CN109532832B - Longitudinal planning method in intelligent driving - Google Patents

Longitudinal planning method in intelligent driving Download PDF

Info

Publication number
CN109532832B
CN109532832B CN201811499161.4A CN201811499161A CN109532832B CN 109532832 B CN109532832 B CN 109532832B CN 201811499161 A CN201811499161 A CN 201811499161A CN 109532832 B CN109532832 B CN 109532832B
Authority
CN
China
Prior art keywords
speed
planning
target
vehicle
longitudinal
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Fee Related
Application number
CN201811499161.4A
Other languages
Chinese (zh)
Other versions
CN109532832A (en
Inventor
陈晓玥
葛荡
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
East China Jiaotong University
Original Assignee
East China Jiaotong University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by East China Jiaotong University filed Critical East China Jiaotong University
Priority to CN201811499161.4A priority Critical patent/CN109532832B/en
Publication of CN109532832A publication Critical patent/CN109532832A/en
Application granted granted Critical
Publication of CN109532832B publication Critical patent/CN109532832B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/14—Adaptive cruise control
    • B60W30/143—Speed control
    • 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
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/80—Spatial relation or speed relative to objects
    • B60W2554/801—Lateral distance
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/80—Spatial relation or speed relative to objects
    • B60W2554/804—Relative longitudinal speed

Landscapes

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

Abstract

The invention provides a longitudinal planning method in intelligent driving, which comprises the following steps: step 01: acquiring target speeds of a plurality of target objects; step 02: respectively carrying out multi-target speed planning on the target speeds; step 03: and selecting the lowest speed as the current planning result according to the result obtained by the multi-target speed planning. By adopting a method based on interpolation and multi-target comprehensive planning, the longitudinal safety of the vehicle can be ensured, the comfort of the vehicle in the acceleration and deceleration process can be ensured at the same time, and the speed planning can be carried out in response to the information of the front multi-target, so that the longitudinal safety can be ensured; the stable speed keeping function can be realized, and the parking error is reduced; aiming at the brake systems with different precisions, the longitudinal planning of the automatic driving vehicle can be realized.

Description

Longitudinal planning method in intelligent driving
Technical Field
The invention relates to the technical field of automatic driving, in particular to a longitudinal planning method in intelligent driving.
Background
In the process of keeping the longitudinal safe distance of the automobile, the traveling speed and the acceleration of the automobile need to be planned, and the safety and the comfort in the acceleration and deceleration process are ensured.
Problems and disadvantages of prior art solutions: the vehicle acceleration and deceleration process usually pursues quick response, so that the acceleration and deceleration are too violent, and the comfort is poor; the longitudinal planning method of the general automatic driving system is difficult to adapt to conditions such as various vehicle types, various road conditions and the like, and the braking and parking precision is difficult to guarantee.
The existing longitudinal planning scheme is single-target speed planning, and the principle of the single-target speed planning is as follows:
first, constraint distance
The constrained distance varies with driving conditions and is dependent on the current speed of the vehicle, the maximum speed allowed, the target obstacle speed, the maximum acceleration constraint, the psychological deceleration constraint, etc., as follows:
Figure BDA0001897726350000011
Figure BDA0001897726350000012
stotal＝sacce+sdece
wherein s isacceTo accelerate the confinement distance, sdeceFor decelerating the constraint distance, stotal is the total constraint distance.
Further, vcurIs the current speed, v, of the vehiclemaxAt an allowable maximum speed, vobjTarget barrier speed (including dynamic barrier speed, bending speed calculated according to curvature, obstacle avoidance speed calculated according to passable path width, and the like); a isacceFor maximum acceleration (settable, default 10s acceleration from zero to maximum speed), adeceIs a psychological deceleration (which may be set to a default of 10s from maximum speed to zero, the deceleration being not constrained by this deceleration in an emergency).
Two, velocity interpolation
Principle of A interpolation
When the distance between the vehicle and the obstacle is beyond the constraint distance, the vehicle can run at the maximum speed to ensure the safety and the longitudinal comfort (the actual acceleration is between a)acceAnd adeceIn between, without sudden rise and decrease), the planned speed is taken as the maximum speed vmax；
When the distance between the vehicle and the obstacle is within the constraint distance, the vehicle speed cannot reach the maximum speed, and then the speed needs to be interpolated for ensuring the continuity of the planned speed, as follows:
Figure BDA0001897726350000021
wherein v isplanFor the planned speed for the obstacle, s is the actual distance of the vehicle (rear axle center) to the obstacle.
B interpolation mode
According to different performances of driving behaviors, the longitudinal planning is divided into an energy-saving mode and a motion mode, and the energy-saving mode and the motion mode are as follows:
a. energy-saving mode:
the method does not carry out speed interpolation on avoidable obstacles in a lane, and the target speed directly adopts the speed v of the obstaclesobj；
b. And (3) motion mode:
pay more attention to time performance, carry out speed to dodging barrier in lane and insertValue vplanThese speeds include the speed of over-bending and the speed of obstacle avoidance.
C control strategy
The control strategy for longitudinal speed includes both precision and fuzzy control, and qin EVs generally employ precision speed control with the following exceptions:
planning the speed in the curve: and (3) adopting fuzzy control, and when the difference between the actual vehicle speed and the over-bending speed fluctuates within 5km/h, not adjusting the speed, but adopting the current speed to over-bend.
Therefore, due to the fact that actual road environments are variable, the traditional speed planning method in intelligent driving has the problems that longitudinal planning and acceleration and deceleration are performed only on a single front target, fixed-point parking cannot be achieved, and the like.
Disclosure of Invention
In order to overcome the problems, the invention aims to provide a longitudinal planning method in intelligent driving, which makes up the defects of single-target longitudinal planning.
In order to achieve the above object, the present invention provides a longitudinal planning method in intelligent driving, including:
step 01: acquiring target speeds of a plurality of target objects;
step 02: respectively carrying out multi-target speed planning on the target speeds;
step 03: and selecting the lowest speed as the current planning result according to the result obtained by the multi-target speed planning.
In some embodiments, the step 03 further includes a security policy planning.
In some embodiments, the security policy plan includes a basic security policy plan and an emergency security policy
Planning slightly;
the basic security policy plan includes: under the non-emergency condition, the safety distance is kept and the longitudinal safety is ensured according to the longitudinal planning strategy which meets the multi-objective constraint;
the emergency security policy planning comprises: in an emergency, when the obstacle is too close to the collision distance with the vehicle, the emergency safety strategy is automatically triggered and emergency braking is carried out.
In some embodiments, the emergency comprises: dynamic obstacle overapproximation, braking system handle
The line effect becomes worse.
In some embodiments, in step 03, the lowest speed is determined by the braking system of the bicycle
If the automobile is in failure or caused by a sudden situation in front, the automobile enters an emergency braking state.
In some embodiments, the collision distance is a set limit of collision of the host vehicle with the obstacle
Distance.
In some embodiments, said step 03 is followed by: planning the results of multiple target speeds
To the vehicle control module; the vehicle control module controls the speed of the intelligent driving according to the result.
In some embodiments, in step 01, the target speed comprises: current speed of vehicle, front
Square dynamic barrier speed, over-bending speed and barrier avoidance speed.
In some embodiments, in step 01, the target object includes: dynamic barrier for bicycle and front
An obstruction.
In some embodiments, the step 02 specifically includes:
respectively carrying out speed planning on the single targets, comprehensively considering the result, and selecting the lowest speed as the final result as follows:
Figure BDA0001897726350000041
a is acceleration, VplanTo plan the speed for the obstacle.
The longitudinal planning method in intelligent driving adopts a method based on interpolation and multi-target comprehensive planning, can ensure the longitudinal safety of the vehicle, and can ensure the comfort of the vehicle in the acceleration and deceleration process; speed planning (such as speed planning of complex multiple targets including curves, obstacles in front of the curves, obstacles in the curves and obstacles behind the curves) can be performed in response to the information of the multiple targets in front, so that the safety in the longitudinal direction is ensured; the technology can realize a stable speed keeping function, and the speed error does not exceed plus or minus 1km/h under the normal working condition; the technology can realize fixed-point parking, the parking error does not exceed plus or minus 20cm under normal working conditions, and longitudinal planning scenes such as static and dynamic barriers, traffic lights, stop lines, deceleration strips and the like can be effectively processed; the technology has obvious practicability when aiming at different vehicle types such as fuel vehicles, electric vehicles, passenger vehicles, commercial vehicles and the like; the longitudinal planning of the autonomous vehicle can be realized for different precision braking systems (from 0.01g for good braking precision and brightness to 0.1g only for emergency braking AEB scene).
Drawings
FIG. 1 is a schematic flow chart of a longitudinal planning method in intelligent driving according to an embodiment of the present invention
Detailed Description
In order to make the contents of the present invention more comprehensible, the present invention is further described below with reference to the accompanying drawings. The invention is of course not limited to this particular embodiment, and general alternatives known to those skilled in the art are also covered by the scope of the invention.
The invention is described in further detail below with reference to fig. 1 and the specific examples. It should be noted that the drawings are in a simplified form and are not to precise scale, and are only used for conveniently and clearly achieving the purpose of assisting in describing the embodiment.
Referring to fig. 1, a longitudinal planning method in intelligent driving of the present embodiment includes:
step 01: acquiring target speeds of a plurality of target objects;
specifically, in step 01, the target speed includes: the current speed of the vehicle, the speed of a front dynamic obstacle, the speed of passing a bend, the obstacle avoidance speed and the like. The target object includes: self-vehicle, front dynamic barrier, etc.
Step 02: respectively carrying out multi-target speed planning on the target speeds;
specifically, speed planning is respectively performed on the single targets, the results are comprehensively considered, and the lowest speed is selected as the final result, as follows:
Figure BDA0001897726350000051
a is acceleration, VplanTo plan the speed for the obstacle.
Step 03: and selecting the lowest speed as the current planning result according to the result obtained by the multi-target speed planning.
Specifically, in this step 03, a security policy plan is further included. The security policy plan herein includes a basic security policy plan and an emergency security policy plan. The basic security policy plan includes: and under the non-emergency condition, the safety distance is kept and the longitudinal safety is ensured according to the longitudinal planning strategy which meets the multi-objective constraint. The emergency security policy planning comprises: in an emergency, when the obstacle is too close to the collision distance with the vehicle, the emergency safety strategy is automatically triggered and emergency braking is carried out. The collision distance here is a set limit distance at which the own vehicle collides with the obstacle. Once less than the limit distance, a vehicle collision may occur.
Here, the emergency includes: dynamic obstacles are too close to each other, the execution effect of a braking system is poor, and the like. Based on the safety strategy, in step 03 of this embodiment, if the lowest speed is caused by a failure of the braking system of the host vehicle or a forward emergency, the host vehicle enters an emergency braking state.
In addition, in this embodiment, after the step 03, the method further includes: sending results obtained by the multi-target speed planning to a vehicle control module; the vehicle control module controls the speed of the intelligent driving according to the result.
In conclusion, the longitudinal planning method in intelligent driving of the invention adopts a method based on interpolation and multi-target comprehensive planning, can ensure the longitudinal safety of the vehicle, and can ensure the comfort of the vehicle in the acceleration and deceleration process; the technology can respond to the information of multiple targets in front to carry out speed planning (such as the speed planning of complex multiple targets including a curve, a barrier in the front of the curve, a barrier in the curve and a barrier behind the curve), thereby ensuring the safety in the longitudinal direction; the technology can realize the stable speed keeping function, and the speed error is very small under the normal working condition; the technology can realize fixed-point parking, and the parking error is very small under normal working conditions; the technology can effectively process longitudinal planning scenes such as static and dynamic barriers, traffic lights, stop lines, speed bumps and the like, and has obvious practicability when aiming at different vehicle types such as fuel vehicles, electric vehicles, passenger vehicles, commercial vehicles and the like; aiming at the brake systems with different precisions, the longitudinal planning of the automatic driving vehicle can be realized.
Although the present invention has been described with reference to preferred embodiments, which are illustrated for the purpose of illustration only and not for the purpose of limitation, it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (3)

1. A longitudinal planning method in intelligent driving is characterized by comprising the following steps:
step 01: acquiring target speeds of a plurality of target objects;
step 02: respectively carrying out multi-target speed planning on the target speeds;
step 03: selecting the lowest speed as the current planning result according to the result obtained by the multi-target speed planning;
in step 01, the target speed includes: the current speed of the vehicle, the speed of a front dynamic obstacle, the speed of passing a bend and the obstacle avoidance speed;
in step 01, the target object includes: a self-vehicle and a front dynamic barrier;
the step 02 specifically includes:
respectively carrying out speed planning on the single targets, comprehensively considering the result, and selecting the lowest speed as the final result;
in the step 03, security policy planning is further included;
the security policy plan comprises a basic security policy plan and an emergency security policy plan;
the basic security policy plan includes: under the non-emergency condition, the safety distance is kept and the longitudinal safety is ensured according to the longitudinal planning strategy which meets the multi-objective constraint;
the emergency security policy planning comprises: under emergency, when the obstacle and the vehicle are too close to the collision distance, an emergency safety strategy is automatically triggered and emergency braking is carried out;
the emergency comprises the following steps: the dynamic barrier is too close to the dynamic barrier, and the execution effect of a brake system is poor;
in the step 03, if the lowest speed is caused by the failure of the braking system of the self-vehicle or a front emergency, the self-vehicle enters an emergency braking state.
2. The longitudinal planning method according to claim 1, wherein the collision distance is a set limit distance at which the host vehicle collides with the obstacle.
3. The longitudinal planning method according to claim 1, wherein the step 03 is followed by: sending results obtained by the multi-target speed planning to a vehicle control module; the vehicle control module controls the speed of the intelligent driving according to the result.
CN201811499161.4A 2018-12-08 2018-12-08 Longitudinal planning method in intelligent driving Expired - Fee Related CN109532832B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811499161.4A CN109532832B (en) 2018-12-08 2018-12-08 Longitudinal planning method in intelligent driving

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811499161.4A CN109532832B (en) 2018-12-08 2018-12-08 Longitudinal planning method in intelligent driving

Publications (2)

Publication Number Publication Date
CN109532832A CN109532832A (en) 2019-03-29
CN109532832B true CN109532832B (en) 2020-12-15

Family

ID=65853319

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811499161.4A Expired - Fee Related CN109532832B (en) 2018-12-08 2018-12-08 Longitudinal planning method in intelligent driving

Country Status (1)

Country Link
CN (1) CN109532832B (en)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105589464A (en) * 2016-03-28 2016-05-18 哈尔滨工程大学 UUV dynamic obstacle avoidance method based on speed obstruction method
CN106470884A (en) * 2014-06-17 2017-03-01 大众汽车有限公司 The determination of vehicle-state and the driver assistance when driving vehicle

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7349767B2 (en) * 2003-12-16 2008-03-25 Nissan Motor Co., Ltd. Method and system for intention estimation and operation assistance
KR101029096B1 (en) * 2010-11-18 2011-04-13 김은숙 Method of avoiding side collision of vehicle
CN105261224B (en) * 2015-09-02 2017-09-12 奇瑞汽车股份有限公司 Intelligent vehicle control method and apparatus
CN107719369B (en) * 2017-09-14 2019-05-10 北京智行者科技有限公司 The longitudinally controlled method, apparatus of automatic Pilot and the automatic driving vehicle with it
CN108189835B (en) * 2017-12-28 2020-04-21 清华大学苏州汽车研究院（吴江） Automatic driving collision avoidance control method and system

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106470884A (en) * 2014-06-17 2017-03-01 大众汽车有限公司 The determination of vehicle-state and the driver assistance when driving vehicle
CN105589464A (en) * 2016-03-28 2016-05-18 哈尔滨工程大学 UUV dynamic obstacle avoidance method based on speed obstruction method

Also Published As

Publication number Publication date
CN109532832A (en) 2019-03-29

Similar Documents

Publication Publication Date Title
CN106564497B (en) Method and apparatus for controlling subject vehicle
US11256260B2 (en) Generating trajectories for autonomous vehicles
US10532736B2 (en) Vehicle travel control device
US11186275B2 (en) Vehicle control system
US10793123B2 (en) Emergency braking for autonomous vehicles
CN111775940A (en) Automatic channel changing method, device, equipment and storage medium
CN104417561A (en) Context-aware threat response arbitration
US20190047571A1 (en) Method and device in a motor vehicle for an automatic drive
CN112896157A (en) Defensive driving control method, device and system, vehicle-mounted terminal and storage medium
CN112224202B (en) Multi-vehicle cooperative collision avoidance system and method under emergency working condition
US20220144269A1 (en) Method for securing a vehicle
JP2021062780A (en) Vehicle control system
CN112977453A (en) Automatic lane changing device and method for vehicle
CN113619575A (en) Emergency steering system based on millimeter wave radar
CN113954827B (en) Intersection vehicle collision probability calculation method and system considering positioning error and communication time delay
CN114763135A (en) Vehicle running control method and device, electronic equipment and storage medium
CN114312787A (en) Intelligent vehicle control method for mixed traffic flow congestion working condition
WO2022004042A1 (en) Vehicle control device and vehicle control system
CN112714730A (en) Method and device for operating an at least partially automatically operated first vehicle
WO2021109163A1 (en) Data processing method for vehicle automatic driving
CN109532832B (en) Longitudinal planning method in intelligent driving
CN115071695B (en) Automatic emergency braking control method and system for vehicle
CN112912292A (en) Method for at least partially automatically guiding a motor vehicle
CN110920622B (en) Prediction method before vehicle changes lane to target lane in automatic driving
CN115140027A (en) Intersection sight shielding scene control method and system and storage medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
CF01 Termination of patent right due to non-payment of annual fee
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20201215

Termination date: 20211208