Research On Trajectory Prediction Technology Based On Multi-Stage Quantum Genetic Algorithm

With the gradual improvement of the urban road network,the rapid development of the industrial process and the rapid growth of the number of vehicles,the contradiction between the travel demand brought by it and the limited road resources becomes more and more prominent,resulting in the increasingly serious traffic congestion.While people enjoy the speed and convenience brought by cars,they also begin to pay attention to the traffic congestion caused by them.Traffic congestion not only limits the efficiency of people’s travel process,but also restricts the coordinated development of cities,especially in large and super-large cities.Therefore,in the process of driving,how to efficiently and quickly pass the congested road section by predicting the vehicle trajectory,reducing the congestion time,and increasing the traffic speed is a difficult point now.In this context,a multi-stage quantum genetic algorithm for trajectory prediction is proposed based on prospect theory.Combining multi-stage decision making with quantum genetic algorithm,a solution algorithm for trajectory prediction model was designed,and the effectiveness of the model and algorithm was verified by intersection trajectory simulation.By understanding the research status of vehicle trajectory prediction and quantum genetic algorithm,combined with the unique properties of quantum computing,the feasibility of quantum genetic algorithm in trajectory prediction is analyzed.Aiming at the shortcomings of genetic algorithm in solving optimal problems,such as long search time and too random crossover mutation operation,quantum genetic algorithm is used to improve it.The principle of quantum genetic algorithm is described,and quantum genetic algorithm is obtained by function test and 0/1 knapsack problem.The algorithm has better convergence than the genetic algorithm in solving the optimal problem,and the result is more accurate.In the traffic-congested road intersection,in order to meet the actual situation of the vehicle,it is necessary to improve the quantum genetic algorithm.On the basis of analyzing the basic principles of prospect theory and the fitness in vehicle driving decision-making,combining prospect theory with quantum computing,taking the change of congestion degree as a reference point for predicting vehicle trajectory,and changing Hami by citing the aversion index in Hadamard gate A quantum genetic algorithm based on prospect theory is proposed.It is analyzed that the waiting aversion index and the congestion aversion index conform to the actual situation with the psychological changes of decision makers,which is convenient for parameter value selection in the experiment.Combined with the idea of multi-stage decision-making,the position update of the vehicle is divided into multiple stages with the change of congestion degree,and a multi-stage quantum genetic algorithm based on prospect theory is proposed.The feasibility of multi-stage quantum genetic algorithm is verified by simulation analysis of various road environments,which can accurately predict vehicle trajectory.In the intersection environment,a variety of algorithms are used to predict the trajectory of the same vehicle.By comparing and analyzing the grid number and iteration times of the experimental trajectory,it can be concluded that the predicted trajectory of the quantum genetic algorithm is shorter and more accurate,and the algorithm has good convergence.