CN110550038A - Intelligent driving decision information acquisition system and method - Google Patents
Intelligent driving decision information acquisition system and method Download PDFInfo
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- CN110550038A CN110550038A CN201910945156.XA CN201910945156A CN110550038A CN 110550038 A CN110550038 A CN 110550038A CN 201910945156 A CN201910945156 A CN 201910945156A CN 110550038 A CN110550038 A CN 110550038A
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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
- B60W40/02—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 ambient conditions
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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
- B60W40/02—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 ambient conditions
- B60W40/06—Road conditions
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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
- B60W40/10—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 vehicle motion
- B60W40/105—Speed
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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
- 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
- B60W50/0098—Details of control systems ensuring comfort, safety or stability not otherwise provided for
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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
- 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
- B60W2050/0001—Details of the control system
- B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation
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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
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
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Abstract
The invention relates to the technical field of automatic driving, and particularly discloses an intelligent driving decision information acquisition system and a method, wherein the system comprises an information acquisition module and a data processing module, wherein the information acquisition module is used for acquiring road surface information, image information of surrounding vehicles, the speed and the position of the vehicle and the relative speed and the relative position of the surrounding vehicles in real time and sending the road surface information, the image information of the surrounding vehicles, the speed and the position of the vehicle and the relative position of the surrounding vehicles to the data processing module; the data processing module identifies the type of the vehicle through image information of surrounding vehicles; the data processing module also analyzes the running states of the vehicle and the surrounding vehicles based on the road surface information, the speed and the position of the vehicle and the relative speed and the relative position of the surrounding vehicles; generating driving decision information of the vehicle; the data processing module also judges whether the running state of the surrounding vehicles is abnormal or not based on the vehicle types, and if so, the running decision information of the vehicle is adjusted according to the preprocessing measures. By adopting the technical scheme of the invention, the behaviors of other running vehicles in the road can be collected and analyzed so as to find out the dangerous condition in advance.
Description
Technical Field
The invention relates to the technical field of automatic driving, in particular to an intelligent driving decision information acquisition system and method.
Background
The intelligent driving technology is widely popularized in the industry as the future development direction of traditional fuel vehicles and new energy vehicles, and all large automobile manufacturers and high-tech companies all put a great deal of effort to research, develop, test and verify the related technologies of intelligent driving.
The existing intelligent driving system generally collects environmental information such as lane lines, traffic lights, obstacles and the like by arranging various sensors, calculates and fuses the environmental information by using a sensing and fusing unit and outputs a global path, and calculates and outputs a local running path of a vehicle and driving action control instructions such as acceleration, deceleration, lane change and the like to vehicle execution systems such as a steering system, a braking system, a driving system and the like of the vehicle by using an intelligent decision unit so as to realize automatic driving of the vehicle.
however, the current intelligent driving system only relies on its own sensor to sense the outside, but ignores the reference to the driving behavior of other vehicles, for example, if the vehicle in front suddenly changes lane, if someone drives, it is considered that there may be an obstacle in front, and then decelerates in advance. The existing intelligent driving system cannot detect whether a barrier exists in the front or not due to the fact that a front vehicle is shielded, the existing vehicle speed can be kept, and if the barrier is detected again after the front vehicle changes the lane, the deceleration operation may not be performed in time, and safety accidents are easily caused.
Therefore, in order to improve the safety of intelligent driving and find out dangerous situations in advance, the behaviors of other running vehicles in the road need to be collected and analyzed.
disclosure of Invention
In order to solve the technical problems, the invention aims to provide an intelligent driving decision information acquisition system.
The technical scheme of the invention is as follows:
An intelligent driving decision information acquisition system comprises an information acquisition module and a data processing module, wherein the information acquisition module is used for acquiring road surface information, image information of surrounding vehicles, the speed and the position of the vehicle and the relative speed and the relative position of the surrounding vehicles in real time and sending the road surface information, the image information of the surrounding vehicles, the speed and the position of the vehicle and the relative position of the surrounding vehicles to the data processing module; the data processing module identifies the type of the vehicle through image information of surrounding vehicles; the data processing module is also used for analyzing the driving states of the vehicle and the surrounding vehicles based on the road surface information, the speed and the position of the vehicle and the relative speed and the relative position of the surrounding vehicles to generate driving decision information of the vehicle;
the data processing module also judges whether the running state of the surrounding vehicles is abnormal or not based on the vehicle types, and if the running state of the surrounding vehicles is abnormal, the running decision information of the vehicle is adjusted according to the preprocessing measures.
The basic scheme principle and the beneficial effects are as follows:
The existing intelligent driving mainly detects the state of the vehicle, the state of the road surface, the distance between the vehicle and the surrounding vehicles and the like through various sensors, in other words, the vehicle only needs to keep a proper distance with the surrounding vehicles, which is similar to a novice driver, and only pays attention to the driving behavior of the driver, so that the adjustment cannot be made in advance according to the abnormal state of the surrounding vehicles, and the driving safety cannot be effectively improved. According to the scheme, the types of the surrounding vehicles are identified, and whether the running states of the surrounding vehicles are abnormal or not is judged based on the vehicle types, so that problems can be found in advance, adjustment can be made in advance, a response can be made before a traffic accident happens, and the accident can be avoided or the loss of the accident can be reduced. The vehicle types need to be identified because the abnormal driving states of different vehicle types are different, and a differential analysis is needed, for example, it is common that a taxi suddenly changes lanes and suddenly stops at the side, because a taxi driver is in a normal operation for catching time or pulling passengers; relatively speaking, when the bus suddenly changes lanes, the danger in front of the sudden brake is high, and attention needs to be paid.
Further, the data processing module comprises a storage unit and a processing unit, wherein an abnormal driving state library is prestored in the storage unit, and the abnormal driving state library comprises an abnormal driving state corresponding to each vehicle type and a preset processing measure corresponding to the abnormal driving state; the processing unit acquires the abnormal running state library, matches the vehicle types and running states of surrounding vehicles with the abnormal running state library, judges that the running state of the vehicle is abnormal if the matching is successful, and adjusts the running decision information of the vehicle according to corresponding preprocessing measures.
By setting a corresponding abnormal driving state for each vehicle type, the pertinence is stronger.
Further, the information acquisition module comprises a speed sensor, a camera, a millimeter wave radar and a laser radar; the speed sensor is used for collecting the speed of the vehicle, the camera is used for collecting road surface information and surrounding vehicle image information, the millimeter wave radar is used for collecting the relative speed and the relative position of surrounding vehicles, and the laser radar is used for collecting road obstacles and emergencies.
by arranging various sensors and radars, various information in the driving process of the vehicle can be effectively acquired.
Further, the information acquisition module also comprises a temperature and humidity sensor and an atmospheric particle concentration sensor.
through temperature and humidity sensor and atmospheric particles concentration sensor, can detect weather conditions, such as raining, snowing, heavy fog and sand storm etc.. The driving decision information of the vehicle can be generated conveniently according to different weather conditions. Such as rain, snow, fog or sand storms, to reduce lane changes and to slow down the ride.
Further, the vehicle type includes one or more of a taxi, a bus, a private car, and a van.
By setting various vehicle types, the success rate of recognizing abnormal driving states can be improved.
Further, the abnormal driving state includes one or more of emergency braking, emergency lane change, S-shaped driving, and edge parking.
By subdividing the abnormal driving state, the accurate identification is facilitated.
An intelligent driving decision information acquisition method comprises the following steps:
S1, collecting road surface information, image information of surrounding vehicles, the speed and the position of the vehicle and the relative speed and the relative position of the surrounding vehicles in real time;
S2, analyzing the driving states of the vehicle and the surrounding vehicles based on the road surface information, the speed and the position of the vehicle and the relative speed and the relative position of the surrounding vehicles;
S3, generating driving decision information of the vehicle;
S4, identifying the type of the vehicle through the image information of the surrounding vehicles; judging whether the running state of the surrounding vehicle is abnormal or not based on the vehicle type and the running state of the surrounding vehicle and a pre-stored abnormal running state library; and if the vehicle is abnormal, adjusting the driving decision information of the vehicle according to the preprocessing measure.
According to the scheme, the types of the surrounding vehicles are identified, and whether the running states of the surrounding vehicles are abnormal or not is judged based on the vehicle types, so that problems can be found in advance, adjustment can be made in advance, a response can be made before a traffic accident happens, and the accident can be avoided or the loss of the accident can be reduced. The vehicle types need to be identified because the abnormal driving states of different vehicle types are different, and a differential analysis is needed, for example, it is common that a taxi suddenly changes lanes and suddenly stops at the side, because a taxi driver is in a normal operation for catching time or pulling passengers; relatively speaking, when the bus suddenly changes lanes, the danger in front of the sudden brake is high, and attention needs to be paid.
Further, in S1, a speed sensor, a camera, a millimeter wave radar, and a laser radar are included; the speed sensor is used for collecting the speed of the vehicle, the camera is used for collecting road surface information and surrounding vehicle image information, the millimeter wave radar is used for collecting the relative speed and the relative position of surrounding vehicles, and the laser radar is used for collecting road obstacles and emergencies.
By arranging various sensors and radars, various information in the driving process of the vehicle can be effectively acquired.
further, in S4, the vehicle type includes one or more of a taxi, a bus, a private car and a truck.
by setting various vehicle types, the success rate of recognizing abnormal driving states can be improved.
Further, in S4, the abnormal driving state includes one or more of emergency braking, emergency lane changing, S-shaped driving, and edge parking.
By subdividing the abnormal driving state, the accurate identification is facilitated.
Drawings
Fig. 1 is a logic block diagram of an embodiment of an intelligent driving decision information acquisition system.
Detailed Description
The following is further detailed by way of specific embodiments:
Example one
As shown in fig. 1, an intelligent driving decision information acquisition system includes an information acquisition module and a data processing module.
The information acquisition module comprises a speed sensor, a camera, a millimeter wave radar, a laser radar, a temperature and humidity sensor and an atmospheric particle concentration sensor.
The speed sensor is used for collecting the speed of the vehicle, the camera is used for collecting road surface information and image information of surrounding vehicles, the millimeter wave radar is used for collecting the relative speed and the relative position of the surrounding vehicles, the laser radar is used for collecting road barriers and emergencies, and the temperature and humidity sensor and the atmospheric particle concentration sensor are used for collecting weather information. In this embodiment, the weather information includes rain, snow, fog, sand storm, and the like; the emergency refers to a sudden intrusion of a pedestrian, an animal, or the like from the front.
For convenience of description, the vehicle speed, the road surface information, the relative speed and relative position of surrounding vehicles, road obstacles, emergencies, and weather information are collectively referred to as decision reference information. The decision reference information and the surrounding vehicle image information collected in real time are sent to the data processing module.
The data processing module comprises a storage unit and a processing unit; an abnormal running state base is prestored in the storage unit, and comprises an abnormal running state corresponding to each vehicle type; and a preset treatment measure corresponding to the abnormal driving state.
The processing unit analyzes the driving states of the vehicle and the surrounding vehicles through the decision reference information and generates driving decision information of the vehicle, wherein the driving decision information comprises acceleration, deceleration, parking, lane changing and the like.
specifically, each piece of driving decision information includes acceleration control information and steering angle control information. The control device of the automobile can adjust one or more of an accelerator, a brake, a clutch and a steering of the automobile according to the combination of the acceleration control information and the steering angle control information, so that the acceleration, the deceleration, the parking or the lane change of the automobile and the like are realized, and the control device belongs to the prior art and is not described herein again.
The processing unit also identifies the type of the surrounding vehicle based on the image information of the surrounding vehicle. The vehicle type includes one or more of a taxi, a bus, a private car, and a van. In this embodiment, the vehicle types include a taxi, a bus, a private car, and a van.
The processing unit acquires the abnormal running state library, matches the vehicle types and running states of surrounding vehicles with the abnormal running state library, judges that the running state of the vehicle is abnormal if the matching is successful, and then adjusts the running decision information of the vehicle according to preset processing measures. The abnormal driving state includes one or more of emergency braking, emergency lane change, S-shaped driving, and edge parking, and in this embodiment, includes all of emergency braking, emergency lane change, S-shaped driving, and edge parking.
for example, in the abnormal driving state library, when the vehicle type is a taxi, the corresponding abnormal driving state includes a parking while leaning on, and the preset processing measures corresponding to the parking while leaning on include: decelerating;
when the vehicle type is the bus, the corresponding abnormal driving state includes urgent lane change, and the preset treatment measures corresponding to the urgent lane change include: decelerating and changing lanes;
When the vehicle type is a private car, the corresponding abnormal driving state comprises S-shaped driving, and the preprocessing measure corresponding to the S-shaped driving comprises the following steps: decelerating;
When the type of the vehicle is a truck, the corresponding abnormal driving state comprises emergency braking, and the preprocessing measure corresponding to the emergency braking comprises the following steps: deceleration and lane change.
Based on the intelligent driving decision information acquisition system, the embodiment further provides an intelligent driving decision information acquisition method, which comprises the following steps:
s1, acquiring the speed of the vehicle in real time through a speed sensor, acquiring road information and image information of surrounding vehicles through a camera, acquiring the relative speed and relative position of the surrounding vehicles through a millimeter wave radar, acquiring road obstacles and emergency situations through a laser radar, and acquiring weather information through a temperature and humidity sensor and an atmospheric particle concentration sensor;
S2, analyzing the driving states of the vehicle and the surrounding vehicles based on the vehicle speed of the vehicle, the road surface information, the relative speed and the relative position of the surrounding vehicles, road obstacles and emergencies and weather information;
S3, generating driving decision information of the vehicle;
S4, identifying the type of the vehicle through the image information of the surrounding vehicles; in this embodiment, the vehicle types include a taxi, a bus, a private car, and a van. Judging whether the running state of the surrounding vehicle is abnormal or not based on the vehicle type and the running state of the surrounding vehicle and a pre-stored abnormal running state library; the abnormal driving state library comprises an abnormal driving state corresponding to each vehicle type and a preset treatment measure corresponding to the abnormal driving state; and if the vehicle is abnormal, adjusting the driving decision information of the vehicle according to the preprocessing measure. In the present embodiment, the abnormal driving states include emergency braking, emergency lane change, S-shaped driving, and edge parking.
example two
An intelligent driving decision information acquisition system is different from the first embodiment in that: the processing unit also identifies the model of the surrounding vehicle based on the image information of the surrounding vehicle, wherein in the embodiment, the model identification of the vehicle is carried out based on the convolutional neural network model; the storage unit is also pre-stored with an appearance image corresponding to each model of the vehicle; the abnormal driving state library also comprises preset treatment measures corresponding to appearance damage. The processing unit acquires the corresponding appearance image from the storage unit based on the model of the surrounding vehicle, and the processing unit compares the image information of the surrounding vehicle with the corresponding appearance image to judge whether the surrounding vehicle has appearance damage. In this embodiment, the appearance damage refers to damage on the appearance of the automobile, such as missing parts, dents, large scratches, and the like caused by collision, which can be captured by the camera. For some small wiping marks, the scheme does not need to collect, and the existing camera is not easy to collect in the high-speed motion of the automobile.
When the processing unit judges that the peripheral vehicles are damaged, the preset processing measures corresponding to the appearance damage are obtained from the storage unit, and the processing unit adjusts the driving decision information of the vehicle according to the preset processing measures.
By judging whether the appearance of the automobile is damaged or not, the driving habit of a corresponding automobile driver can be judged from one dimension; firstly, the accident of the automobile is proved by the existence of damage, and the probability of the traffic accident of the automobile controlled by the driver is higher; secondly, the damage still exists, proves that the automobile is not maintained in time, the degree of the driver's love on the automobile is lower, and the possibility of having a violent driving style is higher (generally, people who love the automobile can maintain in time when problems occur, and people can also worry about the wings to avoid being scratched when driving at ordinary times). The reason why the traffic accident data of the insurance company or the traffic control department is not adopted as a reference in this embodiment is that the traffic accident has private conditions, which are not recorded on the record. The present embodiment is not affected by this.
The foregoing is merely an example of the present invention and common general knowledge of known specific structures and features of the embodiments is not described herein in any greater detail. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (10)
1. An intelligent driving decision information acquisition system comprises an information acquisition module and a data processing module, wherein the information acquisition module is used for acquiring road surface information, image information of surrounding vehicles, the speed and the position of the vehicle and the relative speed and the relative position of the surrounding vehicles in real time and sending the road surface information, the image information of the surrounding vehicles, the speed and the position of the vehicle and the relative position of the surrounding vehicles to the data processing module; the system is characterized in that the data processing module identifies the type of the vehicle through image information of surrounding vehicles; the data processing module also analyzes the running states of the vehicle and the surrounding vehicles based on the road surface information, the speed and the position of the vehicle and the relative speed and the relative position of the surrounding vehicles; generating driving decision information of the vehicle;
the data processing module also judges whether the running state of the surrounding vehicles is abnormal or not based on the vehicle types, and if the running state of the surrounding vehicles is abnormal, the running decision information of the vehicle is adjusted according to the preprocessing measures.
2. The intelligent driving decision information gathering system as recited in claim 1 wherein: the data processing module comprises a storage unit and a processing unit, wherein an abnormal driving state library is prestored in the storage unit, and the abnormal driving state library comprises an abnormal driving state corresponding to each vehicle type and a preset processing measure corresponding to the abnormal driving state; the processing unit acquires the abnormal running state library, matches the vehicle types and running states of surrounding vehicles with the abnormal running state library, judges that the running state of the vehicle is abnormal if the matching is successful, and adjusts the running decision information of the vehicle according to corresponding preprocessing measures.
3. The intelligent driving decision information gathering system as recited in claim 2 wherein: the information acquisition module comprises a speed sensor, a camera, a millimeter wave radar and a laser radar; the speed sensor is used for collecting the speed of the vehicle, the camera is used for collecting road surface information and surrounding vehicle image information, the millimeter wave radar is used for collecting the relative speed and the relative position of surrounding vehicles, and the laser radar is used for collecting road obstacles and emergencies.
4. The intelligent driving decision information gathering system as recited in claim 3 wherein: the information acquisition module further comprises a temperature and humidity sensor and an atmospheric particle concentration sensor.
5. The intelligent driving decision information gathering system as recited in claim 2 wherein: the vehicle type comprises one or more of a taxi, a bus, a private car and a truck.
6. the intelligent driving decision information gathering system as recited in claim 2 wherein: the abnormal driving state includes one or more of emergency braking, emergency lane change, S-shaped driving, and edge parking.
7. an intelligent driving decision information acquisition method is characterized by comprising the following steps:
s1, collecting road surface information, image information of surrounding vehicles, the speed and the position of the vehicle and the relative speed and the relative position of the surrounding vehicles in real time;
S2, analyzing the driving states of the vehicle and the surrounding vehicles based on the road surface information, the speed and the position of the vehicle and the relative speed and the relative position of the surrounding vehicles;
s3, generating driving decision information of the vehicle;
S4, identifying the type of the vehicle through the image information of the surrounding vehicles; judging whether the running state of the surrounding vehicle is abnormal or not based on the vehicle type and the running state of the surrounding vehicle and a pre-stored abnormal running state library; and if the vehicle is abnormal, adjusting the driving decision information of the vehicle according to the preprocessing measure.
8. The intelligent driving decision information collecting method according to claim 7, characterized in that: in the step S1, the device includes a speed sensor, a camera, a millimeter wave radar and a laser radar; the speed sensor is used for collecting the speed of the vehicle, the camera is used for collecting road surface information and surrounding vehicle image information, the millimeter wave radar is used for collecting the relative speed and the relative position of surrounding vehicles, and the laser radar is used for collecting road obstacles and emergencies.
9. The intelligent driving decision information collecting method according to claim 7, characterized in that: in S4, the vehicle type includes one or more of a taxi, a bus, a private car and a van.
10. The intelligent driving decision information collecting method according to claim 7, characterized in that: in S4, the abnormal driving state includes one or more of emergency braking, emergency lane change, S-shaped driving, and edge parking.
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