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Based on analysis and evaluation on the circular, cosine type, constant-speed offset type and ladder type lane change trajectory, this paper proposes an intelligent vehicle lane change trajectory model under multiple barriers, proposes its dynamic constraints in the light of the cellular automata theory, obtains the desired lane change trajectory using this method, and finally changes into a simple coefficient selection problem. Secondly, based on the quadratic optimal control theory, this paper proposes a state space analysis method of intelligent vehicle lateral control, and designs an optimal controller for lateral stability of H2 vehicles. The computer simulation results show that compared with other vehicle trajectory methods, the method in this paper is able to simply and rapidly describe the trajectory, and can describe the intelligent vehicle lane change trajectory under a variety of situations, wherein the controller is reliable and capable of fast convergence.

China is one of the developing countries with sustained economic growth in the world, and has remained more than 7% economic growth rate since 1991 [

At present, the research on intelligent vehicle lane change for overtaking mainly includes macroscopic and microscopic traffic flow simulation, lane change early warning, simplifying automatic control of the vehicle lane change, and so on [

At present, the research on intelligent vehicle lane change for overtaking mainly includes macroscopic and microscopic traffic flow simulation, lane change early warning, simplifying automatic control of the vehicle lane change, and so on. Early lane change model only considers whether the front and back gap on the target lane meets the safe running requirements, and implements lane change if so [

(1) Circular trajectory is one of the simplest trajectories with the strength of short trajectory planning time and weakness of poor smoothness;

(2) Cosine trajectory assumes that the curve consists of two cosine type trajectories, is characterized by good smoothness, but suffers from large accumulated error;

(3) Constant-speed offset planning trajectory is characterized by simple planning method, is easy to get the optimal solution of parameters, but is difficult to be put into engineering practice under real road conditions;

(4) Trapezoidal acceleration lane change trajectory can better satisfy the continuous curvature change and the change rate restrictions during moving from the angle of lateral acceleration of the vehicle with lane change, but is not flexible. For example, it is difficult to regulate the lane change process.

In case of autonomous driving of an intelligent vehicle, multiple obstacles, if any, may be split into several independent individual barriers using the idea of breaking up the whole into parts, and then processed using the above-mentioned algorithm of a single barrier, so that the number of suitable lane change trajectory is reduced accordingly, because under this situation it is necessary to consider not only the constraints of a single barrier, but also the impact of lane change on the next barrier [

Problem Description. In general, the vehicle lane change involves a controlled vehicle (vehicle “c”), a vehicle on the current lane (vehicle “o_{1}”) and two vehicles on the target lane (vehicles “o_{2}” and “o_{3}”), as shown in

Dynamic Constraints. As can be seen from the above discussion, as long as the barriers are identified in the

parameter space, it is very easy to choose a suitable trajectory. This section will discuss the dynamic constraints brought about by dynamic changes of the vehicles [

In fact, the highest horizontal acceleration and longitudinal acceleration interact and influence one another. Here, a more conservative approach is adopted under the assumption that a vehicle can reach the maximum critical acceleration as follows:

where

In order to associate the maximum acceleration with the polynomial selection, the following steps are adopted: the above-mentioned polynomial coefficient only depends on the boundary conditions and should not be changed, so the lateral acceleration arising from the boundary conditions follows the above standard. The Equation (1) is first subject to quadratic differential. If at least one of its maximum values and minimum values is beyond the above scope, then it is necessary to change the boundary conditions (i.e. the final situation). In practical operation, it may be available by checking the table of correspondence between the time of completing lane change and the final horizontal position.

If the designed lateral acceleration is guaranteed to be feasible, then it is necessary to select appropriate polynomial parameter values for calculation of

Then it is possible to get:

where, c_{3} and c_{4} are respectively a function of time and boundary conditions, namely a new function of b_{6}, so the corresponding dynamic constraints shall satisfy the following formula:

So the root of Equation (5) is drawn, and then a polynomial parameter within permissible range is selected to ensure that it is within the above constraints, which will be proved by examples as follows.

The above situation is still taken as an example: the initial and terminal vehicle states are available from Equations (7). For simplification, the vehicles on the target lane are neglected, the order of the polynomial

In this case, the dynamic states are also identified on the figure as barriers, so they can be regarded as practical barriers. Obviously, it becomes very easy to choose a feasible trajectory at this time. Control strategy of intelligent vehicle lane change trajectory for overtaking under multiple barriers was shown as

Principle of Cellular Automata. Cellular Automata (CA for short) is essentially a dynamical system that is defined in a cellular space composed of discrete cells in a finite state, and is evolved in discrete time dimension according to certain local rules. The cellular automata is not determined by strictly defined physical equations or functions, but composed of a series of model construction rules. Any model following these rules can be counted as a cellular automata model.

From the

The position curve is shown in

(1) The position shows smooth transition without sharp points and crude points in the trajectory;

(2) Both horizontal position and longitudinal position reach the target position within expected time.

Robust stability of the yaw velocity helps to ensure the lateral stability of the vehicle itself, and then lay the foundation for steering control of the whole vehicle system according to the navigation information. This paper establishes a mathematical model for vehicle steering control in the light of the kinematic model of two degrees of freedom, kinematic model of preview, and dynamic property function of steering of vehicles, and designs the optimal steering controller of H2 based on the linear quadratic optimal control theory.

Selection of the optimal control rate mainly refers to determination of the performance index function, and is

essentially the optimal control under certain performance indexes. For an actual control system, shift from an initial state to a goal set can be realized by a variety of different control functions, and it is first necessary to determine a performance index for evaluation on the control effect to find an optimal control law from feasible control functions. The computer simulation results in

In this text, we proposed an intelligent vehicle lane change trajectory model under multiple barriers, proposes its dynamic constraints in the light of the cellular automata theory, obtains the desired lane change trajectory using this method, and finally changes into a simple coefficient selection problem.

The cellular automata and the optimal control theory were used in controller design. By the simulation result,

it shows that the controller is reliable and capable of fast convergence.

The project was supported by the Zhejiang Provincial Natural Science Foundation of China (Granted No. LY13E080010), and by the Jinhua civic Bureau of Science and Technology under Grant No. 2013-3-031 .

ZhonghuaZhang,XuecaiYu,ZhijieJin,YejunYing,RongweiHua,XuweiLin, (2015) Trajectory Planning and Optimal Lateral Stability Control under Multiple Barriers for Intelligent Vehicle. World Journal of Engineering and Technology,03,100-105. doi: 10.4236/wjet.2015.33011