Studies on intelligent vehicles, among which the controlling method of intelligent vehicles is a key technique, have drawn the attention of industry and the academe. This study focuses on designing an intelligent lateral control algorithm for vehicles at various speeds, formulating a strategy, introducing the Gauss cloud model and the cloud reasoning algorithm, and proposing a cloud control algorithm for calculating intelligent vehicle lateral offsets. A real vehicle test is applied to explain the implementation of the algorithm. Empirical results show that if the Gauss cloud model and the cloud reasoning algorithm are applied to calculate the lateral control offset and the vehicles drive at different speeds within a direction control area of ±7°, a stable control effect is achieved.
In academic and industrial circles, studies on intelligent vehicles have drawn considerable attention. Such studies play an important role in the research on vehicles and intelligent transportation. Control methods are the key in the study of intelligent vehicles. Vehicle model parameters are extremely complex. The system model equation is nonlinear, and its system parameters constantly change over time. Research on vehicle control theory includes lateral tracking control and longitudinal tracking control. Lateral tracking control includes the support vector machine (SVM) method, the sublevel control method [
The current study aims to improve the accuracy, robustness, and adaptability to various road conditions of the vehicle control algorithm. First, the convergence of vehicles toward trajectory tracking errors is investigated from the perspective of nonlinear system stability, which is the premise of vehicle tracking trajectory. Subsequently, the robustness and control algorithm that can adapt to the environment is also considered, thereby ensuring control performance when the running conditions of a vehicle are drastically changed. Finally, the function of vehicle motion control is expanded, which enables vehicles to complete automatic overtaking task, adaptive cruise task, automatic parking task, flowing into traffic task, and so on.
In most of the studies cited above, some researches only focused on lateral tracking control and some researches only focused on longitudinal tracking control, without considering driving speed and driving direction as input values. When intelligent driving tasks increase in complexity, the control systems cited earlier are unable to adapt to complex tasks. In addition, the control system should be able to guarantee stability. The main contributions of our study are as follows. (1) A new uncertainty control system according to the Gauss cloud model (GCM) and cloud reasoning is illustrated. (2) The new model considers both speed and direction, whereas velocity and direction are mutually constrained. (3) The speed control rules for intelligent driving vehicles are constructed, with reference to human driving experience.
This paper is organized as follows. Section
The Gauss distribution (GD) is one of the most important distributions in probability theory, in which the general characteristics of random variables are represented as means of the mean and variance of two numbers. As a fuzzy membership function, the bell-shaped membership function is mostly used in sets, which is typically expressed through the analytical expressions of
Generate Gauss random Generate Gauss random Calculate the certainty: Repeat (1)–(4) until the number of cloud drops is
The algorithm causes distribution drops, called cloud distribution (CD). The algorithm of GCM can be obtained through a cloud generator (CG), which forms a forward Gauss cloud generator (GCG), as shown in Figure when when
From (1) and (2), certainty can be concluded as a special case of uncertainty, and the GD is a special case of the GCD.
For a qualitative concept of a steering angle of positive and negative 40°, given that
The GCG.
The distribution of 1000 drops.
Knowledge forms a concept and its relationship with communicating and abstracting. The relationship among concepts forms certain rules, from which rules library and rules generator can be established through knowledge reasoning based on GC. Rules include preconditioned and postconditioned rules. Preconditioned rules include one or several rules, whereas postconditioned rules express the results and specific control actions generated by the preconditioned rules. In the control field, “perception-action” can establish the rule library based on the relationship among concepts, thereby realizing control of uncertainty.
A preconditioned Gauss cloud generator (PGCG) and a postconditioned Gauss cloud generator (PCGCG) are composed of the GCG, which is defined as follows.
Assume the following rule:
The PGCG algorithm is presented as follows [
Generate Gauss random Calculate the certainty: Generate the distribution of drops
As shown in Figure
The PGCG.
Cloud drop distribution of the PGCG.
Assume the following rule:
The PCGCG algorithm is presented as follows [
Generate Gauss random Calculate the certainty: Generate the distribution of drops
As shown in Figure
The PCGCG.
Cloud drop distribution of the PCGCG.
Assume the following rule:
The SCSRGCG algorithm is presented as follows.
Generate Gauss random Calculate the certainty: Generate Gauss random If If Generate the distribution of drops
The SCSRGCG implies an uncertainty transfer in the conceptual reasoning process. In the universal sets
The SCSRGCG.
Assume the following rule:
The “soft and” is expressed via 2D GCM
The DCSRGCG can establish numerous conditions of single-rule GCG (MCSRGCG) based on its composition principle. The SCSRGCG and the MCSRGCG are stored in the rule library and applied in qualitative knowledge reasoning and intelligent control field.
The DCSRGCG.
Quantitative transformation of the qualitative concept “soft and.”
The control of an intelligent vehicle mainly comprises the control for speed and angle under conditions of car-following driving, lane-changing driving, and intersection driving, with car-following driving being the most common. Using this state as an example, vehicle speed and angles can be intelligently controlled once cloud reasoning and cloud control are introduced.
Under the condition of car-following driving, an intelligent vehicle should constantly adjust its speed according to obstacles, such as vehicles and pedestrians, while driving efficiently and avoiding collisions. The angle control of an intelligent vehicle aims to keep the car in the middle of the road, with an equal distance between the left/right lane line and the center of the vehicle while driving. Furthermore, the heading direction should remain in accordance with the lane line.
The speed and angle controls of intelligent vehicles are both typical double-conditional and single-rule controllers. In Figure
Distance
The input of the cloud controller is the distance If the intelligent vehicle does not veer off the middle of the lane and the heading direction of that vehicle remains in accordance with the axis of the lane, then the steering wheel should be returned to the zero position to keep the car moving straight forward. That is, if If the vehicle offsets to the right, then turn the wheel to the left to try and return the vehicle to the center of the lane. For a higher offset value, a greater adjustment angle of the steering wheel is necessary. That is, if If the vehicle offsets to the left, then turn the wheel to the right and try to return the vehicle to the center of the lane. For a greater offset value, a greater adjustment angle of the steering wheel is necessary. That is, if If the included angle between the heading direction and the central axis of the lane is greater than 0, which indicates that the vehicle is drifting toward the right front of the axis, then turn the wheel to the left and try to return the vehicle to the center of the lane. For a higher offset value, a larger adjustment angle of the steering wheel is necessary. If If the included angle between the heading direction and the central axis of the lane is less than 0, which indicates that the vehicle is drifting toward the left front of the axis, then turn the wheel to the right and try to return the vehicle to the center of the lane. For a higher offset value, a larger adjustment angle of the steering wheel is necessary. If
In the next section, we will describe the linguistic value sets of the input and the output, define the range of different linguistic values, and establish the cloud controller and its control rules based on the aforementioned five qualitative rules.
The variables
The detailed car-following state and the speed control rules for intelligent vehicles are shown in Table
(a) Rule sets
Axis-line-distance |
Steering wheel angle | ||
---|---|---|---|
Positive more | Negative more | ||
Positive less | Negative less | ||
If | Zero | Then | Zero |
Negative less | Positive less | ||
Negative more | Positive more |
Axis-line-angle |
Steering wheel angle | ||
---|---|---|---|
Positive more | Negative more | ||
Positive less | Negative less | ||
If | Zero | Then | Zero |
Negative less | Positive less | ||
Negative more | Positive more |
Parameter setting of the qualitative concepts of the composition of the speed control rules of an intelligent vehicle.
Parameter | Positive greater | Positive less | Zero | Negative less | Negative greater |
---|---|---|---|---|---|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
The GCM and cloud reasoning can express human inference and decision and both exhibit strong robustness in solving the control problems of complicated systems. This study applies the GCM and the steering behavior imposed by cloud reasoning on drivers to build models. The model is shown in Figure
Lateral control algorithm flowchart.
The flowchart of the steering control algorithm shown in Figure
The on-board sensor configuration of an intelligent vehicle comprises a radar sensor, a vision sensor, and a positioning sensor. The radar sensor consists of two separate Universal Transverse Mercator (UTM) single laser radars on the left and right of the body, a forward SICK single laser radar, a forward four-layer laser radar, and a backward millimeter wave radar. The vision sensor comprises three front-facing cameras, two rear-facing cameras, and two lateral cameras set in both rear-view mirrors. The positioning sensor consists of the Global Positioning System (GPS) and an inertial measurement unit (IMU), as is shown in Figure
Experiment sensor configuration.
“MengShi” intelligent vehicle.
The design and development of intelligent vehicles are aimed at studying the key techniques of multi-interaction and collaborative driving based on visual and auditory information. The software architecture of intelligent vehicle systems is shown in Figure
Software architecture of an intelligent vehicle system.
The Beijing-Tianjin Expressway, which spans the Taihu Toll Station and the Dongli Toll Station, covers 121 km of shuttle distance. Rain is moderate rain in Tianjin, with a small amount of water on the ground. The weather is rainy in the Tianjin section of the Beijing-Tianjin Expressway. When the sun occasionally shines, the weather remains sunny until reaching Beijing, where it is cloudy. The temperature outside the vehicle is 32°C, and that on the road is 40°C. Visibility is over 200 m. The experiment path is designated by the blue line in Figure
Experiments paths.
When the intelligent vehicle proceeds,
Variation curve of the included angle
Variation curve of the distance
Variation curve of the included angle
Variation curve of distance
Variation curve of the included angle
Variation curve of distance
Variation curve of the included angle
Variation curve of distance
The curve graph of the control angle of the steering wheel is shown in Figure
Curve graph of steering wheel control.
Steering to the right creates a negative value, whereas steering left creates a positive value. Approximately 81% of the angles of the steering wheel range from −3° to 3°, 3% range from −6° to 6°, and the maximum angle ranges from −7° to 7°. According to relevant laws, the floating range of a manually operated steering wheel ranges from −7.5° to 7.5°, which is a stable operation.
This study proposes a novel type of lateral control migration algorithm for intelligent vehicles. On the basis of the GCM and cloud reasoning, it also presents the qualitative concept cloud parameterization of the speed control rule for a vehicle on an expressway, designs a lateral control algorithm for an intelligent vehicle, and provides the speed control rules for different car-following conditions. The lateral controller of the vehicle, which is based on the GCM and the cloud reasoning algorithm, can be adapted to various speeds. Therefore, 81% of the angles of the steering wheel range from −3° to 3°, 3% range from −6° to 6°, and the maximum angle, which can achieve stable control, is within the range of −7° to 7°.
The authors would like to declare that there is no conflict of interests regarding the publication of this paper.
This work is supported by National Natural Science Foundation of China under Grant nos. 61035004, 61273213, 61300006, 61305055, 90920305, and 61203366, the National Key Research and Development Program of China under Grant no. 2016YFB0100903, and the National High Technology Research and Development Program (“863” Program) of China under Grant no. 2015AA015401.