CN114148321A - Automatic emergency braking and safety evaluation method considering collision risk - Google Patents

Automatic emergency braking and safety evaluation method considering collision risk Download PDF

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CN114148321A
CN114148321A CN202111387055.9A CN202111387055A CN114148321A CN 114148321 A CN114148321 A CN 114148321A CN 202111387055 A CN202111387055 A CN 202111387055A CN 114148321 A CN114148321 A CN 114148321A
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vehicle
early warning
decision
braking
driver
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CN114148321B (en
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魏田正
李思雨
刘小龙
朱彤
刘浩学
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Changan University
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Changan University
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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
    • 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, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
    • 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
    • B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
    • B60Q9/00—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling
    • B60Q9/008—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling for anti-collision purposes
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
    • B60T7/00—Brake-action initiating means
    • B60T7/12—Brake-action initiating means for automatic initiation; for initiation not subject to will of driver or passenger
    • B60T7/22—Brake-action initiating means for automatic initiation; for initiation not subject to will of driver or passenger initiated by contact of vehicle, e.g. bumper, with an external object, e.g. another vehicle, or by means of contactless obstacle detectors mounted on the vehicle
    • 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
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/08—Interaction between the driver and the control system
    • B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
    • 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
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/08—Interaction between the driver and the control system
    • B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
    • B60W2050/143—Alarm means

Abstract

The invention relates to the technical field of road traffic safety, in particular to an automatic emergency braking and safety evaluation method considering collision risks. According to the automatic emergency braking and safety evaluation method considering the collision risk, the vehicle collision risk, the relative distance of the vehicle and the collision time are comprehensively considered on the premise of considering the individualized reaction time of the driver, the risk driving behavior of the driver is timely pre-warned, the driver is reminded to take danger avoidance measures as soon as possible, forced braking is carried out at the emergency moment, and the personal safety of the driver is protected. Meanwhile, the influence of the road adhesion coefficient and the road gradient on the maximum braking deceleration of the vehicle is considered, the method can better adapt to the change of different road conditions, the early warning and autonomous braking time is more accurate, and the reliability of the system is improved. On the other hand, the risk driving behavior and the risk avoiding capability of the driver can be evaluated, the emergency risk avoiding capability of the driver can be known, and the targeted training can be carried out.

Description

Automatic emergency braking and safety evaluation method considering collision risk
Technical Field
The invention relates to the technical field of road traffic safety, in particular to an automatic emergency braking and safety evaluation method considering collision risks.
Background
With the gradual increase of the automobile holding capacity, the driving trip becomes a trip mode generally selected by people more and more, and in a complex road traffic system, the risk driving behavior of a driver has great influence on traffic safety. The automatic emergency braking system (AEB) is an active safety technology, can monitor the driving environment in front of a vehicle in real time, and automatically starts the vehicle braking system to decelerate the vehicle when collision danger possibly occurs so as to avoid collision or reduce the collision. However, most of the conventional automatic emergency braking systems are built based on a safe vehicle distance or a time parameter algorithm, and the two methods are combined and applied less frequently, and a preset fixed value is adopted for calculating braking deceleration, so that the influence of a road adhesion coefficient and a road gradient on the braking efficiency of the vehicle is considered less. The method can not adapt to the change of road conditions well, and early warning or late warning can occur at the early warning moment based on the calculation of system parameters, so that the reliability of the system is influenced.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, it is an object of the present invention to provide an automatic emergency braking and safety evaluation method considering the risk of collision.
The purpose of the invention can be realized by the following technical scheme:
an automatic emergency braking and safety evaluation method considering a collision risk specifically comprises the following steps: the system comprises a vehicle running state acquisition device, a road environment state acquisition device, a control decision device, an information storage device, an early warning device and a brake control device. The vehicle running state acquisition device is mainly used for acquiring the current speed of the vehicle, the deceleration of the vehicle, the distance between the vehicle and a preceding vehicle and the relative speed data of the vehicle and the preceding vehicle. The road environment state acquisition device is mainly used for acquiring road pavement adhesion coefficients and road gradients. The control decision device is mainly used for receiving vehicle state and road state data transmitted by the vehicle running state acquisition device and the road environment state acquisition device, extracting data information in the information storage device, making a decision based on an automatic emergency braking algorithm and a vehicle collision risk prediction model, and transmitting the decision information to the early warning device, the information storage device and the braking control device of the self vehicle. The information storage device is mainly used for storing data information of the triggering system event so as to be extracted and used by a control decision device or a technician. The early warning device is mainly used for receiving decision information for controlling the decision device and adopting a corresponding early warning reminding mode to remind a driver of paying attention to vehicle speed control or avoiding danger in time. The brake control device is connected with the vehicle brake control system and used for receiving decision information of the control decision device and deciding whether to adopt an active forced braking measure.
Further, the road environment state acquisition device transmits the acquired road surface adhesion coefficient and the road gradient data to the control decision device, and the control decision device calculates the maximum braking deceleration which can be adopted by the vehicle on the current running road according to the following formula according to the road surface adhesion coefficient and the road gradient data:
amax＝g(sinθ+μcosθ)
wherein, amaxThe maximum braking deceleration of the self vehicle is g, the gravity acceleration is g, the road gradient is theta, and the road adhesion coefficient is mu.
The vehicle running state acquisition device acquires the current speed and deceleration of the vehicle, the distance between the vehicle and a preceding vehicle and the relative speed data of the vehicle and the preceding vehicle and transmits the data to the control decision device, the control decision device receives the data transmitted by the road environment state acquisition device and the vehicle running state acquisition device, and the time for starting the vehicle to early warn and starting the forced braking intervention to avoid danger is calculated by the following method:
the control decision device obtains the maximum braking deceleration which can be adopted by the current vehicle running road based on the road environment state parameters, meanwhile, the control decision device receives vehicle running parameter data, and calculates and obtains an early warning critical distance threshold value between a front vehicle and a self vehicle based on the following formula, when the relative distance between the self vehicle and the front vehicle is smaller than or equal to the calculated early warning critical distance threshold value, the control decision device makes an early warning decision and transmits the early warning decision to the early warning device, and the early warning device receives early warning decision information and makes corresponding early warning prompt for a vehicle driver. The front vehicle is a front vehicle which runs on the same lane with the own vehicle and has the shortest relative distance with the own vehicle. The early warning critical distance threshold value calculation formula is as follows:
Figure BDA0003367457930000031
d is the early warning critical distance between the front vehicle and the self vehicle, v is the current speed of the self vehicle, and tau is T + Tr，trA brake delay time; t is the reaction time of the driver, and T is 0.8+ x, wherein x is more than 0, and is a reaction time adjusting parameter used for adjusting the reaction time of the driver in seconds. v. ofrelThe relative speed between the self vehicle and the front vehicle; d0The minimum safe distance between two vehicles after the vehicles stop. According to the relative distance between the vehicle and the front vehicle and the relative speed, when the vehicle speed of the vehicle is greater than that of the front vehicle, the control decision device calculates a TTC value based on the vehicle running state parameter, when the TTC value is not greater than a certain threshold value, the control decision device also makes an early warning decision and transmits the early warning decision to the early warning device, and the early warning device receives early warning decision information and makes corresponding early warning prompt for a vehicle driver. The TTC early warning critical value calculation method is as follows:
Figure BDA0003367457930000032
wherein d isrelIs the relative distance between the bicycle and the front bicycle. Construction based on vehicle speed, relative distance between two vehicles and driving by using binary logistic regression methodA vehicle collision risk prediction model of vehicle occupant braking deceleration. And predicting the vehicle speed of the vehicle with the collision accident based on the vehicle collision risk prediction model and the relative distance of the vehicle, and comparing the vehicle speed with the current vehicle speed of the vehicle. And when the predicted speed is less than or equal to the current speed of the vehicle, the control decision device controls the early warning device to remind a driver to take speed reduction measures in time.
Early warning critical distance threshold value d based on distance and early warning critical value T based on timebrAnd predicting the vehicle speed of the vehicle collision risk. The output results of the three early warning calculation modes are classified variables respectively, and m represents the accumulation sum of decision results of the three early warning modes. And the final output result of the control decision device is a multi-element classification result, the respective discrimination results are coupled and applied to the early warning decision of the early warning system based on three early warning discrimination modes, and the early warning decision result is transmitted to the early warning device, and the early warning device carries out corresponding early warning operation based on a final early warning decision instruction. The specific implementation of the final early warning decision is as follows:
Figure BDA0003367457930000041
when the self vehicle continues to approach the front vehicle, the control decision device calculates the braking critical distance threshold value of the system based on the following formula, dbr1 and dbr2 are respectively calculated and compared with the current distance of the vehicle, and the braking critical distance dbrThe calculation method is as follows:
dbr1＝vrelτ+0.5amaxτ2
dbr2＝TTC*vrel+d0
the output result of the calculation of the braking critical distance is a binary variable 0 or 1, and respectively, when the relative distance between the self vehicle and the front vehicle is less than or equal to the calculated braking critical distance, the output result is 1; and when the relative distance between the self vehicle and the front vehicle is greater than the calculated braking critical distance, the output result is 0, the judgment result is coupled and applied to the forced braking decision of the system based on the two judgment modes, and s represents the addition sum of the two judgment output results. The final braking decision output signal is as follows:
Figure BDA0003367457930000042
in the initial stage of the system, an initial vehicle collision risk prediction model can be constructed by adopting a rear-end collision case based on a deep accident investigation database. In the actual driving process of the vehicle, when the system triggering event occurs, the control decision device stores the vehicle speed at the early warning moment, the relative distance between the vehicle and the front vehicle, the maximum braking deceleration taken by a driver before the system is forcibly intervened, whether the system is forcibly braked and whether the collision result is taken as event data in the data storage device. Whether a collision result can be judged according to the TTC value, when the TTC value is less than or equal to 1.5s, the collision result is recorded as a vehicle collision accident, and in the actual running process of the vehicle, after a certain amount of new observed values are accumulated, the system automatically updates a collision risk prediction model.
The specific early warning and braking implementation mode of the system is as follows:
(1) when f (x) is 0, the system does not give out early warning and brake operation and does not interfere the driving behavior of the driver and the running state of the vehicle;
(2) when f (x) is 1, the early warning device is triggered and activated to remind a driver of paying attention to control the vehicle speed, so that rear-end accidents are prevented;
(3) when f (x) is 2, the early warning device is triggered to be activated and reminds the driver of carrying out emergency deceleration operation.
(4) When g (x) is 0, the system does not perform forced braking intervention, and when g (x) is 1, the control decision device makes a forced braking decision and transmits the braking decision to the braking control device, and the braking control device receives the braking decision information and performs forced braking on the vehicle.
Further, the brake control device counts the system trigger frequency of the driver in the daily driving process, and after the driver triggers the early warning function, whether the system is triggered to perform forced intervention on the vehicle follows Beta (a, b) distribution or not is assumed, wherein the parameter a represents the cumulative number of times that the vehicle triggers the early warning of the system but does not trigger the forced braking intervention, and the parameter b represents the cumulative number of times that the vehicle triggers the forced braking intervention. When a new system triggering event occurs, the prior information of whether a vehicle braking intervention measure is triggered after the system triggers the early warning function is updated by a new observed value after the system triggers the event. The Beta distribution and the binomial distribution are conjugate prior distributions. Therefore, the posterior distribution is also a Beta (c, d) distribution, where c is a + z, d is b +1-z, and z is an observed value of whether the system takes braking intervention when the system is triggered, if the system takes braking intervention, z is 0, and if the system does not trigger forced intervention, z is 1, so that the probability of the driver responding to the system early warning can be estimated.
Furthermore, the control decision device can be combined with the event data records in the data storage device and the driver collision risk prediction model, the vehicle speed, the relative distance between two vehicles and the maximum braking deceleration data of the driver when the system is triggered to perform statistical analysis on the driver early warning response probability, a driving risk analysis report is output, the vehicle following risk avoiding driving behavior habit and the risk driving behavior of the driver are parameterized, and the comprehensive understanding of the risk avoiding behavior of the driver when the system is triggered is facilitated. Optionally, the analysis of various data collected when the system is triggered can be realized by controlling the decision device based on a statistical manner, and the output end of the control decision device is connected with the vehicle-mounted display. And constructing a visual driving risk assessment result display platform. And when the emergency response probability of the system and the triggering of the system are carried out, the historical driving behavior parameters of the driver are statistically and visually displayed, so that the comprehensive evaluation of the risk avoiding driving capability of the driver is facilitated.
Compared with the prior art, the invention has the beneficial effects that:
according to the automatic emergency braking and safety evaluation method considering the collision risk, the vehicle collision risk, the relative distance of the vehicle and the collision time are comprehensively considered on the premise of considering the individualized reaction time of the driver, the risk driving behavior of the driver is timely pre-warned, the driver is reminded to take danger avoidance measures as soon as possible, forced braking is carried out at the emergency moment, and the personal safety of the driver is protected. Meanwhile, the influence of the road adhesion coefficient and the road gradient on the maximum braking deceleration of the vehicle is considered, the method can better adapt to the change of different road conditions, the early warning and autonomous braking time is more accurate, and the reliability of the system is improved. On the other hand, based on Bayesian distribution and in combination with the risk running state of the vehicle when the system is triggered, the risk driving behavior and risk avoiding capability of the driver are evaluated, so that the driver can know the emergency risk avoiding capability and conduct targeted training.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without creative efforts;
FIG. 1 is a schematic flow diagram of an automatic emergency braking system of the present invention;
FIG. 2 is a schematic flow chart of the security evaluation method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides an automatic emergency braking and safety evaluation method considering collision risks, which specifically comprises the following steps: the system comprises a vehicle running state acquisition device, a road environment state acquisition device, a control decision device, an information storage device, an early warning device and a brake control device. The vehicle running state acquisition device and the road environment state acquisition device transmit detected data information to the control decision device, the control decision device receives vehicle running state and road environment state data, makes a control decision based on the automatic emergency braking system considering collision risk, and transmits the decision information to the information storage device, the early warning device and the braking control device, the information storage device receives the decision information of the control decision device to write or read corresponding data, the early warning device receives the decision information of the control decision device to perform corresponding early warning reminding on a driver, and the braking control device is connected with the vehicle braking control system and used for receiving the decision information of the control decision device to perform forced braking on a vehicle.
Specifically, the vehicle running state acquisition device is mainly used for acquiring the current speed of the vehicle, the deceleration of the vehicle, the distance between the vehicle and the front vehicle and the relative speed data of the vehicle and the front vehicle.
The vehicle running state acquisition device can select a speed sensor, an acceleration sensor, a distance measuring sensor and the like.
The road environment state acquisition device is mainly used for acquiring road pavement adhesion coefficients and road gradients.
The road surface adhesion coefficient can be estimated using a tire road surface adhesion coefficient estimation model or an Effect-based estimation algorithm proposed by kyangsu, et al, hanyang university, korea. The road slope θ may be measured using a VN-100 attitude sensor.
The control decision device is mainly used for receiving data transmitted by the vehicle running state acquisition device and the road environment state acquisition device, extracting data information in the information storage device, making a corresponding decision based on an automatic emergency braking algorithm and a vehicle collision risk prediction model calculation result, and transmitting the decision information to the early warning device, the information storage device and the braking control device of the self vehicle. The control decision device can be an 8051 singlechip.
The information storage device is mainly used for storing data information of the triggering system event so as to be extracted and used by a control decision device or a technician. The information storage device may be a disk or memory that the decision-making device can read and write data.
The early warning device is mainly used for receiving decision information for controlling the decision device and adopting a corresponding early warning reminding mode. The early warning device can be a loudspeaker.
The brake control device is connected with the vehicle brake control system and used for receiving decision information of the control decision device and deciding whether to take brake measures or not.
Further, the road environment state acquisition device transmits the acquired road surface adhesion coefficient and the road gradient data to the control decision device, and the control decision device calculates the maximum braking deceleration which can be adopted by the vehicle on the current running road according to the following formula according to the road surface adhesion coefficient and the road gradient data:
amax＝g(sinθ+μcosθ)
wherein, amaxThe maximum braking deceleration of the self vehicle is g, the gravity acceleration is g, the road gradient is theta, and the road adhesion coefficient is mu.
The vehicle running state acquisition device acquires the current speed and deceleration of the vehicle, the distance between the vehicle and a preceding vehicle and the relative speed data of the vehicle and the preceding vehicle and transmits the data to the control decision device, the control decision device receives the data transmitted by the road environment state acquisition device and the vehicle running state acquisition device, and the time for starting the vehicle to early warn and starting the forced braking intervention to avoid danger is calculated by the following method:
the control decision device obtains the maximum braking deceleration which can be adopted by the current vehicle running road based on the road environment state parameters, meanwhile, the control decision device receives vehicle running parameter data, and calculates and obtains an early warning critical distance threshold value between a front vehicle and a self vehicle based on the following formula, when the relative distance between the self vehicle and the front vehicle is smaller than or equal to the calculated early warning critical distance threshold value, the control decision device makes an early warning decision and transmits the early warning decision to the early warning device, and the early warning device receives early warning decision information and makes corresponding early warning prompt for a vehicle driver. The front vehicle is a front vehicle which runs on the same lane with the own vehicle and has the shortest relative distance with the own vehicle. The early warning critical distance threshold value calculation formula is as follows:
Figure BDA0003367457930000091
wherein d is the front vehicle andthe early warning critical distance between the vehicles, v is the current speed of the vehicles, and tau is T + Tr，trA brake delay time; t is the reaction time of the driver in seconds. Specifically, the reaction time of the driver in a dangerous scene may be measured based on a simulated driving device or the like before the driver performs real-vehicle driving. So that the final value of T is the measured driver reaction time. v. ofrelThe relative speed between the self vehicle and the front vehicle; d0The minimum safe distance between two vehicles after the vehicles stop can be 5 m. According to the relative distance between the vehicle and the front vehicle and the relative speed, when the vehicle speed of the vehicle is greater than the vehicle speed of the front vehicle, the control decision device calculates the TTC value based on the vehicle running state parameter, and when the TTC value is greater than the early warning critical value TbrWhen the signal is input, the decision device is controlled to output a 0 signal; when the TTC value is not greater than the early warning critical value TbrAnd controlling the decision device to output a 1 signal. TTC and early warning threshold value TbrThe calculation method is as follows:
Figure BDA0003367457930000092
Figure BDA0003367457930000093
wherein d isrelAnd u is a correction coefficient.
Based on the rear-end collision accident data set and the running historical data of the vehicle, a vehicle collision risk prediction model based on the vehicle speed, the relative distance between the two vehicles and the braking deceleration of a driver is constructed by using a binary logistic regression method. And predicting the vehicle speed of the vehicle with the collision accident based on the vehicle collision risk prediction model and the relative distance of the vehicle, and comparing the vehicle speed with the current vehicle speed of the vehicle. When the predicted speed is greater than the current speed of the vehicle, controlling the decision device to control and output a 0 signal; and when the predicted vehicle speed is less than or equal to the current vehicle speed of the vehicle, controlling the decision device to control and output a 1 signal. In the initial stage of the system, a vehicle collision risk prediction model can be constructed based on a rear-end collision accident case in a deep accident investigation database, the model is input into a control decision device, and the vehicle speed of the vehicle in a collision accident is predicted based on the vehicle collision risk prediction model and the relative distance of the vehicle and the maximum deceleration of a driver.
Early warning critical distance threshold value d based on distance and early warning critical value T based on timebrAnd predicting the vehicle speed of the vehicle collision risk. The output results of the three early warning calculation modes are classified variables respectively, and m represents the accumulation sum of decision results of the three early warning modes. And the final output result of the control decision device is a multi-element classification result, the respective discrimination results are coupled and applied to the early warning decision of the early warning system based on three early warning discrimination modes, and the early warning decision result is transmitted to the early warning device, and the early warning device carries out corresponding early warning operation based on a final early warning decision instruction. The specific implementation of the final early warning decision is as follows:
Figure BDA0003367457930000101
the specific early warning implementation mode of the system is as follows:
(4) when f (x) is 0, the system does not give an early warning and brake operation and does not interfere with the driving behavior of the driver and the running state of the vehicle.
(5) When f (x) is 1, the early warning device is triggered and activated to remind a driver of paying attention to control the vehicle speed, so that the rear-end collision is prevented.
(6) When f (x) is 2, the early warning device is triggered to be activated and reminds the driver of carrying out emergency deceleration operation.
When the self vehicle continues to approach the front vehicle, the control decision device calculates the braking critical distance threshold value of the system based on the following formula, dbr1 and dbr2 are respectively calculated and compared with the current distance of the vehicle, and the braking critical distance dbrThe calculation method is as follows:
dbr1＝vrelτ+0.5amaxτ2
dbr2＝TTC*vrel+d0
the output result of the calculation of the braking critical distance is a binary variable 0 or 1, and respectively, when the relative distance between the self vehicle and the front vehicle is less than or equal to the calculated braking critical distance, the output result is 1; and when the relative distance between the self vehicle and the front vehicle is greater than the calculated braking critical distance, the output result is 0, the judgment result is coupled and applied to the forced braking decision of the system based on the two judgment modes, and s represents the addition sum of the two judgment output results. The final braking decision embodiment is as follows:
Figure BDA0003367457930000111
when g (x) is 0, the system does not perform forced braking intervention, and when g (x) is 1, the control decision device makes a forced braking decision and transmits the braking decision to the braking control device, and the braking control device receives the braking decision information and performs forced braking on the vehicle.
In the actual driving process of the vehicle, when the system triggering event occurs, the control decision device stores the vehicle speed at the early warning moment, the relative distance between the vehicle and the preceding vehicle, the relative vehicle speed, the maximum braking deceleration taken by the driver before the system is forcibly intervened, whether the system is forcibly braked and whether the collision result is taken as event data in the data storage device. Whether a collision result can be judged according to the TTC value, when the TTC value is less than or equal to 1.5s, the collision result is recorded as a vehicle collision accident, and in the actual running process of the vehicle, after a certain amount of new observed values are accumulated, the system automatically updates a collision risk prediction model.
Further, the brake control device counts the system trigger frequency of the driver in the daily driving process, and after the driver triggers the early warning function, whether the system is triggered to perform forced intervention on the vehicle follows Beta (a, b) distribution or not is assumed, wherein the parameter a represents the cumulative number of times that the vehicle triggers the early warning of the system but does not trigger the forced braking intervention, and the parameter b represents the cumulative number of times that the vehicle triggers the forced braking intervention. When a new system triggering event occurs, the prior information of whether a vehicle braking intervention measure is triggered after the system triggers the early warning function is updated by a new observed value after the system triggers the event. The Beta distribution and the binomial distribution are conjugate prior distributions. Therefore, the posterior distribution is also a Beta (c, d) distribution, where c is a + z, d is b +1-z, and z is an observed value of whether the system takes braking intervention when the system is triggered, if the system takes braking intervention, z is 0, and if the system does not trigger forced intervention, z is 1, so that the probability distribution of the driver's early warning response to the system can be estimated.
Furthermore, the control decision device can be combined with the event data records in the data storage device and the driver collision risk prediction model, the vehicle speed, the relative distance between two vehicles and the maximum braking deceleration data of the driver when the system is triggered to perform statistical analysis on the driver early warning response probability, a driving risk analysis report is output, the following and risk avoiding driving behavior habit and the risk driving behavior of the driver are subjected to parameterization processing, and the comprehensive understanding of the risk avoiding behavior of the driver when the system is triggered is facilitated. Optionally, the analysis of various data collected when the system is triggered can be realized by controlling the decision device based on a statistical manner, and the output end of the control decision device is connected with the vehicle-mounted display. And constructing a visual driving risk assessment result display platform. And when the emergency response probability of the system and the triggering of the system are carried out, the historical driving behavior parameters of the driver are statistically and visually displayed, so that the comprehensive evaluation of the risk avoiding driving behavior and the emergency handling capability of the driver is facilitated.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.

Claims (8)

1. An automatic emergency braking system taking into account the risk of collision, comprising: the system comprises a vehicle running state acquisition device, a road environment state acquisition device, a control decision device, an information storage device, an early warning device and a brake control device, and is characterized in that the vehicle running state acquisition device selects a speed measurement sensor, an acceleration sensor and a distance measurement sensor and is mainly used for acquiring the current speed of a vehicle, the deceleration of the vehicle, the distance between the vehicle and a preceding workshop and the relative speed data of the vehicle and the preceding vehicle; the road environment state acquisition device is mainly used for acquiring road pavement adhesion coefficients and road gradients; the control decision device selects a single chip microcomputer and controls the automatic emergency braking system through a vehicle collision risk prediction model; the information storage device is a memory which can read and write data; the early warning device selects a loudspeaker; the brake control device is connected with a vehicle brake control system.
2. An automatic emergency braking system taking into account the risk of collision according to claim 1, the vehicle running state acquisition device and the road environment state acquisition device transmit the detected data information to the control decision device, the control decision device receives the vehicle running state and the road environment state data, and making a control decision based on the automatic emergency braking system considering the collision risk, transmitting the decision information to an information storage device, an early warning device and a braking control device, wherein the information storage device receives the decision information of the control decision device to write or read corresponding data, the early warning device receives the decision information of the control decision device to perform corresponding early warning reminding on a driver, and the braking control device is connected with a vehicle braking control system and used for receiving the decision information of the control decision device to perform forced braking on the own vehicle.
3. The automatic emergency braking system considering the collision risk according to claim 1, wherein the control decision device is mainly used for receiving data transmitted by the vehicle running state acquisition device and the road environment state acquisition device, extracting data information in the information storage device to make a corresponding decision based on an automatic emergency braking algorithm and a vehicle collision risk prediction model calculation result, and transmitting decision information to the early warning device, the information storage device and the braking control device of the own vehicle;
the information storage device is mainly used for storing data information of the trigger system event so as to be extracted and used by a control decision device or a technician;
the early warning device is mainly used for receiving decision information for controlling the decision device and adopting a corresponding early warning reminding mode;
the braking control device is used for receiving the decision information of the control decision device and deciding whether to take braking measures or not.
4. An automatic emergency braking system taking into account the risk of collision according to claim 1, characterized in that the control decision means calculates the maximum braking deceleration that can be assumed on the road on which the vehicle is currently travelling, from the road adhesion coefficient and the road gradient data according to the following formula:
amax＝g(sinθ+μcosθ)
wherein, amaxThe maximum braking deceleration of the self vehicle is g, the gravity acceleration is g, the road gradient is theta, and the road adhesion coefficient is mu.
5. An automatic emergency braking system taking into account risk of collision according to claim 1, characterized in that the control decision means calculates the instant at which the own vehicle starts early warning and starts forced braking intervention risk avoidance by:
the control decision device obtains the maximum braking deceleration which can be taken by the current vehicle running road based on the road environment state parameters, meanwhile, the control decision device receives the vehicle running parameter data, and calculates and obtains an early warning critical distance threshold value between a front vehicle and the front vehicle based on the following formula, when the relative distance between the front vehicle and the front vehicle is smaller than or equal to the calculated early warning critical distance threshold value, the control decision device makes an early warning decision and transmits the early warning decision to the early warning device, the early warning device receives early warning decision information and makes corresponding early warning prompt for a vehicle driver, wherein the front vehicle is a front vehicle which runs on the same lane with the front vehicle and has the shortest relative distance with the front vehicle, and the early warning critical distance threshold value calculation formula is as follows:
Figure FDA0003367457920000021
d is the early warning critical distance between the front vehicle and the self vehicle, v is the current speed of the self vehicle, and tau is T + Tr，trA brake delay time; t is the reaction time of the driver, the unit is second, specifically, the reaction time of the driver in a dangerous scene can be measured based on simulated driving equipment and the like before the driver drives the real vehicle, so that the final value of T is the reaction time of the measured driver, vrelThe relative speed between the self vehicle and the front vehicle; d0The minimum safety distance between two vehicles after the vehicle stops can be set to be 5m, according to the relative distance between the vehicle and the front vehicle and the relative speed, when the vehicle speed of the vehicle is larger than that of the front vehicle, the control decision device calculates the TTC value based on the vehicle running state parameter, and when the TTC value is larger than the early warning critical value TbrWhen the signal is input, the decision device is controlled to output a 0 signal; when the TTC value is not greater than the early warning critical value TbrAnd controlling the decision device to output a 1 signal.
6. An automatic emergency braking system with collision risk consideration according to claim 5, wherein the TTC and the warning threshold TbrThe calculation method is as follows:
Figure FDA0003367457920000031
Figure FDA0003367457920000032
wherein d isrelAnd u is a correction coefficient.
7. The automatic emergency braking system considering the collision risk according to claim 5, wherein a vehicle collision risk prediction model is arranged in the control decision device, the vehicle collision risk prediction model is constructed by using a binary logistic regression method based on a rear-end collision accident data set and self vehicle operation historical data, the vehicle speed of the vehicle in a collision accident is predicted based on the vehicle collision risk prediction model and the vehicle relative distance, the vehicle speed is compared with the current vehicle speed of the self vehicle, and when the predicted vehicle speed is greater than the current vehicle speed of the self vehicle, the control decision device controls to output a 0 signal; when the predicted speed is less than or equal to the current speed of the vehicle, the control decision device controls and outputs a signal 1, a vehicle collision risk prediction model can be constructed based on a rear-end accident case in a deep accident investigation database in the initial stage of the system, the model is input into the control decision device, the vehicle speed of the vehicle in a collision accident is predicted based on the vehicle collision risk prediction model and the relative distance of the vehicle and the maximum deceleration of a driver,
early warning critical distance threshold value d based on distance and early warning critical value T based on timebrAnd predicting the vehicle speed by the vehicle collision risk, wherein output results of the three early warning calculation modes are respectively binary variables, m represents the sum of decision results of the three early warning modes, the final output result of the decision device is controlled to be a multi-element classification result, the respective decision results are coupled and applied to early warning decision of an early warning system based on the three early warning decision modes, and the early warning decision result is transmitted to an early warning device, and the early warning device carries out corresponding early warning operation based on a final early warning decision instruction.
8. The safety evaluation method of the automatic emergency braking system considering the collision risk according to any one of claims 1 to 7, wherein the evaluation method implemented by the specific early warning of the system is as follows:
Figure FDA0003367457920000041
the specific early warning implementation mode of the system is as follows:
(1) when f (x) is 0, the system does not give out early warning and brake operation and does not interfere the driving behavior of the driver and the running state of the vehicle;
(2) when f (x) is 1, the early warning device is triggered and activated to remind a driver of paying attention to control the vehicle speed, so that rear-end accidents are prevented;
(3) when f (x) is 2, the early warning device is triggered and activated and reminds the driver of carrying out emergency deceleration operation;
when the self vehicle continues to approach the front vehicle, the control decision device calculates the braking critical distance threshold value of the system based on the following formula, dbr1 and dbr2 are respectively calculated and compared with the current distance of the vehicle, and the braking critical distance dbrThe calculation method is as follows:
dbr1＝vrelτ+0.5amaxτ2
dbr2＝TTC*vrel+d0
the output result of the calculation of the braking critical distance is a binary variable 0 or 1, and respectively, when the relative distance between the self vehicle and the front vehicle is less than or equal to the calculated braking critical distance, the output result is 1; when the relative distance between the self vehicle and the front vehicle is greater than the calculated braking critical distance, the output result is 0, the judgment result is coupled and applied to the forced braking decision of the system based on two judgment modes, s represents the addition sum of the two judgment output results, and the final braking decision specific implementation mode is as follows:
Figure FDA0003367457920000051
when g (x) is 0, the system does not perform forced braking intervention, and when g (x) is 1, the control decision device makes a forced braking decision and transmits the braking decision to the braking control device, and the braking control device receives the braking decision information and performs forced braking on the vehicle.
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