EP3681767A1 - Situation-dependent decision-making for vehicles - Google Patents
Situation-dependent decision-making for vehiclesInfo
- Publication number
- EP3681767A1 EP3681767A1 EP18758846.2A EP18758846A EP3681767A1 EP 3681767 A1 EP3681767 A1 EP 3681767A1 EP 18758846 A EP18758846 A EP 18758846A EP 3681767 A1 EP3681767 A1 EP 3681767A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- vehicle
- damage
- evaluation device
- function
- neural network
- 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.)
- Withdrawn
Links
- 230000001419 dependent effect Effects 0.000 title claims abstract description 13
- 230000006378 damage Effects 0.000 claims abstract description 175
- 230000009471 action Effects 0.000 claims abstract description 92
- 230000006870 function Effects 0.000 claims abstract description 77
- 238000013528 artificial neural network Methods 0.000 claims abstract description 64
- 238000011156 evaluation Methods 0.000 claims abstract description 57
- 208000027418 Wounds and injury Diseases 0.000 claims abstract description 56
- 208000014674 injury Diseases 0.000 claims abstract description 56
- 238000000034 method Methods 0.000 claims abstract description 23
- 238000004590 computer program Methods 0.000 claims abstract description 18
- 230000002787 reinforcement Effects 0.000 claims abstract description 7
- 238000012549 training Methods 0.000 claims abstract description 6
- 230000001133 acceleration Effects 0.000 claims description 10
- 230000004044 response Effects 0.000 claims description 6
- 230000003340 mental effect Effects 0.000 claims description 5
- 230000003014 reinforcing effect Effects 0.000 claims description 5
- 230000035876 healing Effects 0.000 claims 1
- 230000001537 neural effect Effects 0.000 claims 1
- 238000006243 chemical reaction Methods 0.000 description 7
- 238000013527 convolutional neural network Methods 0.000 description 4
- 238000010801 machine learning Methods 0.000 description 4
- 230000004913 activation Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 3
- 210000002569 neuron Anatomy 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 210000004556 brain Anatomy 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000012797 qualification Methods 0.000 description 2
- 241001465754 Metazoa Species 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000000116 mitigating effect Effects 0.000 description 1
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- 230000009897 systematic effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000002604 ultrasonography Methods 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R21/00—Arrangements or fittings on vehicles for protecting or preventing injuries to occupants or pedestrians in case of accidents or other traffic risks
- B60R21/01—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents
- B60R21/013—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents including means for detecting collisions, impending collisions or roll-over
- B60R21/0134—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents including means for detecting collisions, impending collisions or roll-over responsive to imminent contact with an obstacle, e.g. using radar systems
-
- 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
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/09—Taking automatic action to avoid collision, e.g. braking and steering
-
- 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
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/095—Predicting travel path or likelihood of collision
- B60W30/0956—Predicting travel path or likelihood of collision the prediction being responsive to traffic or environmental parameters
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
-
- 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
- B60W2554/00—Input parameters relating to objects
Definitions
- the invention relates to an evaluation device for determining a vehicle action according to claim 1, a computer program product according to claim 14, a method for training an artificial neural network according to claim 17, a system for a vehicle control for situation-dependent decision-making in an accident situation according to claim 19 and a driver assistance system according to claim 22nd
- Prior art vehicles are equipped with a variety of sensors that enable wide coverage of the vehicle environment.
- Known collision reaction systems such as e.g. Emergency Brake Assist and / or Airbag deployment systems attempt to use data from these sensors to determine an action time for a predefined response, such as an emergency stop, an airbag deployment, or an evasive maneuver. These reactions are based on manually predefined scenarios. In complex real situations, however, a large number of reactions at any time is possible, which can not be defined manually without further ado.
- the object of the invention is to provide a system which makes situation-dependent decisions for a vehicle in an accident situation.
- the system should not only perform a predefined reaction, but also be able to consider another solution, such as e.g. to initiate an evasive maneuver at an earlier point in time and thereby completely avoid a collision.
- the evaluation device for determining a vehicle action is designed to feed an artificial neural network whose output is the vehicle action with data from a vehicle environment.
- the artificial neural network is designed to predetermine an accident situation as a function of this data, to evaluate a damage function for personal injury and / or property damage calculated in dependence on simulated vehicle actions, and to determine the vehicle actions for which the result of the damage function in this case Accident situation is minimal.
- An evaluation device is a device that processes incoming information and outputs a result resulting from this processing.
- an evaluation device is an electronic circuit, such as e.g. a central processor unit or a graphics processor.
- a vehicle action is on the one hand a vehicle action, by which an accident is avoided, and on the other a vehicle action, by the accident consequences of an occurred accident can be mitigated.
- Vehicle actions by which an accident can be avoided are, for example, interventions in longitudinal and / or lateral control, e.g. Braking, steering and / or accelerating.
- a vehicle action to mitigate consequences of accidents is e.g. Adjusting a seatbelt with a belt tensioner or triggering an airbag.
- An artificial neural network is an algorithm that is executed on an electronic circuit and programmed on the model of the neural network of the human brain.
- Functional units of an artificial neural network are artificial neurons whose output is generally evaluated as the value of an activation function over a weighted sum of the inputs plus a systematic error, the so-called bias.
- By testing multiple inputs with different weighting factors and / or activation functions artificial neural networks similar to the human brain, trained.
- the training of an artificial neural network using predetermined inputs is called machine learning. Feed forward means a summation and output by the activation function.
- a subset of machine learning is deep learning, in which a series of hierarchical layers of neurons called hidden layers are used to perform the machine learning process.
- An artificial neural network with multiple hidden layers is a deep neural network. Artificial intelligence refers to the purposeful reaction to new information.
- Deep neural networks enable the efficient coding of a complex state space by the arrangement of hidden layers, in which complex reaction models can be coded.
- the artificial neural network is executed on the evaluation device.
- Data are logical quantities and / or physical quantities, e.g. electrical signals.
- a damage function also called a cost or benefit function, is a function that describes what value a given state or action has.
- One of the advantages of the evaluation device according to the invention is that, due to the generic properties and the efficient coding of a complex state space in the artificial neural network, the artificial neural network is able to respond optimally to previously unseen events. Determining the vehicle action for which the result of the damage function in this accident situation is minimal may mean in particular that, for example, a parallel-running car is deliberately rammed in order to avoid a frontal collision. Decisive here is always the value of the damage function. If the value of the damage function for the vehicle action ramming of a parallel-running car is smaller than the value for the vehicle action front-end collision, this becomes Artificial neural network as vehicle action determine to ram the parallel driving car. In contrast, a known collision response system would only be able to respond to the head-on collision with a predefined response, such as emergency braking or deployment of an airbag.
- the artificial neural network is designed to determine the vehicle action with which the accident situation can be avoided.
- the artificial neural network will not necessarily perform a predefined response, e.g. igniting an airbag at the last possible time, but also considering the possibility, e.g. to initiate an evasive maneuver at an earlier point in time and thereby completely avoid an accident situation, in particular a collision.
- the artificial neural network is designed to determine the vehicle action by reinforcing learning.
- Reinforcement Learning also known as Reinforcement Learning
- Reinforcement Learning is a set of machine learning methods in which an agent, here the artificial neural network, independently learns a strategy to maximize received rewards. In doing so, the agent is not shown which action is best in which situation, but receives a reward at certain times, which can also be negative. Based on these rewards, the agent approximates a utility function, here the damage function, which describes what value a particular state or action has.
- Reinforcement Learning implicitly teaches complex physical models and diverse situations and does not need to be defined in advance for each special case.
- the vehicle actions are steering, braking, and / or accelerating the vehicle and / or triggering a collision device, preferably an airbag, or a sequence of the preceding vehicle actions, wherein vehicle action parameter instants at which a respective vehicle action is initiated, and preferably steering angle values , Braking force and / or braking time and / or amount of acceleration and / or acceleration duration are.
- the artificial neural network is not only designed to determine a suitable driving action for a particular accident situation, but also additionally designed to optimally determine the duration or execution of the respective vehicle action.
- the evaluation device is designed to vary the vehicle action parameters, wherein preferably the evaluation device is designed to vary the vehicle action parameters with a random number generator.
- a random number generator is a method that generates a sequence of random numbers.
- the artificial neural network has the opportunity to consider different vehicle actions for a particular accident situation.
- personal injury is more heavily weighted as property damage in the damage function.
- One thing is not a person.
- an animal is one thing.
- the artificial neural network can determine the vehicle action for which minimal personal injury occurs.
- the damage function is independent of personal characteristics, preferably age, gender, physical and / or mental constitution. Thus, any qualification for personal characteristics is prohibited for an inevitable accident situation.
- personal injury and / or property damage are weighted depending on the severity of the damage in the damage function.
- the artificial neural network can determine the vehicle action, which leads to an overall minimum of damage in an unavoidable accident situation.
- property damage is preferably weighted after a loss in value of the respective object.
- the damage function is a function of all property damage in the vehicle environment or a function of the property damage to the vehicle. This allows the definition of two different damage functions.
- the evaluation device is designed to determine the vehicle action for which the number of personal injuries is minimal. A reduction in the number of personal injuries is ethically acceptable. However, an offsetting of victims is not provided.
- the evaluation device for the case that for two specific vehicle actions for which the result of the damage function is the same personal injury carried out to determine the vehicle action with the least damage to the least number of persons involved. This makes it possible to minimize the resulting total damage.
- the computer program product according to the invention is designed to be loaded into the memory of a computer and comprises software code sections with which an accident situation of a vehicle is simulated, personal injury and / or material damage being calculated for this accident situation as a function of vehicle actions, vehicle action parameters and a damage model to keep the damage function dependent on these personal injury and / or property damage when the computer program product is running on a computer, wherein the vehicle actions include steering, braking and / or accelerating the vehicle and / or triggering a collision device, preferably an airbag, or a sequence of the foregoing Vehicle actions are the vehicle action parameter instants at which a respective vehicle action is initiated and preferably values for steering angle, braking force and / or braking duration and / or amount of acceleration and / or acceleration
- the damage model personal injury is more heavily weighted as property damage, personal injury and / or property loss weighted depending on the severity of injury, personal injury following death, consequential injury, curable injury and weighted injury weighted, and material damage weighted after a loss of
- Computer program products typically include a sequence of instructions that cause the hardware, when the program is loaded, to perform a particular procedure that results in a particular result.
- the computer program product causes an effect, namely, obtaining a damage function dependent on personal injury and / or property damage.
- a computer is a device for processing data that processes data using programmable rules.
- a memory is a medium for backing up data.
- Software is a collective term for programs and their associated data.
- the complement to the software is hardware.
- Hardware refers to the mechanical and electronic alignment of a data processing system.
- vehicle behavior as well as damage models for involved persons e.g. Injury level of a person and loss of value of a vehicle and / or infrastructure simulate.
- This simulation can provide a damage function to an artificial neural network.
- the simulation allows implicit learning of complex physical models and the multiple situations of the artificial neural network.
- the artificial neural network is designed to evaluate a damage function obtained with the computer program product according to the invention.
- the artificial neural network can then be trained in particular by reinforcement learning in a comprehensive simulation, in order to force in the case of an imminent accident the best outcome for all involved or to avoid this accident altogether.
- the evaluation device has an input interface in order to obtain data from vehicle surroundings sensors, preferably camera, radar, lidar, infrared and / or ultrasound sensors.
- vehicle surroundings sensors preferably camera, radar, lidar, infrared and / or ultrasound sensors.
- An interface is a device between at least two functional units at which an exchange of logical quantities, eg data, or physical quantities, eg electrical signals, takes place, either only unidirectionally or bidirectionally.
- the exchange can be analog or digital.
- the exchange can also be wired or wireless.
- Current vehicles already have vehicle environment sensors. This makes capturing data about the vehicle environment particularly easy.
- the artificial neural network is executed on an evaluation device.
- the method comprises the following method steps:
- the artificial neural network is executed on an evaluation device for determining a vehicle action.
- an artificial neural network can be trained to perform optimal reactions to non-predefined situations in order to avoid an accident situation.
- an evaluation device is used to carry out the method.
- the system according to the invention for a vehicle control for situation-dependent decision-making in an accident situation has an input interface to obtain data of a vehicle environment. Furthermore, the system has an evaluation device that is designed to forward an artificial neural network trained by reinforcement learning with this data in order to determine the vehicle action for an accident situation, for which the result of a damage function dependent on personal injury and / or material damage this situation is minimal, and to receive a signal for vehicle control in response to this vehicle action. In addition, the system has an output interface configured to output this signal to a vehicle controller.
- a vehicle control device is a device that performs or assumes functions of the longitudinal and / or lateral control of a vehicle.
- the artificial neural network Since the artificial neural network is already trained in enhancing learning, feeding this artificial neural network forward with vehicle environment data provides an end-to-end solution for avoiding or mitigating the consequences of an accident. In particular, the system can respond optimally to unknown situations due to the generic characteristics of the artificial neural network.
- the evaluation device is designed to determine the vehicle action for an imminent accident situation with which the accident situation can be avoided. This can be minimized with the system of total damage.
- the evaluation device can also determine the vehicle action that ensures an exit with minimal overall damage in case of an accident inevitability.
- the artificial neural network of the system is trained according to the method of the invention.
- a driver assistance system comprises a system according to the invention or an artificial neural network trained according to the method according to the invention.
- 1 shows an exemplary embodiment of an evaluation device according to the invention
- 2 shows an embodiment of a computer program product according to the invention
- Fig. 3 shows an embodiment of a method according to the invention
- Fig. 4 shows an embodiment of a system according to the invention.
- FIG. 1 shows an imminent accident situation 32 for a vehicle 22 driving up onto a roadway on a roadway.
- the accident situation 32 is a frontal collision.
- the vehicle 22 is equipped with a camera as the vehicle surroundings sensor 31.
- the camera 31 receives data 30 of the environment of the vehicle 22. In the environment, the camera 31 collects as data the two preceding vehicles.
- the vehicle 22 may also be equipped with a sensor set, for example a camera, radar and lidar sensor, as a vehicle surroundings sensor 31. Such a sensor set uses sensor-fusion of the individual sensors combined their respective advantages.
- the data of the vehicle surroundings sensor 31 are fed via an input interface 12 to an evaluation device 10.
- the evaluation device 10 may be a processor, in particular also a multi-core processor, of a computer.
- the evaluation device 10 is arranged on the vehicle 22. However, it is also within the scope of the invention that the evaluation device 10 is arranged at a central location outside the vehicle 22, wherein the vehicle 22 transmits the data 30 of the vehicle environment to the evaluation device 10 for evaluation and the evaluation device 10 returns the result of the evaluation to the vehicle 22 sends.
- the evaluation device 10 has an artificial neural network 11.
- the artificial neural network 1 1 is a deep neural network with several hidden layers, in which as a complex state space driving the vehicle 22 in a a vehicle environment is coded as a function of a number and an arrangement of hidden layers.
- the artificial neural network 11 may also be a convolutional neural network.
- Convolutional Neural Networks are multilayer artificial neural networks in which each layer contains independent neurons. Convolutional Neural Networks with repetitive layers are called Deep Convolutional Neural Networks.
- the artificial neural network 1 1 evaluates the accident situation 32 for a given damage function 13.
- the damage function 13 is provided by the computer program product 40 shown in FIG.
- the damage function 13 outputs the vehicle action 20, which results in a minimum of the damage function, i. to a minimum overall damage of the accident situation 32, leads.
- the vehicle action 20 is an evasive maneuver, so that a frontal collision does not even occur with the vehicles in front.
- the computer program product 40 in FIG. 2 is loaded into a memory of a computer 41 and executed in this computer 41.
- Software code portions of the computer program product 40 simulate crash situations 32 of a vehicle 22.
- the computer program product 40 calculates vehicle action parameters 21, such as duration of a braking or acceleration event, depending on vehicle actions 20, such as straight-line driving, braking, dodge to the left or right Damage model, personal injury and / or property damage.
- vehicle action parameters 21 such as duration of a braking or acceleration event, depending on vehicle actions 20, such as straight-line driving, braking, dodge to the left or right Damage model, personal injury and / or property damage.
- vehicle action parameters 21 such as duration of a braking or acceleration event, depending on vehicle actions 20, such as straight-line driving, braking, dodge to the left or right Damage model, personal injury and / or property damage.
- vehicle action parameters 21 such as duration of a braking or acceleration event, depending on vehicle actions 20, such as straight-line driving, braking, dodge to the left or right Damage model, personal injury and
- the damage function is independent of personal characteristics, preferably age, gender, physical and / or mental constitution and the damage function is a function of all property damage in the vehicle environment or a function of property damage to the vehicle.
- the damage severity score corresponds to one Loss of value of the vehicle, of other vehicles or of an object of the infrastructure, eg of a building.
- data of a vehicle environment are provided to an artificial neural network that is executed on an evaluation device.
- an accident situation is predetermined.
- the artificial neural network 11 learns to evaluate a damage function 30 of personal injury and / or property damage calculated for simulated vehicle actions 20 and to determine the vehicle action 20 for which the result of the damage function in this accident situation 32 is minimal ,
- the artificial neural network 11 can observe the environment of the vehicle 22 and be rewarded for correspondingly performed actions. After the artificial neural network 1 1 has determined the vehicle action 22 with minimum damage function 13, the environment of the vehicle 22 is re-observed by providing data of the vehicle environment.
- the system 50 has an input interface 51, via which the system 50 data of a vehicle surroundings sensor 31 are supplied.
- the system 50 includes an evaluator 10 that forwards an amplified learning artificial neural network with this data to determine, for an accident situation 32, the vehicle action 20 for which the result of a personal injury and / or property damage function 13 therein Accident situation is minimal.
- a signal for a vehicle control is obtained. Via an output interface 52, this signal is output to a vehicle control device 53.
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Automation & Control Theory (AREA)
- Transportation (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
- Automotive Seat Belt Assembly (AREA)
Abstract
Description
Claims
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE102017216061.6A DE102017216061A1 (en) | 2017-09-12 | 2017-09-12 | Situation-based decision-making for vehicles |
PCT/EP2018/072055 WO2019052762A1 (en) | 2017-09-12 | 2018-08-14 | Situation-dependent decision-making for vehicles |
Publications (1)
Publication Number | Publication Date |
---|---|
EP3681767A1 true EP3681767A1 (en) | 2020-07-22 |
Family
ID=63311998
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP18758846.2A Withdrawn EP3681767A1 (en) | 2017-09-12 | 2018-08-14 | Situation-dependent decision-making for vehicles |
Country Status (5)
Country | Link |
---|---|
US (1) | US20190382006A1 (en) |
EP (1) | EP3681767A1 (en) |
CN (1) | CN110382303A (en) |
DE (1) | DE102017216061A1 (en) |
WO (1) | WO2019052762A1 (en) |
Families Citing this family (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE102021203354B4 (en) | 2021-04-01 | 2022-11-10 | Volkswagen Aktiengesellschaft | Method for determining a future accident severity of a motor vehicle with an object using an assistance system of the motor vehicle, computer program product and assistance system |
CN113799799A (en) * | 2021-09-30 | 2021-12-17 | 中国第一汽车股份有限公司 | Security compensation method and device, storage medium and electronic equipment |
US20230256999A1 (en) * | 2022-02-17 | 2023-08-17 | Gm Cruise Holdings Llc | Simulation of imminent crash to minimize damage involving an autonomous vehicle |
Family Cites Families (15)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH0785280B2 (en) * | 1992-08-04 | 1995-09-13 | タカタ株式会社 | Collision prediction judgment system by neural network |
DE10036276A1 (en) * | 2000-07-26 | 2002-02-07 | Daimler Chrysler Ag | Automatic braking and steering system for a vehicle |
DE10149206A1 (en) * | 2000-10-04 | 2003-02-06 | Intelligent Tech Int Inc | Method and device for mapping a road and accident prevention system |
DE10328062A1 (en) * | 2003-06-23 | 2005-01-20 | Robert Bosch Gmbh | Method for improving the safety of road users involved in a prematurely recognized accident |
US8594370B2 (en) * | 2004-07-26 | 2013-11-26 | Automotive Systems Laboratory, Inc. | Vulnerable road user protection system |
WO2006070865A1 (en) * | 2004-12-28 | 2006-07-06 | Kabushiki Kaisha Toyota Chuo Kenkyusho | Vehicle motion control device |
DE102006031238B4 (en) * | 2006-07-06 | 2016-07-21 | Robert Bosch Gmbh | Device and method for controlling personal protection devices |
US20130158809A1 (en) * | 2011-12-15 | 2013-06-20 | Ford Global Technologies, Llc | Method and system for estimating real-time vehicle crash parameters |
DE102013221282B4 (en) * | 2013-10-21 | 2024-05-29 | Volkswagen Aktiengesellschaft | Method and device for determining at least one area-specific intrusion parameter |
CN104691490A (en) * | 2013-12-06 | 2015-06-10 | 大连东浦机电有限公司 | Automotive external airbag and starting system with surrounding pedestrian behavior prediction function |
US9505405B2 (en) * | 2015-01-16 | 2016-11-29 | Ford Global Technologies, Llc | Rear collision avoidance and mitigation system |
KR20170028126A (en) * | 2015-09-03 | 2017-03-13 | 엘지전자 주식회사 | Driver assistance apparatus for vehicle and Vehicle |
DE102016000493B4 (en) * | 2016-01-19 | 2017-10-19 | Audi Ag | Method for operating a vehicle system and motor vehicle |
DE102016005230A1 (en) * | 2016-04-29 | 2017-02-09 | Daimler Ag | Method for operating a vehicle |
CN106553655B (en) * | 2016-12-02 | 2019-11-15 | 深圳地平线机器人科技有限公司 | Hazardous vehicles detection method and system and vehicle including the system |
-
2017
- 2017-09-12 DE DE102017216061.6A patent/DE102017216061A1/en not_active Ceased
-
2018
- 2018-08-14 WO PCT/EP2018/072055 patent/WO2019052762A1/en unknown
- 2018-08-14 EP EP18758846.2A patent/EP3681767A1/en not_active Withdrawn
- 2018-08-14 US US16/489,650 patent/US20190382006A1/en not_active Abandoned
- 2018-08-14 CN CN201880016814.0A patent/CN110382303A/en active Pending
Also Published As
Publication number | Publication date |
---|---|
US20190382006A1 (en) | 2019-12-19 |
WO2019052762A1 (en) | 2019-03-21 |
CN110382303A (en) | 2019-10-25 |
DE102017216061A1 (en) | 2019-03-14 |
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