travelling salesman problem G06Q10/04; optimisation of routes G06Q10/047)}
- G01C 21/3453: {Special cost functions, i.e. other than distance or default speed limit of road segments}
- G01C 21/3626: {Details of the output of route guidance instructions (traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968)}
- G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
- G06N 3/045: Combinations of networks
- G06N 3/0455: Auto-encoder networks; Encoder-decoder networks
- G06N 3/0499: Feedforward networks
- G06N 3/088: Non-supervised learning, e.g. competitive learning
- G06N 3/0895: Weakly supervised learning, e.g. semi-supervised or self-supervised learning
- G06N 3/092: Reinforcement learning
- G06N 20/00: Machine learning
- G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

Embodiments provide a method, a machine readable medium, a device and a vehicle for determining a route connecting a plurality of destinations in a road network, and a method, a machine readable medium and a device for training a machine learning module for determining a route connecting a plurality of destinations in a road network. The method for determining a route connecting a plurality of destinations in a road network comprises receiving input information, which comprises information on distances between the plurality of destinations in the road network. The method further comprises inputting the input information on the distances into a trained machine learning module and providing output information on the route connecting the plurality of destinations based on an output of the trained machine learning module.

## Claims

### Claim 1

A method for determining a route connecting a plurality of destinations in a road network, the method comprising:
receiving input information, which comprises information on routing distances between two destinations of each pair of destinations among the plurality of destinations in the road network, wherein a routing distance between two destinations of each pair is a road distance measured from one destination of the pair to the other destination of the pair on the road network;
inputting the input information into a trained machine learning module;
determining, by the trained machine learning module, a route connecting the plurality of destinations in the road network based on an overall routing distance of candidate routes connecting the plurality of destinations; and
generating output information on the determined route,
wherein the information on the routing distances comprises a matrix with mutual routing distances between two destinations of each pair of the plurality of destinations, and a mutual routing distance between two destinations of at least one pair is asymmetric.

### Claim 2 (depends on 1)

The method of claim 1, wherein the input information further comprises information on a time window constraint for at least one of the plurality of destinations, and further comprising inputting the information on the time window constraint into the trained machine learning module, wherein the route is determined further based on a compliance of the time window constraint for a destination in the candidate routes.

### Claim 3 (depends on 1)

The method of claim 1, wherein the mutual routing distances between two destinations of at least one pair of destinations is asymmetric.

### Claim 4 (depends on 1)

The method of claim 1, further comprising determining embedding information for the machine learning module.

### Claim 5 (depends on 3)

The method of claim 3, wherein the embedding information comprises a vector with a predetermined size.

### Claim 6 (depends on 1)

The method of claim 1, further comprising adapting the input information to a predetermined number of destinations.

### Claim 7 (depends on 6)

The method of claim 6, wherein the adapting comprises complementing the plurality of destinations to the predetermined number of destinations by introducing virtual destinations.

### Claim 8 (depends on 6)

The method of claim 6, wherein the adapting comprises complementing destinations without a time window constraint with a non-restrictive time window constraint.

### Claim 9 (depends on 1)

A device comprising processing circuitry configured to perform the method of claim 1.

### Claim 10 (depends on 9)

A vehicle comprising the device of claim 9.