CN110517480B - Driving right switching and collision early warning system for man-machine co-driving intelligent networked vehicle - Google Patents
Driving right switching and collision early warning system for man-machine co-driving intelligent networked vehicle Download PDFInfo
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- CN110517480B CN110517480B CN201910666351.9A CN201910666351A CN110517480B CN 110517480 B CN110517480 B CN 110517480B CN 201910666351 A CN201910666351 A CN 201910666351A CN 110517480 B CN110517480 B CN 110517480B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/096833—Systems involving transmission of navigation instructions to the vehicle where different aspects are considered when computing the route
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
Abstract
The invention discloses a driving right switching and collision early warning system for a man-machine co-driving intelligent networked vehicle, which comprises an environment sensing module, a positioning navigation module, a path planning module, a control right switching module, a collision early warning module and a control execution module; the environment perception module is used for collecting the surrounding environment information of the vehicle; the positioning navigation module acquires the position information and the state information of the vehicle; the path planning module carries out optimal path planning according to the surrounding environment information, the road information and the vehicle position information; the control right switching module judges whether the vehicle can be switched to the driving mode under the current condition according to the set safety criterion; and the collision early warning module judges whether the current moment of the vehicle has collision risk or not according to the improved safe time distance model and provides a corresponding scheme. The system has higher accuracy and real-time performance, and improves the safety and the intelligent level of the intelligent automobile.
Description
Technical Field
The invention relates to the field of intelligent vehicle collision avoidance, in particular to a collision early warning system considering vehicle behaviors.
Background
The notice of "2025 made in China" mentions the development of intelligent internet automobiles, which is a development idea of the countries in the next decade and is also a key for technical innovation in the automobile industry in China. The man-machine driving is the third development stage of the intelligent networked automobile, the collision early warning research aiming at the man-machine driving is developed, and the intelligent networked automobile is pushed to the fourth stage for improving the safety performance of the intelligent networked automobile in the stage: the unmanned automobile develops, follows and even surpasses the highest point of the international intelligent vehicle technology, and has important significance.
The man-machine co-driving means that a driver and an intelligent system are in the same loop, the control right of a vehicle is shared through decision, and the man-machine integrated cooperative control completes a driving task, and is an important stage in the intelligent internet automobile development process.
The man-machine co-driving type intelligent networked automobile has interference factors such as complex working conditions, multiple emergency situations and the like during collision early warning, and the traditional algorithm based on a safe distance model and collision time cannot solve the real-time performance required by collision early warning under different working conditions. On the other hand, the man-machine co-driving type intelligent networked automobile has higher requirements on the accuracy of collision early warning, and the traditional method is difficult to ensure the low false alarm rate and the low false alarm rate of the collision early warning under the complex working condition.
The vehicle collision early warning system is an important component of the automobile safety auxiliary driving technology, mainly utilizes a distance measuring sensor (radar and vision) to detect the running state of a vehicle in front, measures the longitudinal distance with the vehicle in front, judges whether the vehicle is safe to continue running within a period of time in the future by an algorithm according to the current working condition of the vehicle, and warns through sound, graphics or touch if collision risks exist. In an emergency situation, the system can actively carry out emergency braking, and the occurrence of collision accidents is reduced to the maximum extent. For example, when the driving is switched from the machine driving to the driver driving, the states of the vehicle and the vehicle ahead must be changed, and the traditional collision early warning system cannot timely cope with the situation and cannot ensure the accuracy and the real-time performance of the collision early warning system.
Disclosure of Invention
In order to solve the problems, the invention provides a collision early warning system considering vehicle behaviors under a man-machine co-driving system, which is characterized in that the vehicle behaviors are used as risk assessment factors to redesign the collision early warning system, so that the adaptation of the man-machine co-driving type intelligent networked automobile collision early warning system is realized, and the intellectualization and the safety level of an intelligent networked automobile can be improved.
The driving right switching mechanism and the collision early warning system for the man-machine co-driving intelligent networked vehicle comprise an environment sensing module, a positioning navigation module, a path planning module, a control right switching module, a collision early warning module and a control execution module, wherein the environment sensing module sends acquired surrounding environment information of the vehicle to the path planning module, the positioning navigation module sends acquired state of the vehicle to the path planning module, and the path planning module plans an optimal path according to the received information and sends the optimal path to the control execution module; the environment sensing module and the positioning navigation module respectively send the behavior of the front vehicle and the state of the vehicle to the control right switching module, and respectively send the relative distance and the relative speed between the vehicle and the front vehicle and the state of the vehicle to the collision early warning module; the control right switching module judges whether the driving right switching of the vehicle can be carried out under the current condition according to a set safety criterion, the collision early warning module judges whether the vehicle has a collision risk at the current moment according to an improved safety time interval model, if the collision risk exists, a driver is reminded of avoiding the vehicle in a man driving mode, the early warning information is sent to the path planning module in a machine driving mode, the path planning is carried out again, and meanwhile the early warning information is sent to the control execution module to control the vehicle.
In the above scheme, the control right switching module includes a state monitoring unit and a logic judgment unit, the state monitoring unit is configured to acquire a state of a vehicle ahead and a state of the vehicle, receive a switching signal of the mode switching switch and early warning information of the collision early warning module at the same time, and send the acquired information to the logic judgment unit, and the logic judgment unit judges whether the vehicle can perform driving mode switching according to three safety criteria, namely that the vehicle is in a following state and collision time is greater than 2 seconds, that the vehicle ahead has no other abnormal behavior, and that the collision early warning module transmits no collision risk.
In the above scheme, the mode switch is composed of 2 single-pole double-throw switches K11、K12When the switch is communicated with different contacts, the vehicle is in different driving modes.
In the above scheme, the improved safety time interval model is as follows:
where T is the time of collision, SrIs the relative distance between the front vehicle and the host vehicle, VrIs the relative speed, k, of the front vehicle and the host vehicle1In order to combine risk assessment factors, k, of the behavior of the vehicle2For risk assessment factors, k, combined with the recognition of the behaviour of the vehicle in front3Is the driving mode of the vehicle.
In the above scheme, the
Alpha is more than 1; wherein, conttion A represents the acceleration running of the vehicle, and conttion B represents the deceleration running of the vehicle.
In the above scheme, the
Beta is more than 0 and less than 1; wherein, contsionx represents that the front vehicle runs on the side lane and has the intention of entering the lane, contsiony represents that the front vehicle runs on the side lane and has no intention of entering the lane, and contsionz represents that the front vehicle runs on the lane.
In the above scheme, the
Where contsionp indicates that the host vehicle is in the machine driving mode, and contsionq indicates that the host vehicle is in the driver driving mode.
In the above scheme, the environment sensing module comprises an industrial camera and a laser radar, the camera collects a traffic sign board, a lane line and a traffic signal lamp, and the laser radar detects obstacle information, a behavior of a vehicle ahead and a relative distance and a relative speed between the vehicle and the vehicle ahead.
In the scheme, the positioning navigation module comprises a GPS inertial navigation system and an inertial navigation inter + and is used for collecting vehicle position information and a course angle.
The invention has the beneficial effects that:
1. the invention provides a collision early warning system suitable for a man-machine co-driving type intelligent automobile, which promotes the development of an unmanned vehicle towards the directions of safety and intelligence to a certain extent;
2. the invention is not limited to consider the relative distance and relative speed of two vehicles when designing the collision early warning system, but considers the transverse behavior of the front vehicle and the longitudinal behavior of the vehicle, and utilizes the longitudinal behavior characteristic of the vehicle, the transverse behavior characteristic of the front vehicle and the state information of the vehicle to comprehensively judge the dangerous situation of the current vehicle;
3. in the aspect of intelligent networked automobiles, the intelligent networked automobile safety warning system not only pays attention to the active safety of the intelligent networked automobiles, but also pays attention to the smoothness of the intelligent networked automobiles, reduces unnecessary braking in the driving process and reduces the impact on passengers by improving the warning precision of the collision warning system, ensures that the automobile has higher smoothness, and improves the riding comfort of the passengers.
Drawings
FIG. 1 is a schematic structural diagram of a collision warning system according to the present invention;
FIG. 2 is a diagram of an environment awareness module arrangement according to the present invention;
FIG. 3 is a schematic diagram of a mode switch according to the present invention;
FIG. 4 is a schematic diagram of a task area of the intelligent water droplet algorithm of the present invention;
FIG. 5 is a flow chart of a path planning algorithm of the present invention;
fig. 6 is a schematic diagram of an internal structure of the control right switching module according to the present invention.
Detailed Description
The technical scheme of the invention will be described in detail in the following with reference to the accompanying drawings.
As shown in fig. 1, the driving right switching and collision early warning system for the human-computer co-driving intelligent networked vehicle comprises an environment sensing module, a mode switching switch, a positioning navigation module, a path planning module, a control right switching module, a collision early warning module and a control execution module.
The environment sensing module comprises an industrial camera, a 64-line laser radar (HDL-64E), a 4-line laser radar (ibeo LUX) and a single-line laser radar (SICK291/511), wherein as shown in FIG. 2, the 64-line laser radar is positioned at the longitudinal symmetry axis of the top of the vehicle and mainly collects information of long-distance obstacles; the two 4-line laser radars are arranged below the head lamp of the vehicle and used for detecting middle-distance obstacles; the single-line laser radar is arranged at the center of the head of the vehicle and used for detecting a short-distance obstacle; the laser radar is also used for acquiring the behavior (position information and course angle) of the front vehicle, the relative distance between the vehicle and the front vehicle and the relative speed; the camera is arranged at the position of the rearview mirror in the vehicle and mainly collects information of a traffic sign board, a lane line and a traffic signal lamp.
The mode switch is composed of 2 single-pole double-throw switches K11、K12The specific functional diagram of the composition is shown in fig. 3. Switch K11、K12There are only 3 cases: (1) switch K11And K12The contact 2 and the contact 3 are communicated respectively in a default state, and the driver is in a driving mode at the moment; (2) switch K11And K12When the contact 2 and the contact 4 are communicated respectively, the man-machine driving mode is in the moment; (3) switch K11And K12When the contact 1 and the contact 4 are communicated respectively, the machine driving mode is in the moment. Switch K11Arranged near the steering wheel or the center console and controlled to be turned off by the driver, a switch K12The switching of the driving modes is controlled by a logic judgment unit in the control right switching module through a CAN signal, so that the switching of the driving modes is realized.
The positioning and navigation module comprises a GPS inertial navigation system and an inertial navigation inter +, which are positioned at the longitudinal symmetrical axis of the top of the vehicle, as shown in FIG. 2, and are mainly used for collecting vehicle position information and a heading angle (vehicle behavior information).
The GPS inertial navigation system and the inertial navigation inter + respectively transmit the position information and the course angle information of the vehicle to the path planning module, the camera transmits the identified lane lines, traffic signal lamps and traffic sign information to the path planning module, the radar transmits the detected information of obstacles such as pedestrians, vehicles and the like to the path planning module, the sensing module transmits the behavior of the vehicle ahead to the control right switching module, and the radar in the sensing module transmits the behavior of the vehicle ahead, the relative distance and the relative speed between the vehicle and the vehicle ahead to the collision early warning module.
The path planning module plans an effective path which is free of collision and can safely reach a target point, acquires road information (including road and road congestion conditions) based on an external high-precision map in a machine driving mode, performs path planning by using an Intelligent Water drop algorithm (IWD), obtains an optimal path, and transmits the optimal path to the control execution module. In the intelligent water drop model, a path starting point is B, a target point is A, q obstacles are in a target area, and coordinates of a center point of the obstacle are (x)j，yj) (j ═ 1, …, q), OA is the x axis, OB is the y axis, OA is divided equally into m parts, OB and OC are divided equally into n parts, there are (m-1) vertical lines between node B and node a, (m-1) vertical lines and (2n +1) horizontal lines intersect to form (m-1) × (2n +1) nodes, as shown in fig. 4. Each node has a coordinate of Li(xi，yki) (i-1, …, m-1; ki 1, …,2n +1), the path from the originating node B to the target node a is described by the sub-formula:
Path＝{B，L1(x1，yk1)，L2(x2，yk2)，...，Lm-1(xm-1，yki)，A} (1)
defining an objective function J in trajectory planning, the formula is as follows:
in the formula: l iskIs the movement distance of node i, diminDistance of node i to nearest obstacleδ is a threat avoidance coefficient;
distance of movement LkThe formula of (1) is as follows:
distance d from node i to nearest obstacleiminThe formula of (1) is as follows:
in the formula: r isi(i-1, …, q) is the danger radius of the obstacle.
In the intelligent water drop model, the intelligent water drop has two attributes, namely a motion speed viwdAnd a sand content soiliwdTherein soiliwdIs the amount of silt missing relative to the horizon (negative); defining: the current position of a certain intelligent water drop is i, and the current position of the intelligent water drop moves to the next position of j. As shown in fig. 5, the method specifically includes the steps of:
step (1), initializing static parameters: setting the number of Water drops Iiwd；
Step (2), initializing dynamic parameters: the speed of each intelligent water drop is set to be constant InitVel, and the initial soil of each intelligent water drop is set to be zero;
step (3), placing each water drop on a starting point;
and (4) selecting the next point according to the probability selection strategy: p (i, j) represents the probability of selecting the position j as the next position when the intelligent water drop is at the position i, and the relation between P (i, j) and the sediment content soil (i, j) of the path (i, j) is as follows:
in the formula: eta (i, j) is the distance of location j to the destination, and Q is a distance-related heuristic Performance index.
And (5) calculating the water drop speed increment and updating the water drop speed: Δ viwdRepresents the velocity increase of the water droplets, which is inversely proportional to the silt content soil (i, j) of the path (i, j) in a non-linear manner, and is expressed by the following formula:
in the formula: a isv、bvAnd cvIs a self-defined coefficient.
And (6) calculating the variation of the sediment: amount of sand Δ soil washed away by water dropletsiwdThe non-linearity is inversely proportional to the time variable time (i, j, v) required for a water drop to traverse a path (i, j)iwd) Equal to the amount of silt Δ soil (i, j) of the path (i, j) reduction, as follows:
Δsoil(i，j)＝Δsoiliwd (8)
in the formula: a iss、bsAnd csIs a self-defined coefficient;
additional time (i, j, v)iwd) Is the time taken for the drop to move from location i to location j, and is given by the formula:
and (7) updating the silt amount of the passed road section: after the water drop arrives at the position j from the position i, the sediment amount contained in the road section (i, j) is updated, a feedback mechanism is formed for the movement of other water drops, and the sediment amount local road section updating formula is as follows:
soil(i，j)＝soil(i，j)-ρ·Δsoil(i，j) (11)
in the formula: rho is a coefficient between 0 and 1.
To cultivateThe ability of cultivating a group of water drops to search for an optimal path, and designing a feedback mechanism to iteratively solve the optimal solution TIBThe global road section sediment amount is updated by the following formula:
in the formula: n is a radical ofIBIs the number of nodes of the path,
the amount of silt contained in the water drop with the optimal path in the water drops in the group.
And (8) calculating and updating the optimal path: when all the water drops have the paths thereof according to the steps (4) to (7), the paths are calculated according to the formula (1) and T is usedIBRepresents;
optimal path solution TTBThe update is made according to the following formula:
in the formula: q (.) is an evaluation function used for selecting the optimal solution in the water drops of the group.
And (9) outputting a result: and (4) if the iteration number Tter reaches the maximum iteration number Ttermax, outputting the optimal path, otherwise, turning to the step (2).
As shown in fig. 6, the control right switching module includes a state monitoring unit, a logic determination unit, a state delay holding unit and a steering control execution unit, where the state monitoring unit obtains the behavior of the vehicle ahead (including following behavior, passing parallel behavior and other abnormal behaviors) through the radar of the sensing module on one hand, and obtains the speed and acceleration of the vehicle on the other hand through the inertial navigation inter +, and receives the switching signal of the mode switch at the same time, and then sends the states, the behaviors of the vehicle and the vehicle ahead and the mode switching signal to the logic determination unit; the logic judgment unit judges whether the vehicle can carry out driving mode switching or not according to three safety criteria of 'the vehicle is in a following state and the collision time is more than 2 seconds', 'no other abnormal behaviors of the front vehicle' and no collision risk transmitted by the collision early warning module; the state delay holding unit keeps the driving state before the control right of the vehicle is switched unchanged in the control right switching process of the vehicle, so that the driving safety of the vehicle is ensured; the steering control execution unit is mainly a device capable of ensuring the steering smooth control of the vehicle under the driving modes of a driver and a machine, the device is modified on the basis of the original vehicle steering system, and the scheme is as follows: the original vehicle transmission mechanism is reserved, a German motor is used for replacing an original vehicle motor, a corner sensor is added, a German controller is used for replacing an original vehicle controller, and a CAN communication interface is added; the control of the steering control execution unit under different modes is realized through the added CAN communication port.
The collision early warning module considers the transverse and longitudinal behavior characteristics of the front vehicle and the vehicle state on the basis of the traditional safety time distance model, takes the vehicle state and the transverse behavior of the front vehicle as risk assessment factors, and defines a new safety time distance model.
The control execution module modifies steering, braking and an accelerator on the basis of the original vehicle, and is used for controlling the accelerator, the brake, the steering and other mechanisms to realize the automatic driving function of the vehicle in a driving state, or ensuring the absolute control right of a driver in a driving state. And the control execution module is used for respectively outputting throttle opening, brake master cylinder pressure and front wheel steering angle signals to control an accelerator, a brake and a turning system according to the optimal path given by the path planning module and by combining the early warning information input by the collision early warning module, so that the automatic driving function of the vehicle is realized.
A collision early warning method considering vehicle behaviors under a man-machine driving system comprises the following steps:
step 1), a sensing module collects surrounding environment information of a vehicle (lane line information, traffic sign information, barrier information of pedestrians, non-motor vehicles and the like), a positioning navigation module obtains the state of the vehicle, and the information is sent to a path planning module;
step 2), the path planning module distinguishes the barrier area and the free area according to the received information, then sets a starting point and a target point in the path planning module, plans an optimal path which has no collision and can safely reach the target point, and sends the path to the control execution module;
step 3), when the control execution module executes the optimal path, the sensing module sends the behavior of the front vehicle to the control right switching module, and sends the behavior of the front vehicle and the relative distance and relative speed between the vehicle and the front vehicle to the collision early warning module; the positioning navigation module sends the state of the vehicle to the control right switching module and the collision early warning module;
step 4), a state monitoring unit in the control right switching module acquires information sent by the sensing module, the positioning navigation module and the collision early warning module and transmits the information to a logic judgment unit, and the logic judgment unit switches a mode switch K when a driver triggers the mode11When the driving mode of the vehicle is required to be switched, according to the state of the vehicle and the behavior of the front vehicle, judging whether the vehicle is allowed to switch driving rights under the current condition in the process of executing the optimal path according to three safety criteria of ' the vehicle is in a vehicle following state and the collision time is more than 2 seconds ', ' the front vehicle has no other abnormal behaviors ' and no collision risk transmitted by a collision early warning module ', if the safety criteria are met, switching the driving mode, and meanwhile, a state delay keeping unit controls the vehicle to keep the current state to run in the process of switching the driving mode, and if the safety criteria are not met, switching the driving mode is forbidden, and a steering control execution unit controls the front wheel corner of the vehicle under different driving modes to ensure the normal running of the vehicle;
step 5), the collision early warning module obtains an improved safe time distance model on the basis of a traditional safe time distance model according to the front vehicle behavior and the vehicle state obtained by the sensing module and the positioning navigation module and takes the vehicle state and the front vehicle behavior as risk assessment factors, judges whether the current moment of the vehicle has a collision risk, reminds a driver to avoid in a driving mode of the driver if the collision risk exists, transmits early warning information to the path planning module in a machine driving mode, replans path planning, and simultaneously sends a braking signal to the control execution module to control the vehicle;
the improved safety time distance model is concretely as follows:
in the formula: t is the time of collision, SrThe relative distance between the front vehicle and the host vehicle is in the unit of m; vrThe relative speed of the front vehicle and the host vehicle is shown in the unit of m/s; k is a radical of1In order to combine the risk evaluation factors of the state of the vehicle, k is defined because the risk of collision with the front vehicle is increased under the condition that the vehicle accelerates, and the risk of collision is reduced or even does not collide under the condition that the vehicle decelerates1Different values in case of acceleration and deceleration, in particular:
in the formula: conttion A represents the accelerated running of the vehicle, conttion B represents the decelerated running of the vehicle, and the value of alpha can be set according to the actual condition of the vehicle;
k2in order to combine the risk evaluation factors of the behavior recognition result of the front vehicle, considering that not only the longitudinal behavior characteristics of the front vehicle have influence on the collision risk evaluation, but also the transverse behavior of the front vehicle has important significance on the collision risk evaluation, if the lane change of the vehicle in the adjacent lane to the vehicle lane only considers the longitudinal behavior characteristics of the vehicle, the potential collision risk cannot be accurately judged, so the transverse characteristics, k and k of the vehicle must be combined2The method specifically comprises the following steps:
in the formula: contsionx indicates that the front vehicle is running in the side lane and has an intention to enter the lane, contsiony indicates that the front vehicle is running in the side lane and has no intention to enter the lane, contsionz indicates that the front vehicle is running in the lane, and the value of beta can be set according to the actual condition of the front vehicle;
k 3representing the driving mode of the vehicle, because a certain time is required from the moment that a driver faces a critical situation to make a corresponding evasive action in the man driving mode, and the reaction time from the moment that the driver faces the critical situation to make the evasive action in the man driving mode is in millisecond order, the values of k 3 in two different driving modes are defined as follows:
in the formula: contsionp indicates that the vehicle is in a machine driving mode, i.e., a machine driving mode; condtion q indicates that the host vehicle is in the driver driving mode, i.e., the human driving mode.
Giving an early warning according to the performance requirement and the test procedure of the vehicle forward collision early warning system of the GB/T33577 intelligent transportation system: in a machine driving mode, a collision warning (T <0.2) and a collision early warning (T <3.5 > is more than or equal to 0.2) respectively represent that a driver is informed to immediately take a risk avoiding measure and a control execution module prepares to take the risk avoiding measure, for example, the control execution module performs emergency braking during the collision warning, the collision early warning module sends early warning information to a path planning module, performs path planning again and simultaneously sends the early warning information to the control execution module to control the vehicle; under the driving mode of a driver, collision warning (T <1) and collision early warning (T <3.5 > is more than or equal to 1) respectively represent that the driver is informed to immediately take risk avoiding measures and prepare to take risk avoiding measures, for example, a warning sound is played to remind the driver of collision risk; under the man-machine common driving mode, collision warning (T is less than 0.2) and collision early warning (T is more than or equal to 0.2 and less than 3.5) respectively represent that a driver is informed to immediately take risk avoiding measures and a control execution module prepares to take the risk avoiding measures, for example, the control execution module performs speed reduction to avoid possible collision risks during collision early warning; and the collision early warning module sends the early warning information (risk avoidance measures) to the control execution module.
And 6), the control execution module controls mechanisms such as an accelerator, a brake and a steering mechanism to realize the automatic driving function of the vehicle in the mechanical driving mode, or ensures the absolute control right of the driver in the human driving mode.
The foregoing is a description of the preferred embodiments of the present invention, which is provided for the purpose of illustrating the design concepts and features of the present invention and is provided for the purpose of enabling those skilled in the art to practice the present invention based on the description of the present invention, and the protection scope of the present invention is not limited to the above embodiments. Therefore, any modification, equivalent replacement, or improvement made under the technical idea of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. The system is characterized by comprising an environment sensing module, a positioning navigation module, a path planning module, a control right switching module, a collision early warning module and a control execution module, wherein the environment sensing module sends acquired surrounding environment information of the vehicle to the path planning module; the environment sensing module and the positioning navigation module respectively send the behavior of the front vehicle and the state of the vehicle to the control right switching module, and respectively send the relative distance and the relative speed between the vehicle and the front vehicle and the state of the vehicle to the collision early warning module; the control right switching module judges whether the driving right switching can be carried out under the current condition of the vehicle according to a set safety criterion, the collision early warning module judges whether the current moment of the vehicle has a collision risk according to an improved safety time distance model, if the collision risk exists, a driver is reminded of avoiding the collision in a man driving mode, early warning information is sent to the path planning module in a machine driving mode, path planning is carried out again, and meanwhile the early warning information is sent to the control execution module to control the vehicle;
the improved safety time distance model comprises the following steps:
where T is the time of collision, SrIs the relative distance between the front vehicle and the host vehicle, VrIs a front vehicleRelative speed with respect to the host vehicle, k1In order to combine risk assessment factors, k, of the behavior of the vehicle2For risk assessment factors, k, combined with the recognition of the behaviour of the vehicle in front3The driving mode of the vehicle is set;
the above-mentioned
Wherein, conttion A represents the accelerated running of the vehicle, and conttion B represents the decelerated running of the vehicle;
the above-mentioned
Wherein, contsionX represents that the front vehicle runs on the side lane and has the intention of entering the lane, contsionY represents that the front vehicle runs on the side lane and has no intention of entering the lane, and contsionZ represents that the front vehicle runs on the lane;
the above-mentioned
Where contsionp indicates that the host vehicle is in the machine driving mode, and contsionq indicates that the host vehicle is in the driver driving mode.
2. The system for switching driving right and early warning collision of the man-machine co-driving-oriented intelligent networked vehicle as claimed in claim 1, wherein the control right switching module comprises a state monitoring unit and a logic judgment unit, the state monitoring unit is configured to acquire a state of the vehicle ahead and a state of the vehicle, receive a switching signal of the mode switching switch and early warning information of the collision early warning module, and send the acquired information to the logic judgment unit, and the logic judgment unit judges whether the vehicle can perform driving mode switching according to three safety criteria of "the vehicle is in a following state and the collision time is more than 2 seconds", "the vehicle ahead has no other abnormal behaviors" and no collision risk transmitted by the collision early warning module.
3. The human-computer co-driving oriented intelligent networked vehicle driving right switching method according to claim 2And a collision early warning system, characterized in that the mode switch is composed of 2 single-pole double-throw switches K11、K12When the switch is communicated with different contacts, the vehicle is in different driving modes.
4. The human-computer co-driving-oriented intelligent networked vehicle driving right switching and collision early warning system as claimed in claim 1, wherein the environment sensing module comprises an industrial camera and a laser radar, the camera collects traffic signs, lane lines and traffic signal lamps, and the laser radar detects obstacle information, behaviors of vehicles in front and relative distance and relative speed between the vehicle and the vehicles in front.
5. The human-computer co-driving oriented intelligent networked vehicle driving right switching and collision early warning system as claimed in claim 1, wherein the positioning navigation module comprises a GPS inertial navigation system and an inertial navigation inter + for collecting vehicle position information and a course angle.
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| CN112092805B (en) * | 2020-09-23 | 2021-10-19 | 北京理工大学 | Integrated control method and system for collision avoidance and collision damage reduction of intelligent vehicle |
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| CN113223325A (en) * | 2021-03-26 | 2021-08-06 | 南京市德赛西威汽车电子有限公司 | Method for safely passing signal-lamp-free intersection |
| CN113147754A (en) * | 2021-05-29 | 2021-07-23 | 深圳市平衡力科技有限公司 | Anti-collision system for electric scooter |
| CN114141019B (en) * | 2021-12-15 | 2023-03-28 | 阿波罗智联(北京)科技有限公司 | Traffic control method, apparatus, medium, and program product |
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