Collaborative driving management system of heavy truck fleet

Abstract

The invention discloses a cooperative driving management system for a heavy truck fleet, relates to the technical field of unmanned driving, and solves the problem that the driving perception and obstacle avoidance capability of the fleet are poor due to track errors between single vehicles. And the technical problems of sensing blind areas and too short sensing distance of bicycle sensing often exist are solved. Comprising a fleet decision-making module used for making an overall driving path and a driving strategy of a heavy truck fleet according to traffic flow information, road condition information and meteorological condition data received by a cloud server; the strategy correction module is used for summarizing and fusing the obstacle information collected by each vehicle in the vehicle team to obtain the obstacle information around the vehicle team, so that the driving path and the driving strategy of each vehicle in the vehicle team are corrected, and the precision of cooperative control of the vehicle team is improved; the vehicle tracking module is used for tracking the vehicle distance related parameters of the single vehicle in the vehicle team to carry out strategy deviation index analysis so as to judge whether the vehicle deviates from the vehicle distance control strategy or not, and therefore the cooperative control efficiency of the vehicle team is improved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned driving technology, and in particular to a collaborative driving management system for a heavy truck fleet. Background Art

[0002] The purpose of platoon cooperative driving is to ensure road traffic safety and efficiency, make full use of road conditions, and form a platoon of several vehicles so that they can complete relevant cooperative strategies such as cruising and following in a collaborative manner according to different traffic conditions. The platoon cooperative driving technology requires that the vehicles in the platoon can exchange information in real time so that all vehicles in the platoon can maintain consistent behavior, such as accelerating and decelerating at the same time to maintain the following distance, and changing lanes at the same time to maintain the platoon shape.

[0003] Due to the limitations of the braking and movement capabilities of the fleet vehicles, the number of obstacles will change and different obstacles will block each other, and there will be information interaction between vehicles, which will cause unacceptable trajectory prediction errors, that is, the error between the predicted trajectory and the actual trajectory, resulting in poor driving perception and obstacle avoidance capabilities of the fleet; at the same time, due to the limitations of sensor technology, single-vehicle perception often has problems such as blind spots and short perception distances, which is not conducive to making decisions on the overall driving behavior of the fleet; based on the above shortcomings, the present invention proposes a collaborative driving management system for a heavy truck fleet. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a collaborative driving management system for a heavy truck fleet.

[0005] To achieve the above-mentioned object, a first aspect of the present invention provides a collaborative driving management system for a heavy truck fleet, comprising a traffic perception module, a cloud server, an information acquisition module, a fleet decision module, a strategy correction module, and a vehicle tracking module;

[0006] The traffic sensing module is a number of roadside base stations distributed in all directions of the road, which are used to sense traffic flow information and share the traffic flow information to the cloud server;

[0007] The information acquisition module is used to acquire the road condition information and weather condition data within the coverage area of ​​the roadside base station through the information exchange channel, and share the road condition information and weather condition data to the cloud server;

[0008] The fleet decision module is used to formulate the overall driving path and driving strategy of the heavy truck fleet according to the traffic flow information, road condition information and weather condition data received by the cloud server, so that each vehicle in the fleet drives according to the driving path and driving strategy;

[0009] The strategy correction module is used to aggregate the obstacle information collected by each vehicle in the fleet and fuse them to obtain the obstacle information around the fleet; and according to the obstacle information around the fleet, correct the driving path and driving strategy of each vehicle in the fleet;

[0010] When a single vehicle in a convoy drives according to the driving path and driving strategy, a vehicle spacing control strategy is executed at the same time; the vehicle spacing control strategy is: fixing the headway time, that is, controlling the time when the heads of two consecutive vehicles in a convoy driving on the same longitudinal lane pass a certain point to be consistent;

[0011] The vehicle tracking module is used to track the vehicle spacing correlation parameters of a single vehicle in the fleet to perform strategy deviation index Cz analysis to determine whether the vehicle deviates from the vehicle spacing control strategy;

[0012] If the strategy deviation index Cz is greater than the preset deviation threshold, a strategy out-of-control signal is generated and sent to the cloud server to remind the safety officer to adjust the driving trajectory and speed of the corresponding vehicle to avoid leaving the fleet.

[0013] Furthermore, the specific analysis steps of the vehicle tracking module are:

[0014] Collecting vehicle spacing related parameters of a single vehicle, wherein the vehicle spacing related parameters include vehicle speed, front vehicle speed, rear vehicle speed, and the distance between the vehicle and the front and rear vehicles;

[0015] The vehicle speed is marked as Vt, the speed of the front vehicle is marked as Vq, and the speed of the rear vehicle is marked as Vh; using the formula Calculate and obtain the speed deviation value VL of a single vehicle;

[0016] The distance between the vehicle and the vehicle in front is marked as Lq, and the distance between the vehicle and the vehicle behind is marked as Lh;

[0017] Using the formula Calculate the time headway deviation value Ls of a single vehicle; where η is the preset headway; normalize the speed deviation value and the time headway deviation value and take their values;

[0018] The strategy deviation index Cz of a single vehicle is calculated using the formula Cz=VL×a1+Ls×a2, where a1 and a2 are both preset coefficient factors.

[0019] Furthermore, the specific working steps of the strategy modification module are as follows:

[0020] The on-board processor collects information collected by various sensors on the corresponding vehicle, processes the information collected by the various sensors, and obtains obstacle information sensed by the various sensors; wherein the on-board processor corresponds to the vehicle in the fleet;

[0021] The obstacle information sensed by each sensor is integrated to obtain the obstacle information around the corresponding vehicle; the obstacle information includes the position, speed, size, category, acceleration, and number of frames tracked of the obstacle;

[0022] The strategy correction module integrates the obstacle information around each vehicle in the fleet to obtain the obstacle information around the fleet; then, based on the obstacle information around the fleet, the driving path and driving strategy of each vehicle in the fleet are corrected.

[0023] Furthermore, the driving strategy includes acceleration, deceleration, lane change, merging, and braking.

[0024] Furthermore, the traffic flow information includes vehicle travel direction, travel speed and vehicle type, which is used to reflect the traffic congestion situation on the road.

[0025] Furthermore, the road condition information includes lane width, number of lanes, road slope, road curvature, traffic accident information, construction information, traffic control information, and speed limit information; the meteorological condition data includes rainfall conditions, snowfall conditions, wind level, haze conditions, and visibility.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. The fleet decision module in the present invention is used to formulate the overall driving path and driving strategy of the heavy truck fleet according to the traffic flow information, road condition information and weather condition data received by the cloud server, so that each vehicle in the fleet drives according to the driving path and driving strategy; the strategy correction module is used to aggregate the obstacle information collected by each vehicle in the fleet and fuse it to obtain the obstacle information around the fleet; and according to the obstacle information around the fleet, the driving path and driving strategy of each vehicle in the fleet are corrected, thereby improving the accuracy of the fleet collaborative control;

[0028] 2. In the present invention, when a single vehicle in a fleet travels according to the driving path and driving strategy, it also executes the vehicle spacing control strategy; the vehicle tracking module is used to track the vehicle spacing associated parameters of a single vehicle in the fleet to perform strategy deviation index Cz analysis to determine whether the vehicle deviates from the vehicle spacing control strategy, thereby reminding the safety officer to adjust the driving trajectory and speed of the corresponding vehicle in time to avoid leaving the fleet, thereby improving the efficiency of fleet collaborative control. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 This is a system block diagram of a collaborative driving management system for a heavy truck fleet of the present invention. DETAILED DESCRIPTION

[0031] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] See also Figure 1 , the first aspect of the present invention provides a collaborative driving management system for a heavy truck fleet, including a traffic perception module, a cloud server, an information acquisition module, a fleet decision module, a strategy correction module and a vehicle tracking module;

[0033] The traffic sensing module is a number of roadside base stations distributed in all directions of the road, where the roadside base stations are set at preset intervals to sense traffic flow information and share the traffic flow information to the cloud server; the traffic flow information includes the direction of vehicle travel, the speed of vehicle travel and the type of vehicle, which is used to reflect the traffic congestion on the road;

[0034] The information acquisition module is used to obtain the road condition information and weather condition data within the coverage area of ​​the roadside base station through the information exchange channel, and share the road condition information and weather condition data to the cloud server;

[0035] Road condition information includes but is not limited to: lane width, number of lanes, road slope, road curvature, traffic accident information (such as the section where the accident occurred, the severity of the situation, the progress of rescue, whether traffic is prohibited, etc.), construction information (such as the section under construction, construction time, whether traffic is prohibited, etc.), traffic control information (such as restricted sections, restricted time, etc.), speed limit information;

[0036] Meteorological data include but are not limited to: rainfall (such as rainfall range, rainfall amount, duration), snowfall (such as snowfall range, snowfall amount, duration), wind level, fog / smog (such as impact range, duration, pollution level), visibility, etc.;

[0037] The fleet decision module is used to formulate the overall driving route and driving strategy of the heavy truck fleet based on the traffic flow information, road condition information and weather condition data received by the cloud server, so that each vehicle in the fleet drives according to the driving route and driving strategy; the driving strategy includes but is not limited to acceleration, deceleration, lane change, lane merging, braking, etc.;

[0038] In this embodiment, due to the limitations of the braking and movement capabilities of the convoy vehicles, the number of obstacles will change and different obstacles will block each other, and there will be information interaction between vehicles, which will cause trajectory errors between individual vehicles;

[0039] Therefore, the strategy correction module is used to aggregate the obstacle information collected by each vehicle in the fleet and fuse them to obtain the obstacle information around the fleet; and according to the obstacle information around the fleet, the driving path and driving strategy of each vehicle in the fleet are corrected; the specific steps are as follows:

[0040] The on-board processor collects information collected by various sensors on the corresponding vehicle, and processes the information collected by each sensor to obtain obstacle information sensed by each sensor; wherein the on-board processor corresponds to the vehicle in the fleet;

[0041] The obstacle information sensed by each sensor is integrated to obtain the obstacle information around the corresponding vehicle; the obstacle information includes one or more of the following information of the obstacle: position, speed, size, category, acceleration, and number of frames tracked;

[0042] The on-board processor is used to send the obstacle information around the corresponding vehicle to the strategy correction module through the wireless communication unit; the strategy correction module integrates the obstacle information around each vehicle in the fleet, obtains the obstacle information around the fleet, and corrects the driving path and driving strategy of each vehicle in the fleet according to the obstacle information around the fleet;

[0043] This application performs secondary fusion of sensor information collected by all vehicles in the fleet to obtain obstacle information for the entire fleet, which is conducive to making collaborative control decisions for the entire fleet and improving the accuracy of the fleet's collaborative control. It not only overcomes the problems of blind spots and short perception distances of single-vehicle sensor perception, but also has lower configuration requirements for each vehicle in the fleet, thereby improving the fleet's flexible scheduling capabilities;

[0044] A further technical solution is that when a single vehicle in a convoy is driving according to the driving path and driving strategy, it also implements a vehicle spacing control strategy; the vehicle spacing control strategy is: fixed headway, that is, controlling the time when the heads of two consecutive vehicles in a convoy driving on the same longitudinal lane pass a certain point to be consistent; in this control strategy, the expected vehicle spacing between adjacent vehicles in a convoy is determined by the headway and vehicle speed together;

[0045] The vehicle tracking module is used to track the inter-vehicle distance correlation parameters of a single vehicle in the fleet to perform strategy deviation index Cz analysis to determine whether the vehicle deviates from the inter-vehicle distance control strategy; the specific analysis steps are:

[0046] Collect the inter-vehicle distance parameters of a single vehicle, which include the vehicle speed, the speed of the front vehicle, the speed of the rear vehicle, and the distance between the vehicle and the front and rear vehicles. The vehicles here do not include the first and last vehicles in the convoy. The first and last vehicles have safety officers to realize unmanned driving of the convoy.

[0047] The speed of the vehicle is marked as Vt, the speed of the front vehicle is marked as Vq, and the speed of the rear vehicle is marked as Vh; the distance between the vehicle and the front vehicle is marked as Lq, and the distance between the vehicle and the rear vehicle is marked as Lh;

[0048] Using the formula Calculate and obtain the speed deviation value VL of a single vehicle;

[0049] Using the formula The time headway deviation value Ls of a single vehicle is calculated; wherein η is the preset headway;

[0050] The speed deviation value and the time distance deviation value are normalized and their values ​​are taken, and the strategy deviation index Cz of a single vehicle is calculated using the formula Cz=VL×a1+Ls×a2, where a1 and a2 are both preset coefficient factors; a1 takes the value (0, 0.1], a2 takes the value (0.11, 0.2];

[0051] The strategy deviation index Cz is compared with the preset deviation threshold. If the strategy deviation index Cz is greater than the preset deviation threshold, it means that the vehicle has deviated from the vehicle spacing control strategy, and a strategy out-of-control signal is generated to the cloud server to remind the safety officer to adjust the driving trajectory and speed of the corresponding vehicle to avoid leaving the fleet and improve the efficiency of fleet collaborative control.

[0052] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0053] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0054] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A collaborative driving management system for a heavy truck fleet, characterized in that: It includes traffic perception module, information acquisition module, fleet decision module, strategy correction module and vehicle tracking module; The traffic sensing module is a number of roadside base stations distributed in all directions of the road, which are used to sense traffic flow information and share the traffic flow information to the cloud server; The information acquisition module is used to acquire the road condition information and weather condition data within the coverage area of ​​the roadside base station through the information exchange channel, and share the road condition information and weather condition data to the cloud server; The fleet decision module is used to formulate the overall driving path and driving strategy of the heavy truck fleet according to the traffic flow information, road condition information and weather condition data received by the cloud server, so that each vehicle in the fleet drives according to the driving path and driving strategy; The strategy correction module is used to aggregate the obstacle information collected by each vehicle in the fleet and fuse them to obtain the obstacle information around the fleet; and correcting the driving path and driving strategy of each vehicle in the convoy according to obstacle information around the convoy; When a single vehicle in a convoy drives according to the driving path and driving strategy, a vehicle spacing control strategy is executed at the same time; the vehicle spacing control strategy is: fixing the headway time, that is, controlling the time when the heads of two consecutive vehicles in a convoy driving on the same longitudinal lane pass a certain point to be consistent; The vehicle tracking module is used to track the vehicle spacing correlation parameters of a single vehicle in the fleet to perform strategy deviation index Cz analysis to determine whether the vehicle deviates from the vehicle spacing control strategy; If the strategy deviation index Cz is greater than the preset deviation threshold, a strategy out-of-control signal is generated and sent to the cloud server to remind the safety officer to adjust the driving trajectory and speed of the corresponding vehicle to avoid leaving the fleet.

2. A collaborative driving management system for a heavy truck fleet according to claim 1, characterized in that: The specific analysis steps of the vehicle tracking module are: Collecting vehicle spacing related parameters of a single vehicle, wherein the vehicle spacing related parameters include vehicle speed, front vehicle speed, rear vehicle speed, and the distance between the vehicle and the front and rear vehicles; The vehicle speed is marked as Vt, the speed of the front vehicle is marked as Vq, and the speed of the rear vehicle is marked as Vh; using the formula Calculate and obtain the speed deviation value VL of a single vehicle; The distance between the vehicle and the vehicle in front is marked as Lq, and the distance between the vehicle and the vehicle behind is marked as Lh; Using the formula Calculate the time headway deviation value Ls of a single vehicle; where η is the preset headway; normalize the speed deviation value and the time headway deviation value and take their values; The strategy deviation index Cz of a single vehicle is calculated using the formula Cz=VL×a1+Ls×a2, where a1 and a2 are both preset coefficient factors.

3. A collaborative driving management system for a heavy truck fleet according to claim 1, characterized in that: The specific working steps of the strategy correction module are as follows: The on-board processor collects information collected by various sensors on the corresponding vehicle, processes the information collected by the various sensors, and obtains obstacle information sensed by the various sensors; wherein the on-board processor corresponds to the vehicle in the fleet; The obstacle information sensed by each sensor is integrated to obtain the obstacle information around the corresponding vehicle; the obstacle information includes the position, speed, size, category, acceleration, and number of frames tracked of the obstacle; The strategy correction module integrates the obstacle information around each vehicle in the fleet to obtain the obstacle information around the fleet; then, based on the obstacle information around the fleet, the driving path and driving strategy of each vehicle in the fleet are corrected.

4. A collaborative driving management system for a heavy truck fleet according to claim 1, characterized in that: The driving strategy includes acceleration, deceleration, lane changing, merging, and braking.

5. The collaborative driving management system for a heavy truck fleet according to claim 1, characterized in that: The traffic flow information includes vehicle travel direction, travel speed and vehicle type, and is used to reflect the traffic congestion situation on the road.

6. The collaborative driving management system for a heavy truck fleet according to claim 1, characterized in that: The road condition information includes lane width, number of lanes, road slope, road curvature, traffic accident information, construction information, traffic control information, and speed limit information; the meteorological condition data includes rainfall conditions, snowfall conditions, wind level, haze conditions, and visibility.

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