CN113183984B - Automatic driving control method - Google Patents
Automatic driving control method Download PDFInfo
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- CN113183984B CN113183984B CN202110480120.6A CN202110480120A CN113183984B CN 113183984 B CN113183984 B CN 113183984B CN 202110480120 A CN202110480120 A CN 202110480120A CN 113183984 B CN113183984 B CN 113183984B
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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
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
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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
Abstract
The invention relates to the technical field of automatic driving, in particular to an automatic driving control method, which comprises the following steps: generating a planned route according to the collected driving information, wherein the driving information comprises whether the vehicle is urgent or not; screening the driving strategy of this time from a plurality of prestored driving strategies; judging whether the front vehicle is a special vehicle or not according to the collected front vehicle image; when the current vehicle is a special vehicle, judging whether the next road section is a special road section according to the planned route and the collected current coordinates; calling a preset special association table, and judging whether the special vehicle and the special road section are in special association or not according to the special association table when the next road section is the special road section; and when the special vehicle is specially associated with the special road section, extracting a preset adjusting strategy according to the special road section, and adjusting the driving strategy according to the adjusting strategy and whether the driving strategy is urgent or not. This scheme of adoption can reduce the risk of taking place the accident among the autopilot process.
Description
Technical Field
The invention relates to the technical field of automatic driving, in particular to an automatic driving control method.
Background
With the rapid development of vehicle intelligence, people have higher and higher requirements on an intelligent auxiliary system of an automobile, for example, an automatic driving system of the vehicle realizes safer and more efficient driving experience through the automatic driving system. When the automatic driving system is used, a destination needs to be selected first, a route is planned based on the destination to generate an automatic driving strategy, and automatic driving of a vehicle is achieved based on the planned route and the automatic driving strategy. At present, when the vehicle is automatically driven, the vehicle can only run according to a planned route and a conventional speed, and traffic regulations are followed in the running process. However, in the driving process, not only the driving vehicle is used, but also other vehicles can be driven on the same road section together, and when the risk of other vehicles is high, the risk of accidents caused by the driving vehicle is also high, for example, an engineering vehicle has higher risk after driving the engineering vehicle than after driving a common vehicle. Meanwhile, in a special road section, the risk of accidents is higher, such as a frequently-occurring accident road section and a slope road section, and the current automatic driving vehicle cannot carry out accident risk prediction according to the situation of the front vehicle, so that different automatic driving operations are executed, and the risk of accidents in the automatic driving process is reduced.
Disclosure of Invention
The invention aims to provide an automatic driving control method which can reduce the risk of accidents in the automatic driving process.
The present invention provides the following basic scheme:
an automatic driving control method, comprising:
a route planning step: generating a planned route according to the collected driving information, wherein the driving information comprises whether the vehicle is urgent or not;
strategy selection: screening the driving strategy of this time from a plurality of prestored driving strategies;
the method also comprises the following steps:
a vehicle judgment step: judging whether the front vehicle is a special vehicle or not according to the collected front vehicle image;
road section judging step: when the current vehicle is a special vehicle, judging whether the next road section is a special road section according to the planned route and the collected current coordinates;
and (3) association matching: calling a preset special association table, and judging whether the special vehicle and the special road section are in special association or not according to the special association table when the next road section is the special road section;
a strategy adjusting step: and when the special vehicle is specially associated with the special road section, extracting a preset adjusting strategy according to the special road section, and adjusting the driving strategy according to the adjusting strategy and whether the driving strategy is emergently adjusted.
The beneficial effects of the basic scheme are as follows:
the driving route of the trip is planned through the route planning step, and the driving strategy of the automatic driving of the trip is screened through the strategy selection step, so that the vehicle can be automatically driven according to the requirements of the driver.
The special vehicle is a vehicle distinguished from a conventional car, for example, a vehicle with a high degree of breakage, for example, a taxi which is often parked temporarily. And the vehicle judging step is set, and whether the front vehicle is a special vehicle is judged according to the collected front vehicle image. The probability of accidents of different vehicles on different road sections is different, therefore, whether the next road section is a special road section is judged through the road section judging module, such as a section with multiple accidents, an ascending slope and a descending slope, whether the special vehicles are matched with the special road section is judged through the correlation matching step, the probability of accidents of the special vehicles when the special vehicles run on the special road section is known, the combination of the special road section and the special vehicles which are specially correlated exists, the probability of accidents is high, and therefore the adjusting strategy corresponding to the special road section and the driving strategy of the trip which is adjusted whether the trip is urgent or not are obtained.
By adopting the scheme, when the front vehicle is the special vehicle, whether the next road section is the special road section associated with the special vehicle is judged, so that different adjustment strategies are executed to adjust the driving strategy of the trip. For example, in the case of a damaged vehicle, the degree of damage is high, which means that the probability of a safety accident occurring is high, and the corresponding road sections where the safety accident is likely to occur are the accident-prone road sections and the sharp-turn road sections, so that the damaged vehicle needs to be paid more attention in the accident-prone road sections and the sharp-turn road sections, and the risk of the accident is reduced by a strategy of being away from or exceeding the preceding vehicle based on the degree of urgency of the current driving.
Further, the policy selection step specifically includes the following steps:
screening the driving strategy of the current time from a plurality of pre-stored driving strategies according to whether the driving strategy is urgent or not, wherein the driving strategies comprise a conventional driving strategy and an urgent driving strategy; when the current behavior is not urgent, screening a conventional driving strategy as the current driving strategy; and when the trip is urgent, screening an urgent driving strategy as the driving strategy.
Has the advantages that: the conventional driving strategy is an optimal driving strategy formed by comprehensively considering factors such as a route, a vehicle speed, driving experience and safety under the condition of neglecting a time factor, and the emergency driving strategy is a driving strategy formed by adopting the highest allowable vehicle speed. The driving strategy is screened as the driving strategy according to the urgency of the trip, so that the requirement of drivers and passengers on the trip time is met on the premise of ensuring the safety.
Further, the special vehicle comprises a damaged vehicle, and the vehicle judgment step specifically comprises the following steps:
calling a preset damage threshold, wherein the damage threshold comprises an area threshold and a depth threshold;
and obtaining the damaged area and the recess depth of the front vehicle according to the image of the front vehicle, judging whether the front vehicle is a damaged vehicle or not according to the damaged area, the recess depth and the damaged threshold value, and judging that the front vehicle is the damaged vehicle when the damaged area is larger than the area threshold value or the recess depth is larger than the depth threshold value.
Has the advantages that: the damaged area is the sum of the areas of the damaged parts of the front vehicle, and the sunken depth is the maximum depth of the damaged parts of the front vehicle. The damage degree of the vehicle represents the degree of the vehicle owner's love to the vehicle and the probability of the occurrence of an accident, and the damaged vehicle has a higher risk and a higher possibility of the occurrence of the accident than the other vehicles. Therefore, the damage degree of the front vehicle is judged based on the damage area and the recess depth, so that whether the front vehicle belongs to a damaged vehicle or not, namely a vehicle with high damage degree is judged.
Further, the special vehicle comprises a practice vehicle and a taxi, and the vehicle judgment step specifically comprises the following steps:
extracting vehicle marks according to the images of the front vehicles, wherein the vehicle marks comprise practice and rent, and when the extracted vehicle marks are practice, judging the front vehicles to be practice vehicles; and when the extracted vehicle mark is a taxi, judging that the front vehicle is a taxi.
Has the advantages that: the method is characterized in that a training vehicle is not enough in strain capacity and is one of vehicles which are easy to have accidents, a taxi is frequently parked temporarily, traffic jam and the like are easy to occur, and the training vehicle and the taxi have obvious marks, so that a vehicle mark is extracted based on a front vehicle image, and whether the front vehicle is the taxi or the training vehicle is judged.
Further, the special vehicle comprises a large vehicle and an engineering vehicle, and the vehicle judgment step specifically comprises the following steps:
calling preset models of large vehicles and engineering vehicles, extracting the models of the front vehicles according to images of the front vehicles, matching the models of the front vehicles with the preset models, judging whether the matching items are the models of the large vehicles or not when the matching items exist, judging that the front vehicles are the large vehicles if the matching items exist, and judging that the front vehicles are the engineering vehicles if the matching items do not exist.
Has the advantages that: the large-sized vehicle and the engineering vehicle are also dangerous vehicles belonging to accidents on special road sections, such as slope road sections, and are easy to slip and slide, so whether the front vehicle is the large-sized vehicle or the engineering vehicle is judged according to the vehicle type.
Further, the special road sections comprise a slope road section, a road section with multiple accidents, a narrow road section and a sharp turning road section, and the special vehicles comprise damaged vehicles, practice vehicles, large-scale vehicles, engineering vehicles and taxis;
the special association table records that the slope road section is specially associated with the large-scale vehicle and the engineering vehicle, the accident-prone road section is specially associated with the damaged vehicle, the practice vehicle, the large-scale vehicle and the engineering vehicle, the narrow road section is specially associated with the practice vehicle, the large-scale vehicle, the engineering vehicle and the taxi, and the sharp-turn road section is specially associated with the damaged vehicle, the practice vehicle, the large-scale vehicle and the engineering vehicle.
Has the advantages that: the special road section is a road section which is easy to have accidents, and the special vehicle is a vehicle which is easy to have accidents. The special association table records special vehicles which are easy to have accidents under different special road sections, and association judgment between the special road sections and the special vehicles is realized through the special association table.
Drawings
Fig. 1 is a flowchart of an embodiment of an automatic driving control method according to the present invention.
Detailed Description
The following is further detailed by way of specific embodiments:
examples
An automatic driving control method, as shown in fig. 1, includes the following steps:
the collection step comprises: and acquiring driving information, wherein the driving information comprises a departure place, a destination and whether the driving information is urgent, and the driving information is acquired in a man-machine interaction mode. And acquiring a front vehicle image and the current coordinates of the vehicle in the automatic driving process in real time.
A route planning step: and generating a planned route according to the departure place and the destination, namely an automatic driving route of the vehicle. The planned route is composed of a plurality of road sections, wherein the road sections comprise special road sections and common road sections, and the special road sections comprise slope road sections, accident-prone road sections, narrow road sections and sharp-turn road sections.
Strategy selection: the method comprises the steps of screening a current driving strategy from a plurality of driving strategies which are pre-stored according to whether the current driving strategy is urgent or not, wherein the plurality of driving strategies comprise a conventional driving strategy and an urgent driving strategy, the conventional driving strategy is an optimal driving strategy which is formed by comprehensively considering factors such as a route, a vehicle speed, driving experience and safety under the condition of ignoring time factors, and the urgent driving strategy is a driving strategy which is formed by adopting the highest allowable vehicle speed. Specifically, when the current behavior is not urgent, screening a conventional driving strategy as the current driving strategy; and when the trip is urgent, screening an urgent driving strategy as the driving strategy.
A vehicle judgment step: and judging whether the front vehicle is a special vehicle or not according to the image of the front vehicle, and judging whether the front vehicle is the special vehicle or not again when the front vehicle is changed. The special vehicles comprise damaged vehicles, practice vehicles, large vehicles, engineering vehicles and taxis, and the vehicle judgment step specifically comprises the following steps:
s1: and calling a preset damage threshold, wherein the damage threshold is used for judging the damage degree of the front vehicle, so that whether the front vehicle belongs to a damaged vehicle is judged, and the damage threshold comprises an area threshold and a depth threshold.
And carrying out image analysis according to the image of the front vehicle to obtain the damaged area and the recess depth of the front vehicle, judging whether the front vehicle is a damaged vehicle or not according to the damaged area, the recess depth and the damaged threshold value, and judging that the front vehicle is the damaged vehicle when the damaged area is larger than the area threshold value or the recess depth is larger than the depth threshold value. The damaged area is the sum of the areas of the damaged parts of the front vehicle, and the sunken depth is the maximum depth of the damaged parts of the front vehicle.
S2: carrying out image recognition according to the image of the front vehicle to extract vehicle marks, wherein the vehicle marks comprise practice and rent, and when the extracted vehicle marks are practice, judging that the front vehicle is a practice vehicle; and when the extracted vehicle mark is a taxi, judging that the front vehicle is a taxi.
S3: calling preset models of large vehicles and engineering vehicles, extracting the models of the front vehicles according to images of the front vehicles, matching the models of the front vehicles with the preset models, judging whether the matching items are the models of the large vehicles or not when the matching items exist, judging that the front vehicles are the large vehicles if the matching items exist, and judging that the front vehicles are the engineering vehicles if the matching items do not exist.
Road section judging step: and when the current vehicle is a special vehicle, judging whether the next road section is a special road section according to the planned route and the current coordinate.
And (3) association matching: and calling a preset special association table, and judging whether the special vehicle and the special road section are in special association or not according to the special association table when the next road section is the special road section. In the embodiment, the special association table records that the slope road section is specially associated with the large-scale vehicle and the engineering vehicle, the section with multiple accidents is specially associated with the damaged vehicle, the practice vehicle, the large-scale vehicle and the engineering vehicle, the narrow road section is specially associated with the practice vehicle, the large-scale vehicle, the engineering vehicle and the taxi, and the sharp turn road section is specially associated with the damaged vehicle, the practice vehicle, the large-scale vehicle and the engineering vehicle.
A strategy adjusting step: and calling a plurality of preset adjusting strategies associated with the special road sections, wherein the adjusting strategies are used for adjusting the driving strategy. And when the special vehicle is specially associated with the special road section, extracting an adjustment strategy according to the special road section, and adjusting the driving strategy according to the adjustment strategy if the driving strategy is urgent.
When the special road section is a slope road section, the corresponding adjustment strategy is as follows: and judging whether the slope section is an uphill slope. When the slope section is an ascending slope, if the exit behavior is not urgent, the vehicle is controlled to slowly run until the special vehicle runs into the ascending slope after leaving the ascending slope; and if the trip is urgent, controlling the vehicle to accelerate and exceed the special vehicle before entering the uphill. When the slope road section is a downhill, if the present behavior is not urgent, controlling the vehicle to slowly run and keeping away from the special vehicle by 3-4 vehicle distances; if the trip is urgent, the vehicle is controlled to accelerate, and the special vehicle is driven off the downhill before entering the downhill.
When the special road section is the accident-prone road section, the corresponding adjustment strategy is as follows: if the behavior is not urgent, controlling the vehicle to slowly run and keeping 3-4 vehicle distances away from the special vehicle; and if the trip is urgent, controlling the vehicle to run at an accelerated speed and exceeding the special vehicle before entering the accident-prone road section.
When the special road section is a narrow road section, the corresponding adjustment strategy is as follows: and controlling the vehicle to run at an accelerated speed and exceed the special vehicle before entering the narrow road section.
When the special road section is a sharp turning road section, the corresponding adjustment strategy is as follows: if the behavior is not urgent, controlling the vehicle to slowly run and keeping 3-4 vehicle distances away from the special vehicle; and if the trip is urgent, controlling the vehicle to run in an accelerated manner and exceeding the special vehicle before entering the sharp turning road section.
By adopting the scheme, when the front vehicle is a special vehicle, whether the next road section is the special road section associated with the special vehicle or not is judged, so that different adjustment strategies are executed to adjust the driving strategy of the trip. For example, in the case of a damaged vehicle, the degree of damage is high, which means that the probability of a safety accident occurring is high, and the corresponding road sections where the safety accident is likely to occur are the accident-prone road sections and the sharp-turn road sections, so that the damaged vehicle needs to be paid more attention in the accident-prone road sections and the sharp-turn road sections, and the risk of the accident is reduced by a strategy of being away from or exceeding the preceding vehicle based on the degree of urgency of the current driving.
The foregoing is merely an example of the present invention, and common general knowledge in the field of known specific structures and characteristics is not described herein in any greater extent than that known in the art at the filing date or prior to the priority date of the application, so that those skilled in the art can now appreciate that all of the above-described techniques in this field and have the ability to apply routine experimentation before this date can be combined with one or more of the present teachings to complete and implement the present invention, and that certain typical known structures or known methods do not pose any impediments to the implementation of the present invention by those skilled in the art. 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 (5)
1. An automatic driving control method, comprising:
a route planning step: generating a planned route according to the collected driving information, wherein the driving information comprises whether the vehicle is urgent or not;
a strategy selection step: screening the driving strategy of this time from a plurality of prestored driving strategies;
the method is characterized by also comprising the following steps:
a vehicle judgment step: judging whether the front vehicle is a special vehicle or not according to the collected front vehicle image;
road section judging: when the current vehicle is a special vehicle, judging whether the next road section is a special road section or not according to the planned route and the acquired current coordinates;
and (3) association matching: calling a preset special association table, and judging whether the special vehicle and the special road section are in special association or not according to the special association table when the next road section is the special road section;
a strategy adjusting step: when the special vehicle is specially associated with the special road section, extracting a preset adjusting strategy according to the special road section, and adjusting the driving strategy according to the adjusting strategy and whether the driving strategy is urgent or not;
the special road sections comprise slope road sections, accident-prone road sections, narrow road sections and sharp-turning road sections, and the special vehicles comprise damaged vehicles, practice vehicles, large-scale vehicles, engineering vehicles and taxis;
the special association table records that the slope road section is specially associated with the large-scale vehicle and the engineering vehicle, the accident-prone road section is specially associated with the damaged vehicle, the practice vehicle, the large-scale vehicle and the engineering vehicle, the narrow road section is specially associated with the practice vehicle, the large-scale vehicle, the engineering vehicle and the taxi, and the sharp-turn road section is specially associated with the damaged vehicle, the practice vehicle, the large-scale vehicle and the engineering vehicle.
2. An automatic driving control method according to claim 1, characterized in that: the strategy selection step specifically comprises the following steps:
screening the driving strategy of the current time from a plurality of pre-stored driving strategies according to whether the driving strategy is urgent or not, wherein the driving strategies comprise a conventional driving strategy and an urgent driving strategy; when the current behavior is not urgent, screening a conventional driving strategy as the current driving strategy; and when the trip is urgent, screening an urgent driving strategy as the driving strategy.
3. An automatic driving control method according to claim 1, characterized in that: the vehicle judging step specifically comprises the following steps:
calling a preset damage threshold, wherein the damage threshold comprises an area threshold and a depth threshold;
and obtaining the damaged area and the recess depth of the front vehicle according to the image of the front vehicle, judging whether the front vehicle is a damaged vehicle or not according to the damaged area, the recess depth and the damaged threshold value, and judging that the front vehicle is the damaged vehicle when the damaged area is larger than the area threshold value or the recess depth is larger than the depth threshold value.
4. An automatic driving control method according to claim 1, characterized in that: the vehicle judging step specifically comprises the following steps:
extracting vehicle marks according to the images of the front vehicles, wherein the vehicle marks comprise practice and rent, and when the extracted vehicle marks are practice, judging the front vehicles to be practice vehicles; and when the extracted vehicle mark is a taxi, judging that the front vehicle is a taxi.
5. An automatic driving control method according to claim 1, characterized in that: the vehicle judging step specifically comprises the following steps:
calling preset models of large vehicles and engineering vehicles, extracting the models of the front vehicles according to images of the front vehicles, matching the models of the front vehicles with the preset models, judging whether the matching items are the models of the large vehicles or not when the matching items exist, judging that the front vehicles are the large vehicles if the matching items exist, and judging that the front vehicles are the engineering vehicles if the matching items do not exist.
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