To address existing shortcomings such as short time domains and low interpretability,this study proposes a long-term trajectory prediction model for leading vehicles that considers the impact of traffic flow.Through a...To address existing shortcomings such as short time domains and low interpretability,this study proposes a long-term trajectory prediction model for leading vehicles that considers the impact of traffic flow.Through an analysis of trailing trajectory data from the HighD natural driving dataset,fitting relationships for the following behavior patterns were derived.Building upon the intelligent driver model(IDM),three long-term trajectory prediction models were established:acceleration delta velocity(ADV),space delta velocity intelligent driver model(SDVIDM),and space velocity intelligent driver model(SVIDM).These models were then compared with the IDM model through simulations.The results indicate that when there is one vehicle ahead,under aggressive following conditions,the ADV model outperforms the IDM model,reducing the root mean square errors in acceleration,speed,and position by 79.61%,91.26%,and 87.82%,respectively.In scenarios with two vehicles ahead and conservative short-distance following,the SDVIDM model exhibits reductions of 83.42%,92.85%,and 92.25%,while the SVIDM model shows reductions of 82.31%,92.47%,and 94.02%,respectively,compared to the IDM model.展开更多
Optimization problems are prevalent in various fields,and the gradient-based gradient descent algorithm is a widely adopted optimization method.However,in classical computing,computing the numerical gradient for a fun...Optimization problems are prevalent in various fields,and the gradient-based gradient descent algorithm is a widely adopted optimization method.However,in classical computing,computing the numerical gradient for a function with variables necessitates at least d+1 function evaluations,resulting in a computational complexity of O(d).As the number of variables increases,the classical gradient estimation methods require substantial resources,ultimately surpassing the capabilities of classical computers.Fortunately,leveraging the principles of superposition and entanglement in quantum mechanics,quantum computers can achieve genuine parallel computing,leading to exponential acceleration over classical algorithms in some cases.In this paper,we propose a novel quantum-based gradient calculation method that requires only a single oracle calculation to obtain the numerical gradient result for a multivariate function.The complexity of this algorithm is just O(1).Building upon this approach,we successfully implemented the quantum gradient descent algorithm and applied it to the variational quantum eigensolver(VQE),creating a pure quantum variational optimization algorithm.Compared with classical gradient-based optimization algorithm,this quantum optimization algorithm has remarkable complexity advantages,providing an efficient solution to optimization problems.The proposed quantum-based method shows promise in enhancing the performance of optimization algorithms,highlighting the potential of quantum computing in this field.展开更多
基金support provided by the Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone(HZQB-KCZYZ-2021055)the support from the National Natural Science Foundation of China(52172389).
文摘To address existing shortcomings such as short time domains and low interpretability,this study proposes a long-term trajectory prediction model for leading vehicles that considers the impact of traffic flow.Through an analysis of trailing trajectory data from the HighD natural driving dataset,fitting relationships for the following behavior patterns were derived.Building upon the intelligent driver model(IDM),three long-term trajectory prediction models were established:acceleration delta velocity(ADV),space delta velocity intelligent driver model(SDVIDM),and space velocity intelligent driver model(SVIDM).These models were then compared with the IDM model through simulations.The results indicate that when there is one vehicle ahead,under aggressive following conditions,the ADV model outperforms the IDM model,reducing the root mean square errors in acceleration,speed,and position by 79.61%,91.26%,and 87.82%,respectively.In scenarios with two vehicles ahead and conservative short-distance following,the SDVIDM model exhibits reductions of 83.42%,92.85%,and 92.25%,while the SVIDM model shows reductions of 82.31%,92.47%,and 94.02%,respectively,compared to the IDM model.
基金supported by the National Natural Science Foundation of China under Grant No.12105195.
文摘Optimization problems are prevalent in various fields,and the gradient-based gradient descent algorithm is a widely adopted optimization method.However,in classical computing,computing the numerical gradient for a function with variables necessitates at least d+1 function evaluations,resulting in a computational complexity of O(d).As the number of variables increases,the classical gradient estimation methods require substantial resources,ultimately surpassing the capabilities of classical computers.Fortunately,leveraging the principles of superposition and entanglement in quantum mechanics,quantum computers can achieve genuine parallel computing,leading to exponential acceleration over classical algorithms in some cases.In this paper,we propose a novel quantum-based gradient calculation method that requires only a single oracle calculation to obtain the numerical gradient result for a multivariate function.The complexity of this algorithm is just O(1).Building upon this approach,we successfully implemented the quantum gradient descent algorithm and applied it to the variational quantum eigensolver(VQE),creating a pure quantum variational optimization algorithm.Compared with classical gradient-based optimization algorithm,this quantum optimization algorithm has remarkable complexity advantages,providing an efficient solution to optimization problems.The proposed quantum-based method shows promise in enhancing the performance of optimization algorithms,highlighting the potential of quantum computing in this field.
