WO2021036083A1 - Procédé et dispositif de développement de modèle de comportement de conducteur pour conduite automatique et support d'informations - Google Patents

Procédé et dispositif de développement de modèle de comportement de conducteur pour conduite automatique et support d'informations Download PDF

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Publication number
WO2021036083A1
WO2021036083A1 PCT/CN2019/123508 CN2019123508W WO2021036083A1 WO 2021036083 A1 WO2021036083 A1 WO 2021036083A1 CN 2019123508 W CN2019123508 W CN 2019123508W WO 2021036083 A1 WO2021036083 A1 WO 2021036083A1
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Prior art keywords
driver behavior
change data
data
vehicle
behavior model
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PCT/CN2019/123508
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English (en)
Chinese (zh)
Inventor
杜光辉
袁雁城
张尧文
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格物汽车科技(苏州)有限公司
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Publication of WO2021036083A1 publication Critical patent/WO2021036083A1/fr

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    • 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
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W40/09—Driving style or behaviour

Definitions

  • the present invention relates to the technical field of automatic driving, in particular to a method, equipment and storage medium for developing a driver behavior model for automatic driving.
  • Autonomous vehicles also known as unmanned vehicles, are intelligent vehicles that realize unmanned driving through computer systems. They rely on artificial intelligence, visual computing, radar, monitoring devices, and global positioning systems to work together to enable the vehicle’s computer system to Automatically and safely maneuver motor vehicles without unmanned operation.
  • the embodiments of the present invention provide a driver behavior model development method, device and storage medium for automatic driving, so as to solve the problem that the driver behavior model developed in the existing automatic driving technology cannot effectively extract the end-to-end solution algorithm and solution problem.
  • the present invention provides a driver behavior model development method for autonomous driving, which includes the following steps:
  • the actual output data includes the target vehicle driver's decision data and Operational change data of the target vehicle;
  • step (3) where the correction of the basic model includes:
  • the excitation input includes operating change parameters of surrounding vehicles.
  • the incentive input further includes road traffic information parameters and environmental information parameters.
  • the environmental information parameters include one or any combination of illuminance, weather, temperature, humidity, wind direction, and wind speed;
  • the road traffic information parameters include traffic information parameters for main roads in urban areas, suburban areas One or any combination of road traffic information parameters, national road traffic information parameters, provincial road traffic information parameters, and high-speed traffic information parameters.
  • the incentive input further includes the driver's age parameter, gender parameter, physiological parameter, psychological parameter, and driving age parameter.
  • the target vehicle operation change data includes vehicle position change data, speed change data, acceleration change data, steering angle change data, yaw rate change data, lateral acceleration One or any combination of change data, accelerator opening change data, and brake pedal opening change data.
  • it further includes the operating change parameters of the surrounding vehicles including vehicle position change data, speed change data, acceleration change data, steering angle change data, yaw rate change data, lateral acceleration change data, One or any combination of accelerator opening change data and brake pedal opening change data.
  • the present invention also provides a driver behavior model development device, which includes a memory, a processor, and a program stored in the memory and capable of running on the processor.
  • a driver behavior model development device which includes a memory, a processor, and a program stored in the memory and capable of running on the processor.
  • the present invention also provides a computer-readable storage medium, the computer-readable storage medium stores a driver behavior model development program, which is implemented when the driver behavior model development program is executed by a processor.
  • the above-mentioned driver behavior model development method for autonomous driving is described in detail below.
  • the driver behavior model development method, equipment, and storage medium for automatic driving disclosed in the embodiments of the present application are completely different from the prior art method based on AI technology analysis and training to obtain a driver behavior model from massive amounts of data.
  • First create a basic model in the simulation environment based on the transfer relationship between the excitation input and the theoretical output under the excitation input.
  • Second use the excitation input to intervene in the target vehicle in the field test scenario to obtain the true value of the target vehicle in the field test scenario.
  • Output and finally, use the real output in the field test scenario to modify the basic model to obtain a driver behavior model that is close to the real driver behavior to the greatest extent, and effectively extract end-to-end algorithms and solutions.
  • Fig. 1 is a flowchart of a method for developing a driver behavior model in a first embodiment of the present invention
  • Figure 2 is the model diagram under the first field test scenario
  • Figure 3 is a model diagram under the second field test scenario
  • Figure 4 is a model diagram under the third field test scenario
  • Fig. 5 is a structural block diagram of a driver behavior model development device in the second embodiment of the present invention.
  • This embodiment discloses a driver behavior model development method for automatic driving. As shown in FIG. 1, the development method includes the following steps:
  • the theoretical output here is the driving behavior predicted by the creator of the basic model to have 4 to 10 years of driving experience, and to have good driving behavior within the driving age, and the driving behavior of an experienced driver in the driving environment of the incentive input.
  • (A) Operating variation parameters of vehicles located around the target vehicle in the driving environment including the front and rear directions of the same lane, the parallel position of the left and right adjacent lanes, and the front and rear directions of the left and right adjacent lanes.
  • the operating change parameters of the surrounding vehicles here include vehicle position change data, speed change data, acceleration change data, steering angle change data, yaw rate change data, lateral acceleration change data, accelerator opening change data, brake pedal opening change One or any combination of data.
  • the road traffic information parameters here include one or any combination of main road traffic information parameters in urban areas, suburban road traffic information parameters, national road traffic information parameters, provincial road traffic information parameters, and high-speed traffic information parameters;
  • the environmental information parameters here include One or any combination of illuminance, weather, temperature, humidity, wind direction, wind speed.
  • the creator of the basic model creates the basic model in the simulation environment according to the transfer relationship between the excitation input and the theoretical output under the excitation input.
  • the actual output data includes the target vehicle driver's decision data and the target Vehicle operation change data.
  • the target vehicle operation change data includes vehicle position change data, speed change data, acceleration change data, steering angle change data, yaw rate change data, lateral acceleration change data, accelerator opening change data, brake pedal opening change data One of them or any combination.
  • test scenarios design field test scenarios based on experience and typical scenes of Chinese roads collected throughout the year, including key elements such as road types, vehicle types, weather conditions, vehicle distribution conditions, and vehicle behaviors.
  • a test outline is prepared according to the needs of the driver's behavior model development.
  • the outline content includes the definition of collected data, the change mode of vehicle operation in a single scene, and the change mode of vehicle operation in combined scenes.
  • the person in charge of the test instructs the test vehicle (or "surrounding vehicle") to change its operation mode according to the requirements of the test program, as an incentive to collect the response mode of the driver of the tested vehicle (or "target vehicle”), including the driver The decision-making data and the operational change data of the tested vehicle.
  • the correction process here includes judging whether the actual output data of the target vehicle in the field test scenario is different from the theoretical output, if so, using the real output data to update the theoretical output data, and using the updated theoretical output data to modify the basic model.
  • the vehicle is controlled to accelerate or decelerate by sensing the speed of the preceding vehicle, the distance between the accelerometer, and other related information to ensure that the vehicle and the preceding vehicle maintain a safe and comfortable distance dx.
  • the own vehicle needs to obtain at least the following data: 1The speed of the preceding vehicle in the last time period; 2The acceleration of the preceding vehicle in the last time period; 3The distance between the own vehicle and the preceding vehicle in the last time period; 4The current vehicle in front Estimated parking distance at vehicle speed; 5Estimated parking distance at current speed of the vehicle. And output at least the following data: 1The distance between the vehicle and the preceding vehicle in the current time period; 2The acceleration of the vehicle in the current time period; 3The speed of the vehicle in the current time period.
  • the SC1 scene of the field test includes the correction of the difference between the acceleration and deceleration strategy, and the limitation of the experience value (or "threshold experience value") difference correction while maintaining the distance between the vehicle and the preceding vehicle.
  • the vehicle adopts the deceleration strategy, and the acceleration strategy is used when creating the basic model.
  • the deceleration strategy is used to update the acceleration strategy, and one of the incentive input in this scenario and the updated acceleration strategy is used.
  • the field test SC2 scenario includes the correction of the deceleration strategy difference and the deceleration experience value difference correction adopted by the vehicle after the emergency braking of the vehicle in front.
  • the deceleration of the vehicle is a1
  • the deceleration of the vehicle under the excitation input is a2 (a2 is not equal to a1)
  • the deceleration is updated with deceleration a2 in this scenario a1
  • the intention of changing lanes is predicted by sensing the trajectory of abnormal vehicles in adjacent lanes, and by actively shortening the distance between the vehicle and the preceding vehicle to avoid the full rights of hidden dangers caused by the arbitrary lane change of the adjacent vehicle , The vehicle and the preceding vehicle will eventually maintain a safe and compact distance dx.
  • the vehicle needs to obtain at least the following data: 1The speed change and acceleration change range of the N time period before the lane change vehicle; 2The distance to the vehicle ahead and the change range of the N time period before the lane change vehicle and the change range; 3Last time Cycle distance between the vehicle in front and the vehicle in front; 4Estimation of the parking distance of the vehicle in front at the current speed; 5The usable area of adjacent lanes
  • the field test SC3 scenario includes the correction of the empirical value difference of the distance between the vehicle and the preceding vehicle at different speeds of the vehicle in front, and the revision of the difference of the change rule function.
  • the field test SC3 scenario includes the correction of the lateral control strategy difference, the correction of the experience value of the yaw rate, the correction of the change rate, etc. after the vehicle in front of the vehicle has braked at different levels.
  • the vehicle In the field test SC5 scenario, the vehicle is controlled longitudinally and laterally by sensing the speed, acceleration, and distance of the preceding vehicle, the vehicle being overtaken, and the neighboring vehicle to ensure that the vehicle and the neighboring vehicle stay in place.
  • a safe and comfortable distance (-dx and dRR, etc.), even if the vehicle in front of the vehicle in front of the vehicle does a certain degree of emergency braking without any warning, the vehicle can safely change lanes to the expected adjacent lane.
  • the own vehicle needs to obtain at least the following data: 1The speed of the vehicle to be overtaken in the last time period; 2The acceleration of the vehicle to be overtaken in the last time period; 3The vehicle and the vehicle to be overtaken in the last time period The distance between the vehicle; 4The speed of the vehicle to be overtaken in the last time period; 5The acceleration of the vehicle to be overtaken in the last time period; 6The distance between the own vehicle and the vehicle to be overtaken in the last time period; 7The current vehicle in front Estimated parking distance at vehicle speed; 8 Estimated parking distance at current speed of the vehicle.
  • the field test SC3 scenario includes the correction of the difference between the horizontal and vertical control strategies adopted by the vehicle when the vehicle in front and the overtaken vehicle are in different relative positions and at different relative speeds, and the correction of the difference in related experience values.
  • This embodiment also discloses a driver behavior model development device.
  • the device includes a memory, a processor, and a program stored in the above-mentioned memory and running on the above-mentioned processor, and the program is processed by the above-mentioned processor.
  • the driver's behavior model development method for autonomous driving as shown in Figure 1 is implemented when the driver is executed.
  • This embodiment also discloses a computer-readable storage medium, the computer-readable storage medium stores a driver behavior model development program, and when the driver behavior model development program is executed by a processor, the program shown in FIG. 1 is implemented.
  • Driver behavior model development method for autonomous driving is also disclosed.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Mathematical Physics (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Traffic Control Systems (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

La présente invention concerne un procédé et un dispositif de développement de modèle de comportement de conducteur pour une conduite automatique et un support d'informations. Le procédé comporte les étapes suivantes consistant : (1) à établir un modèle de base dans un environnement de simulation selon une relation transitive entre une entrée d'excitation et une sortie théorique ; (2) à utiliser la même entrée d'excitation que celle destinée à établir le modèle de base pour intervenir dans un véhicule cible dans un scénario de test de terrain de manière à acquérir des données de sortie réelles du véhicule cible sous l'entrée d'excitation dans le scénario de test de terrain, les données de sortie réelles comprenant des données de décision de conducteur de véhicule cible et des données de changement de fonctionnement de véhicule cible; et (3) à utiliser des données de sortie réelles acquises du véhicule cible dans le scénario de test de terrain pour corriger le modèle de base. La présente invention est différente d'un procédé d'apprentissage d'une grande quantité de données pour acquérir un modèle de comportement de conducteur sur la base d'une analyse technologique AI dans l'état de la technique. Un modèle de comportement de conducteur s'approchant du comportement de conducteur réel dans la plus grande mesure est acquis et un algorithme de bout en bout et une solution de bout en bout sont efficacement extraits.
PCT/CN2019/123508 2019-08-26 2019-12-06 Procédé et dispositif de développement de modèle de comportement de conducteur pour conduite automatique et support d'informations WO2021036083A1 (fr)

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