EP4359274A1 - Procédé d'optimisation de trajectoire - Google Patents

Procédé d'optimisation de trajectoire

Info

Publication number
EP4359274A1
EP4359274A1 EP22758420.8A EP22758420A EP4359274A1 EP 4359274 A1 EP4359274 A1 EP 4359274A1 EP 22758420 A EP22758420 A EP 22758420A EP 4359274 A1 EP4359274 A1 EP 4359274A1
Authority
EP
European Patent Office
Prior art keywords
trajectory
driving
information
travel
route
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP22758420.8A
Other languages
German (de)
English (en)
Inventor
Nicolas Stein
Hendrik DEUSCH
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Continental Automotive Technologies GmbH
Original Assignee
Continental Automotive Technologies GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Continental Automotive Technologies GmbH filed Critical Continental Automotive Technologies GmbH
Publication of EP4359274A1 publication Critical patent/EP4359274A1/fr
Pending legal-status Critical Current

Links

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
    • B62D—MOTOR VEHICLES; TRAILERS
    • B62D15/00—Steering not otherwise provided for
    • B62D15/02—Steering position indicators ; Steering position determination; Steering aids
    • B62D15/027—Parking aids, e.g. instruction means
    • B62D15/0285—Parking performed automatically
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • 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
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/06—Automatic manoeuvring for parking
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • 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
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

Definitions

  • the invention relates to a method for trajectory optimization in recurring driving situations, in particular in recurring parking situations.
  • a trajectory traveled manually by the human driver from a starting position to a parking space is stored as the target position, including the parking maneuver and the detected surroundings. Based on this stored information, the stored trajectory can be traversed at a later point in time.
  • the disadvantage here is that the trajectory can only be followed as it was originally stored. Since the trajectory driven by the human driver is often not optimal in terms of length, time and/or the steering angle, and this manually driven trajectory forms the basis for the automated driving process, the automatically driven trajectory is often not optimal with regard to the aforementioned trajectory properties.
  • the invention relates to a method for determining a driving trajectory for recurring driving situations.
  • the procedure has the following steps:
  • a route is traveled from a starting position to a target position by a human driver using a vehicle.
  • This route refers to the recurring driving situation, i.e. it will be driven through more often in the future, for example when parking to a whimper, at work, etc.
  • the sensor system can be formed by any vehicle sensor system, via which the environment of the route can be detected and the collision-free driving lane can be determined therefrom.
  • the sensor system can include one or more ultrasonic sensors, at least one camera, at least one radar sensor and/or at least one LIDAR sensor.
  • information about a traffic lane that can be driven on is generated and stored based on this environmental information. This defines the area or driving path within which the trajectory can be planned.
  • a driving trajectory is determined by means of a computing unit of the vehicle based on the information on the driving lane that can be driven on.
  • the computing unit implements a strategy of reinforcement learning, ie it is over several iterative test trajectories, a Assessment of the test trajectories and feedback to the learning system as to whether the trajectory properties of the currently calculated test trajectory have improved compared to a previous test trajectory, a driving trajectory is sought that has improved trajectory properties.
  • the determined travel trajectory is stored in order to be able to drive the route automatically or partially automatically using a driver assistance system based on this travel trajectory in the future.
  • the driver can be outside the vehicle, i.e. the vehicle drives itself to the parking position, for example.
  • the technical advantage of the method according to the invention is that the reinforcement learning method does not require complex training data to generate an optimized driving trajectory, but rather the human driver only has to travel the route at least once from a starting position to a target position in order to find the passable lane determine. An optimized tramline is then found by means of reinforcement learning.
  • This has the advantage that the driving trajectory to be determined by the optimization is not limited by the quality of the training data, but a driving trajectory that is better than the driving trajectory driven by the human driver can be determined by the reinforcement learning.
  • an agent and an evaluation system are implemented in the computing unit.
  • the agent is configured to determine a travel trajectory without training data, which is optimized with regard to predefined trajectory properties.
  • a travel trajectory calculated by the agent is assessed by the rating system based on trajectory properties and depending on the A new travel trajectory is calculated based on the assessment result.
  • new travel trajectories can be calculated, influenced by the assessment result, and travel trajectories with better trajectory properties can thereby be generated.
  • a number of different trajectory properties are used to assess a trajectory.
  • the different trajectory properties can preferably be weighted differently.
  • a new trajectory can be calculated under one or more specifications, in such a way that one or more trajectory properties are improved. This can be done, for example, in such a way that one or more trajectory properties are improved and other trajectory properties are degraded. For example, a very long trajectory can be improved by reducing the trajectory length in order to reach the target position more quickly.
  • the agent iteratively calculates new travel trajectories in such a way that the assessment result is increased.
  • a number of different trajectory properties are preferably included in the assessment. As a result, the trajectory can be improved over several iteration steps.
  • the evaluation system includes a reward function that calculates a positive or negative reward for a calculated travel trajectory.
  • This reward is feedback information for the agent, which influences subsequent trajectory calculations.
  • the agent can be influenced in such a way that successive trajectories with better Trajectory properties and thus a better assessment result are calculated.
  • the agent iteratively calculates travel trajectories such that a subsequent travel trajectory receives a higher positive reward than a previous travel trajectory.
  • the quality of the travel trajectories can thus be successively improved and a final travel trajectory which has sufficiently good trajectory properties can be determined by the reinforcing learning method.
  • the trajectory properties used to assess a driving trajectory include the time required to travel through the driving trajectory, the distance covered by the driving trajectory, information about steering angle changes, information about longitudinal acceleration and/or information about lateral acceleration.
  • the travel trajectories can be assessed objectively by means of these trajectory properties, based on the perception of a human driver.
  • the route from the starting position to the target position is traversed several times. This can exclusively be a number of driving processes carried out by the human driver or driving processes that are also carried out at least partially in an automated manner. Information about the surroundings is recorded by a sensor system in the vehicle and information about the lane that can be driven on is determined and stored. More comprehensive environmental information can be obtained by driving along the route several times, so that an improved trajectory determination can take place.
  • This fusion of environmental information or tramline information allows a modified, in particular larger, tramline to be obtained. Based on this modified tramline, there is more freedom for the calculation of travel trajectories.
  • the environment when driving through the route from the starting point to the destination, the environment is continuously detected and a driving trajectory is determined by reinforcement learning if it can be seen from the detected environmental information that the information on the trafficable lane has changed due to static objects . It can thereby be ensured that the travel trajectory is continuously adapted to static changes in the driving lane that can be driven on, for example as a result of a structural change.
  • the route from the starting position to the target position is driven through automatically based on the determined travel trajectory. There is a deviation from the determined travel trajectory when an obstacle is detected on the determined travel trajectory. This makes it possible to react situation-specifically to temporary changes in the passable lane and avoid collisions by avoiding collisions.
  • the computing unit determines a number of different optimized driving trajectories using the reinforcement learning strategy, and a driving trajectory from these optimized driving trajectories is selected based on one or more target criteria. In this way it can be achieved that in cases in which several different optimized driving trajectories can be determined, that optimized travel trajectory is used that best meets the specified assessment criteria.
  • the invention relates to a system for determining a driving trajectory for recurring driving situations, comprising a computing unit provided in a vehicle. The procedure has the following steps:
  • FIG. 2 shows an example of a block diagram that explains the method steps for determining a driving trajectory for recurring driving situations.
  • FIG. 1 shows an example and a rough schematic of a vehicle F at the start of a recurring driving situation that is described by a route FS from a starting position SP to a target position ZP.
  • a route FS can be, for example, the route between a property access road and a parking position provided on the property, for example a garage parking space.
  • a computing unit of the vehicle F In order to be able to drive through this route FS automatically, it is necessary for a computing unit of the vehicle F to calculate a travel trajectory, along which the vehicle F travels from the starting position SP to the target position ZP.
  • the driving trajectory it is necessary that information about the environment in the area of the driving route FS is available in order to know the freely navigable area based on this, also referred to below as the driving path.
  • the vehicle F has a sensor system, by means of which the environmental information can be recorded.
  • the sensor system can include, for example, ultrasonic sensors, one or more cameras, one or more radar sensors and/or one or more LIDAR sensors.
  • the route FS it is necessary for the route FS to be traveled at least once by the human driver, ie from the starting position SP to the target position ZP.
  • the environmental information can be recorded by the sensors of the vehicle F and the driving path that can be driven on can thus be determined.
  • a travel trajectory from the starting position SP to the target position ZP can be calculated. This is preferably done by a trajectory planner that is implemented in a computer unit of the vehicle.
  • the computer unit is configured for machine learning, based on the principle of reinforcement learning.
  • the computer unit has an agent that can determine a travel trajectory without training data, which is optimized with regard to predefined trajectory properties.
  • the computer unit does not implement a supervised learning method based on a set of training data.
  • an evaluation system which is designed to evaluate a calculated travel trajectory based on predefined trajectory properties. Depending on how well the calculated driving trajectory fulfills the specified trajectory properties, a positive or negative reward is provided by the evaluation system.
  • the rating system generates a positive reward if the trajectory properties of a newly calculated travel trajectory are better than the trajectory properties of a previously calculated trajectory.
  • the rating system generates a negative reward, for example, if the trajectory properties of a newly calculated driving trajectory are worse than the trajectory properties of a previously calculated trajectory.
  • the previously calculated trajectory can in each case be the trajectory calculated directly beforehand, or a trajectory can be used that was calculated a long time ago.
  • the agent Based on the positive or negative rewards, the agent independently learns how a driving trajectory has to be changed in order to receive positive rewards.
  • the rewards can be used to derive a utility function that represents the value of a trajectory property in relation to receiving a positive reward. This makes it possible to achieve a targeted improvement in the trajectory properties.
  • the process for determining a travel trajectory is preferably initially initiated when a driving path that can be driven on is available for a repeated driving situation. For example, the calculation can be initiated immediately after reaching the target position ZP. Travel trajectories are preferably calculated until a sufficiently good travel trajectory can be determined. One or more termination criteria can be defined here, based on which the iterative trajectory optimization is terminated.
  • the trajectory properties on which the assessment of the travel trajectories is based can be, for example, the time to travel through the travel trajectory, the distance of the travel trajectory, information about the steering angle change, information about the longitudinal acceleration and/or information about the lateral acceleration.
  • the route FS is preferably traversed several times between the starting position SP and the target position ZP in order to capture environmental information. As a result, different environmental information can be recorded in chronological succession. These can be combined or merged with each other in order to obtain improved information on the traffic lane that can be driven on. This improved information can then be used as a basis for calculating the driving trajectory.
  • the advantage lies in the fact that with each additional trajectory driven, in particular manually driven trajectory, the driving path that can be driven on can be enlarged and thus there is also a higher potential for optimizing the driving trajectory through reinforcement learning.
  • a detection of the surroundings also takes place when driving along the route FS automatically on the basis of a previously calculated driving trajectory. On the one hand, this is done with the aim that obstructive objects located on the travel trajectory are detected and the vehicle can avoid them or stop in front of them.
  • the environment is also detected during automated driving because new stationary objects are recognized on a driving path that was previously detected as passable, and thus a new trajectory optimization through reinforcement learning, taking into account the changed driving path, becomes possible. In other words, the calculation of the travel trajectory can be carried out again when the driving lane that can be driven on changes, in order to determine an optimized travel trajectory based on the changed environmental situation.
  • the trajectory optimization can preferably take place several times, for example based on other optimization criteria, in order to obtain several different optimized travel trajectories. Afterward these different optimized driving trajectories can be compared with each other.
  • the different optimized driving trajectories can be compared with each other based on predetermined criteria such as time to drive through the driving trajectory, distance of the driving trajectory, information about the steering angle change, information about the longitudinal acceleration and/or information about the lateral acceleration in order to determine a final driving trajectory, which is then used for the autonomous driving function is used.
  • the autonomous driving function can be “trained parking” in a recurring parking situation.
  • FIG. 2 shows a diagram that explains the method steps for determining the travel trajectory.
  • a route is traveled from a start position to a target position by a human driver using a vehicle (S10).
  • a travel trajectory is then determined based on information on the traffic lane that can be driven on by means of a computing unit in the vehicle (S13).
  • the computing unit implements a strategy of reinforcement learning, in which a calculated travel trajectory is assessed based on trajectory properties and iteratively optimized, specifically in such a way that an attempt is made to successively improve the trajectory properties of the travel trajectory through the iteration steps.
  • the determined driving trajectory is stored (S14). As a result, the driving trajectory determined can be used for automated driving in the recurring driving situation.

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Software Systems (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • Combustion & Propulsion (AREA)
  • Evolutionary Computation (AREA)
  • Data Mining & Analysis (AREA)
  • Chemical & Material Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Mathematical Physics (AREA)
  • Artificial Intelligence (AREA)
  • Medical Informatics (AREA)
  • Human Computer Interaction (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Traffic Control Systems (AREA)
  • Navigation (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

L'invention concerne un procédé de détermination d'une trajectoire de conduite pour des situations de conduite récurrentes, le procédé comprenant les étapes suivantes : - parcours d'un trajet (FS) depuis une position de départ (SP) jusqu'à une position cible (ZP) par un conducteur humain à l'aide d'un véhicule (F) (S10) ; - acquisition d'informations relatives à l'environnement par un système de détection du véhicule (F) pendant le parcours du trajet (FS) (S11) ; - mémorisation d'informations concernant une voie de circulation pouvant être empruntée sur la base des informations relatives à l'environnement (S12) ; - détermination d'une trajectoire de conduite sur la base d'informations concernant la voie de circulation pouvant être empruntée au moyen d'une unité de calcul du véhicule (F) qui implémente une stratégie de l'apprentissage par renforcement (S13) ; ‑ mémorisation de la trajectoire de conduite déterminée (S14).
EP22758420.8A 2021-06-25 2022-06-21 Procédé d'optimisation de trajectoire Pending EP4359274A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102021206588.0A DE102021206588A1 (de) 2021-06-25 2021-06-25 Verfahren zur Trajektorienoptimierung
PCT/DE2022/200136 WO2022268274A1 (fr) 2021-06-25 2022-06-21 Procédé d'optimisation de trajectoire

Publications (1)

Publication Number Publication Date
EP4359274A1 true EP4359274A1 (fr) 2024-05-01

Family

ID=83059126

Family Applications (1)

Application Number Title Priority Date Filing Date
EP22758420.8A Pending EP4359274A1 (fr) 2021-06-25 2022-06-21 Procédé d'optimisation de trajectoire

Country Status (4)

Country Link
EP (1) EP4359274A1 (fr)
CN (1) CN117500708A (fr)
DE (1) DE102021206588A1 (fr)
WO (1) WO2022268274A1 (fr)

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102017110020A1 (de) 2017-05-10 2018-11-15 Valeo Schalter Und Sensoren Gmbh Verfahren zum Betreiben eines Fahrerassistenzsystems eines Kraftfahrzeugs zum Manövrieren des Kraftfahrzeugs in einem fahrbaren Bereich, Fahrerassistenzsystem sowie Kraftfahrzeug
CN108860139B (zh) * 2018-04-11 2019-11-29 浙江零跑科技有限公司 一种基于深度增强学习的自动泊车轨迹规划方法
DE102018129556A1 (de) * 2018-11-23 2020-05-28 Valeo Schalter Und Sensoren Gmbh Verfahren zum wenigstens teilautonomen Fahren eines Fahrzeugs im Rahmen eines Parkvorgangs
CN111098852B (zh) 2019-12-02 2021-03-12 北京交通大学 一种基于强化学习的泊车路径规划方法

Also Published As

Publication number Publication date
CN117500708A (zh) 2024-02-02
DE102021206588A1 (de) 2022-12-29
WO2022268274A1 (fr) 2022-12-29

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