Research On Motion Trajectory Planning Method For Intelligent Vehicles

Intelligent Vehicle (IV) is an important constituent of the Intelligent Transportation System (ITS).Intelligent Vehicle can improve the road traffic safety and reduce the traffic accidents remarkably, so ithas important theory searching significance and engineering practice value in military and civilterritory．The trajectory planning system is an important constituent of Intelligent Vehicle’s planningand control system. The trajectory planning is different from the path planning, the path planning onlygenerate the static collision-free path, while the trajectory planning not only generate collision-freepath but also relevant state parameters and control parameters, such as velocity, acceleration anddriving time, etc. The relevant contents of Intelligent Vehicle’s motion trajectory planning are studiedin this paper. The research contents include state space based trajectory planning method,ACT-R(Adaptive Control of Thought-Rational) based motion trajectory planning method and motiontrajectory optimized method. Concrete contents of the research have been summarized as follows:(1) Considering the time factor, a state space based trajectory planning method is presented. Inorder to generate curvature-continous and dynamic suitable different driving environment’ trajectory,based on the optimal control theory an improved trajectory planning method is presented in this paper.The multi-objective dynamic cost function is built in the improved method. The influence law ofdifferent weights to trajectory’s features is studied and the effective weights regulation method isobtained. The simulation results showed that the state spaced based trajectory planning method couldgenerate dynamically adaptive different road environments’ driving trajectory. The trajectory iscurvature-continous with several constraints are obtained.(2) Based on ACT-R cognitive model, a trajectory planning method with human’s behavioralcharacteristic is introduced. The state space based trajectory planning method and the ACT-Rcognitive model are contacted, the ACT-R cognitive model is the core of the method. Firstly, theinitialization module initializes the weight value set. Secondly, the trajectory is generated by the statespace based trajectory planning method and the trajectories’ feature values are extracted. Lastly, theevaluation module evaluates the trajectory’s feature values, make sure of the feature values underconstraints and return the trajectory. If the constraints are not obtained, the weight regulation moduleis used to regulate the weight value to generate the driving trajectory with human’s behavioralcharacteristic. The simulation results and model vehicle’s experiment results showed that the methodwas feasible. The driving trajectory generated by the method has human’s behavioral characteristicand several constraints are obtained.(3) On the basis of the vehicle’s dynamic constraints, a motion trajectory optimized method combined the B-spline curve and improved genetic algorithm is presented. First, the B-spline curve’sproperties of curvature-continous and local support character are used to parameterize the generatedtrajecroy. In order to make sure of the B-spline curve’s shape, the data point positions are known tocalculate the control points. Then, the improved genetic algorithm is used to multiobjective optimizethe parameterized trajectory. The parameters that can confirm the B-spline curve’s shape areoptimized to satisfy the dynamic constraints. The optimized objectives include data points and relativedate points’ velocity, acceleration and the time interval between the adjacent data points. Thesimulation results showed that the optimized trajectories’ dynamic constraints was obtained andoptimized trajectory was better than the unoptimized trajectory.(4) Based on the CarSim simulation software, a test method of this paper’s method is designed.The motion trajectory planning method generates the motion trajectory and the trajectories’parameters within the sampling period in the Matlab and ACT-R sofware. The generated trajectoryand trajectories’ parameters as CarSim’s path and parameter inputs are supplied to CarSim’s optimalcontroller. The optimal controller controls the vehicle model to drive on the planned trajectory invirtual scene simulation. The vehicle’s response parameters are extracted and reponsed to the motiontrajectory planning algorithm when the vehicle model driving on the planned trajectory. The trajectoryplanning algorithm estimates the parameters if under the constraints, if the trajectory is not under theconstraints, the trajectory planning algorithm modifies the trajectory and output the new trajectory.The cycle is done until the end of the simulation. Three group comparation studies were done to testthis paper’s method. Three couple comparation studies include the simulation comparation betweenthis paper’s method and two article’s path planning methods, the comparation of vehicle driving withdifferent velocities on the planned steady circular trajectory, serpentine curve trajectory and doubleshift line trajectory, the comparation between simulation results and other article’s experiment resultsof the real vehicle. The research results showed that this paper’s approach was more effective andfeasible than other article’s path planning method, the vehicle driving at the velocity planned by thispaper’s method could satisfy several constraints while the vehicle driving at other velocities could notsatisfy several constraints, the simulation datas were equal to the article’s real experiment datas.In summary, this paper focus on the sudy of Intelligent Vehicle’s motion trajectory planningmethod, the CarSim simulation software and the model vehicle’s experiment are used to test thevalidity and feasibility of the motion trajectory planning method. The studied results can providecurvature-continous and effective motion trajectory for the controller, so that the controller couldauto-control the vehicle driving on the road.