# US 11,443,633 B2 · Method and system for vehicle-to-pedestrian collision avoidance

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- Number: US 11,443,633 B2
- Document ID: US11443633B2
- Status: Granted
- Kind: B2
- Application number: 17/334,434
- Filed: 2021-05-28
- Published: 2021-09-16
- Granted: 2022-09-13
- Earliest claimed: 2018-05-10
- Applicants: Bastien Beauchamp
- Inventors: Bastien Beauchamp
- Web page: https://patentgrep.com/patent/US11443633B2

## CPC

- G01S 13/931: of land vehicles
- G08G 1/166: {for active traffic, e.g. moving vehicles, pedestrians, bikes}
- G01S 5/0284: {Relative positioning}
- G01S 5/0294: {Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering}
- G01S 19/393: {Trajectory determination or predictive tracking, e.g. Kalman filtering}
- G01S 19/51: Relative positioning
- G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
- G06N 3/092: Reinforcement learning
- G08G 1/163: {involving continuous checking}
- H04W 4/023: {using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds}
- H04W 4/029: Location-based management or tracking services
- H04W 4/40: for vehicles, e.g. vehicle-to-pedestrians [V2P]
- H04W 64/00: Locating users or terminals {or network equipment} for network management purposes, e.g. mobility management
- H04W 76/50: for emergency connections
- G01S 5/0295: {Proximity-based methods, e.g. position inferred from reception of particular signals}
- G01S 5/08: Position of single direction-finder fixed by determining direction of a plurality of spaced sources of known location
- G01S 5/10: Position of receiver fixed by co-ordinating a plurality of position lines defined by path-difference measurements {, e.g. omega or decca systems}(G01S5/12 takes precedence {; beacons and receivers cooperating therewith G01S1/306, G01S1/308})
- G01S 13/86: Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
- G01S 2013/9316: {combined with communication equipment with other vehicles or with base stations}
- G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
- G06N 3/044: Recurrent networks, e.g. Hopfield networks
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- H04W 64/006: {with additional information processing, e.g. for direction or speed determination}
- 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
- 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})
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)

## Abstract

A method and a system for vehicle-to-pedestrian collision avoidance system, the system comprising participants consisting of Long-Term Evolution (LTE)-capable user equipment (UE) terminals physically linked to at least one vehicle and at least one pedestrian; wherein a spatiotemporal positioning of the terminals is determined from Long Term Evolution (LTE) cellular radio signals mediated by Long-Term Evolution (LTE) cellular base stations (BS) and a Location Service Client (LCS) server including an embedded Artificial Intelligence algorithm comprising a Recurrent Neural Network (RNN) algorithm and analyzes the spatiotemporal positioning of the terminals and determines the likely future trajectory and communicates the likely future trajectory of the participants to the terminals physically linked to the pedestrian; the terminals physically linked to the pedestrian include an embedded Artificial Intelligence algorithm comprising a Conditional Random Fields (CRFs) algorithm to determine if the likely future trajectory of the pedestrian is below a vehicle-to-pedestrian proximity threshold limit and, if this condition is reached, communicates a collision-avoidance emergency signal to the at least one pedestrian and/or vehicle that meet the proximity threshold limit.

## Claims

### Claim 1

A vehicle-to-pedestrian collision avoidance system, comprising participants consisting of a set of at least two Long-Term Evolution (LTE)-capable user equipment (UE) terminals physically linked to at least one vehicle and at least one pedestrian;
wherein a spatiotemporal positioning of the terminals is determined from Long Term Evolution (LTE) cellular radio signals mediated by at least three Long-Term Evolution (LTE) cellular base stations (BS) and at least one Location Service Client (LCS) server;
the at least one Location Service Client (LCS) server includes an embedded Artificial Intelligence algorithm comprising a Recurrent Neural Network (RNN) algorithm, analyzes the spatiotemporal positioning of the terminals and determines the likely future trajectory of the participants so as to maximize a reward metric based on Reinforcement Learning (RL) analysis; and communicates the likely future trajectory of the participants to the terminals physically linked to the at least one pedestrian;
the terminals physically linked to the at least one pedestrian include an embedded Artificial Intelligence algorithm comprising a Conditional Random Fields (CRFs) algorithm to determine if the likely future trajectory of the at least one pedestrian is below a vehicle-to-pedestrian proximity threshold limit and, if this condition is reached, the terminal physically linked to the at least one pedestrian communicates a collision-avoidance emergency signal to at least one of: the at least one pedestrian and the at least one vehicle that meet the proximity threshold limit.

### Claim 2 (depends on 1)

The system of claim 1, wherein the terminals physically linked to the at least one vehicle include an embedded Artificial Intelligence algorithm comprising a Conditional Random Fields (CRFs) algorithm to determine if the likely future trajectory of the at least one vehicle is below the vehicle-to-pedestrian proximity threshold limit and, if this condition is reached, the terminal physically linked to the at least one vehicle communicates the collision-avoidance emergency signal to the at least one pedestrian that meets the proximity threshold limit.

### Claim 3 (depends on 1)

The system of claim 1, wherein the Long Term Evolution (LTE) uses 5G NR new radio access technology (RAT) developed by 3GPP for the 5G (fifth generation) mobile network.

### Claim 4 (depends on 1)

The system of claim 1, wherein the spatiotemporal positioning of the terminals is determined by sensors embedded in the terminals, said sensors comprising at least one of: Global Navigation Satellite Systems (GNSS, GPS), camera, sonar, lidar, radar, accelerometry, inertial, and gyroscopic sensors.

### Claim 5 (depends on 1)

The system of claim 1, wherein the spatiotemporal positioning of the terminals is determined by sensors embedded in the terminals, said sensors comprising at least one of: Global Navigation Satellite Systems (GNSS, GPS), camera, sonar, lidar, radar, accelerometry, inertial, and gyroscopic sensors and wherein the spatiotemporal positioning of the terminals receives geolocation input from sensors distributed in the urban environment.

### Claim 6 (depends on 1)

The system of claim 1, wherein the User Equipment (UE) terminals comprise a computational unit for processing an artificial Intelligence algorithm, the computational unit being at least one of: a mobile application, a software, a firmware, a hardware, a physical device, and a computing device.

### Claim 7 (depends on 1)

The system of claim 1, wherein the collision-avoidance emergency signal comprises a decision process for enabling at least one of: changing the direction of the vehicle; changing the speed of the vehicle; and sending a signal to the at least one pedestrian.

### Claim 8 (depends on 1)

The system of claim 1, wherein said participants comprise a logo with proprietary bar code.