# US 12,222,722 B2 · Platform for path planning system development for automated driving system

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- Number: US 12,222,722 B2
- Document ID: US12222722B2
- Status: Granted
- Kind: B2
- Application number: 17/729,470
- Filed: 2022-04-26
- Published: 2022-11-03
- Granted: 2025-02-11
- Earliest claimed: 2021-04-29
- Assignees: Zenseact AB
- Applicants: Zenseact AB
- Inventors: Magnus Gyllenhammar; Carl Zandén; Majid Khorsand Vakilzadeh
- Web page: https://patentgrep.com/patent/US12222722B2

## CPC

- B60W 60/0025: {specially adapted for specific operations}
- G05D 1/0214: {in accordance with safety or protection criteria, e.g. avoiding hazardous areas (monitoring the location of vehicles within a certain area, e.g. forbidden or allowed areas, in traffic control systems for road vehicles G08G1/13)}
- G05D 1/0088: {characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours (using knowledge based models G06N5/00)}
- G05D 1/0217: {in accordance with energy consumption, time reduction or distance reduction criteria}
- G05D 1/0221: {involving a learning process}
- G05D 1/0223: {involving speed control of the vehicle (vehicle fittings for automatically controlling, i.e. preventing speed from exceeding an arbitrarily established velocity or maintaining speed at a particular velocity, as selected by the vehicle operator B60K31/00)}
- G05D 1/617: Safety or protection, e.g. defining protection zones around obstacles or avoiding hazards (arrangements for controlling the position or course of two or more vehicles for avoiding collisions therebetween G05D1/693; arrangements for reacting to or preventing system or operator failure G05D1/80)
- G05D 1/644: Optimisation of travel parameters, e.g. of energy consumption, journey time or distance
- G05D 1/65: Following a desired speed profile
- G05D 1/81: Handing over between on-board automatic and on-board manual control
- G06N 3/08: Learning methods
- G06N 3/092: Reinforcement learning
- G06N 3/098: Distributed learning, e.g. federated learning
- B60W 2050/0031: {Mathematical model of the vehicle}
- B60W 2050/0088: {Adaptive recalibration}
- B60W 2520/105: Longitudinal acceleration
- B60W 2520/125: Lateral acceleration
- B60W 2520/14: Yaw
- B60W 2520/16: Pitch
- B60W 2520/18: Roll
- 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
- G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

The present invention relates to a method and apparatus that utilize production vehicles to develop new path planning features for Automated Driving Systems (ADSs) by using federated learning. To achieve this the “under-test” path planning module's output is evaluated in closed-loop in order to produce a cost-function that is subsequently used to update or train a path planning model of the path planning development-module.

## Claims

### Claim 1

A method performed by an in-vehicle computing system for automated development of a path planning development-module of a vehicle, wherein the vehicle is equipped with an Automated Driving System, ADS, the method comprising:
obtaining a candidate path from the path planning development-module, wherein the path planning development-module is configured to generate the candidate path for execution by the vehicle based on a path planning model and data indicative of the surrounding environment of the vehicle;
obtaining a risk map of a surrounding environment of the vehicle, wherein the risk map is formed based on an actuation capability of the vehicle and a location of free-space areas in the surrounding environment, the actuation capability including an uncertainty estimation for the actuation capability and the location of free-space areas comprising an uncertainty estimation for the estimated location of free-space areas,
wherein the risk map comprises a risk parameter for each of a plurality of area segments comprised in the surrounding environment of the vehicle, and
wherein the risk map further has a temporal component indicative of a time evolution of the risk parameters of the area segments based on a predicted temporal evolution of the free-space areas for at least a duration of time defined by a predicted duration of the candidate path;
determining a risk value for the candidate path based on the risk parameters of the area segments to be intersected by the candidate path; and
if the determined risk value satisfies at least one risk criterion, the method further comprises:
generating, at an output, a first control signal indicative of an instruction for the ADS to execute the candidate path;
obtaining at least one quality parameter of the executed candidate path, wherein the at least one quality parameter is indicative of a performance of the candidate path in terms of quality and/or comfort of the executed candidate path;
determining a cost function indicative of a performance of the path planning development-module based on the obtained at least one quality parameter; and
updating one or more parameters of the path planning model by means of an optimization algorithm configured to optimize the determined cost function.

### Claim 2 (depends on 1)

The method according to claim 1, wherein the at least one quality parameter comprises at least one of: lateral acceleration, longitudinal acceleration, vertical acceleration, jerk, path length, roll acceleration, pitch acceleration, yaw acceleration, and fuel consumption.

### Claim 3 (depends on 1)

The method according to claim 1, wherein the step of determining the risk value comprises:
determining an aggregated risk value for the candidate path based on the risk parameters of a set of area segments to be intersected by the candidate path,
wherein the at least one risk criterion comprises an aggregated risk threshold, and wherein step of generating, at an output, the first control signal indicative of an instruction for the ADS to execute the candidate path is only performed if the aggregated risk value is lower than the aggregated risk threshold.

### Claim 4 (depends on 3)

The method according to claim 3, wherein the step of determining an aggregated risk value comprises:
aggregating the risk parameters of the set of area segments to be intersected by the candidate path.

### Claim 5 (depends on 1)

The method according to claim 1, wherein the step of determining the risk value further comprises:
determining a total risk value of the path planning development-module based on the risk parameters of a set of area segments intersected by at least one previously executed path, wherein the set of area segments intersected by at least one previously executed path is associated with a previously obtained risk map of the same time period as the at least one previously executed path, and
wherein the at least one risk criterion comprises a total risk threshold, and wherein step of generating, at an output, the first control signal indicative of an instruction for the ADS to execute the candidate path is only performed if the total risk value is lower than the total risk threshold.

### Claim 6 (depends on 5)

The method according to claim 5, wherein the step of determining the total risk value comprises:
averaging the risk parameters of the set of area segments intersected by the at least one previously executed path in order to determine an average risk exposure of the path planning development-module, the average risk exposure defining the total risk value.

### Claim 7 (depends on 6)

The method according to claim 6, wherein the step of averaging the risk parameters comprises:
for each time sample:
determining a current risk value of the path planning development-module based on a set of area segments intersected by a most recently executed candidate path since a preceding time sample;
determining a current compounded risk value by summing the current risk value with a previously computed compounded risk value for the preceding time sample; and
determining an average risk exposure by dividing the current compounded risk value with a total number of samples used up until and including a current time sample.

### Claim 8 (depends on 1)

The method according to claim 1, further comprising:
obtaining a back-up path from a production platform of the vehicle, the production platform comprising a validated path planning development-module configured to generate the back-up path; and
if the determined risk value fails to satisfy the at least one risk criterion, the method further comprises:
generating, at an output, a second control signal so to execute the obtained back-up path; and
deactivating the path planning development-module.

### Claim 9 (depends on 8)

The method according to claim 8, further comprising:
if the determined risk value fails to satisfy the at least one risk criterion:
generating, at an output, a second control signal so to activate a path planner of the production platform of the vehicle for generating candidate paths to be executed by the vehicle.

### Claim 10 (depends on 1)

The method according to claim 1, further comprising:
transmitting the one or more updated parameters of the path planning model of the path planning development-module to a remote entity;
receiving a set of globally updated parameters of the path planning model of the path planning development-module from the remote entity, wherein the set of globally updated parameters are based on information obtained from a plurality of vehicles comprising the path planning development-module; and
updating the path planning model of the path planning development-module based on the received set of globally updated parameters.

### Claim 11 (depends on 1)

The method according to claim 1, wherein each risk parameter is indicative of at least one of a probability of an accident event if the vehicle were to occupy an associated area segment and a probability of violating a predefined safety threshold.

### Claim 12 (depends on 1)

The method according to claim 1, wherein the location of free-space areas comprises:
a position of external objects located in the surrounding environment of the vehicle, and wherein the estimated position of the external objects includes uncertainties of the position of the external objects, trajectories of any dynamic objects of the external objects and uncertainties of the estimated trajectories of the dynamic objects.

### Claim 13 (depends on 1)

A non-transitory computer-readable storage medium storing one or more instructions configured to be executed by one or more processors of an in-vehicle processing system, the one or more instructions for performing the method according to claim 1.

### Claim 14

An apparatus for automated development a path planning development-module of a vehicle, wherein the vehicle is equipped with an Automated Driving System, ADS, the apparatus comprising control circuitry configured to:
obtain a candidate path from the path planning development-module, wherein the path planning development-module is configured to generate the candidate path for execution by the vehicle based on a path planning model and data indicative of the surrounding environment of the vehicle;
obtain a risk map of a surrounding environment of the vehicle, wherein the risk map is formed based on an actuation capability of the vehicle and a location of free-space areas in the surrounding environment, the actuation capability including an uncertainty estimation for the actuation capability and the location of free-space areas comprising an uncertainty estimation for the estimated location of free-space areas,
wherein the risk map comprises a risk parameter for each of a plurality of area segments comprised in the surrounding environment of the vehicle, and
wherein the risk map further has a temporal component indicative of a time evolution of the risk parameters of the area segments based on a predicted temporal evolution of the free-space areas for at least a duration of time defined by a predicted duration of the candidate path;
determine a risk value for the candidate path based on the risk parameters of the area segments to be intersected by the candidate path; and
if the determined risk value satisfies at least one risk criterion, the control circuitry is further configured to:
generate, at an output, a first control signal indicative of an instruction for the ADS to execute the candidate path;
obtain at least one quality parameter of the executed candidate path, wherein the at least one quality parameter is indicative of a performance of the candidate path in terms of quality and/or comfort of the executed candidate path;
determine a cost function indicative of a performance of the path planning development-module based on the obtained at least one quality parameter; and
update one or more parameters of the path planning model by means of an optimization algorithm configured to optimize the determined cost function.

### Claim 15

A vehicle comprising:
a set of vehicle-mounted sensors configured to monitor a surrounding environment of the vehicle;
an automated driving system, ADS, comprising:
a perception system configured to generate perception data based on sensor data obtained from one or more of the set of vehicle-mounted sensors;
a production-platform path planner;
a path planning development-module configured to generate a candidate path for execution by the vehicle; and
an apparatus for automated development the path planning development-module of the vehicle, wherein the vehicle is equipped with ADS, the apparatus comprising control circuitry configured to:
obtain a candidate path from the path planning development-module, wherein the path planning development-module is configured to generate the candidate path for execution by the vehicle based on a path planning model and data indicative of the surrounding environment of the vehicle;
obtain a risk map of a surrounding environment of the vehicle, wherein the risk map is formed based on an actuation capability of the vehicle and a location of free-space areas in the surrounding environment, the actuation capability including an uncertainty estimation for the actuation capability and the location of free-space areas comprising an uncertainty estimation for the estimated location of free-space areas,
wherein the risk map comprises a risk parameter for each of a plurality of area segments comprised in the surrounding environment of the vehicle, and
wherein the risk map further has a temporal component indicative of a time evolution of the risk parameters of the area segments based on a predicted temporal evolution of the free-space areas for at least a duration of time defined by a predicted duration of the candidate path;
determine a risk value for the candidate path based on the risk parameters of the area segments to be intersected by the candidate path; and
if the determined risk value satisfies at least one risk criterion, the control circuitry is further configured to:
generate, at an output, a first control signal indicative of an instruction for the ADS to execute the candidate path;
obtain at least one quality parameter of the executed candidate path, wherein the at least one quality parameter is indicative of a performance of the candidate path in terms of quality and/or comfort of the executed candidate path;
determine a cost function indicative of a performance of the path planning development-module based on the obtained at least one quality parameter; and
update one or more parameters of the path planning model by means of an optimization algorithm configured to optimize the determined cost function.