Dear Editor,Allele conversion technology has broad application value in plant breeding.Here,we report a user-friendly allele conversion technology based on clustered regularly interspaced short palindromic repeats(CRI...Dear Editor,Allele conversion technology has broad application value in plant breeding.Here,we report a user-friendly allele conversion technology based on clustered regularly interspaced short palindromic repeats(CRISPR)-Cas9 and hyperrecombination lines(HRLs)(Figure 1A).Many agronomically important traits are controlled by single nucleotide polymorphisms(SNPs),insertions or deletions,and other types of small variations between alleles.Traditionally,breeders have relied on backcrossing and selection to introduce a useful allele into the genome of another variety.展开更多
To improve the driving efficiency of unmanned vehicles in a complex urban traffic flow environment and the safety and passenger comfort of vehicles when changing lanes,we propose a hierarchical reinforcement learning(...To improve the driving efficiency of unmanned vehicles in a complex urban traffic flow environment and the safety and passenger comfort of vehicles when changing lanes,we propose a hierarchical reinforcement learning(HRL)-based vehicle trajectory planning and tracking method.First,we present a hierarchical control framework for vehicle trajectory tracking that is based on deep reinforcement learning(DRL)and model predictive control(MPC).We design an upper-level decision model based on the trust region policy optimization algorithm integrated with long short-term memory to obtain more accurate strategies.Second,to improve stability and passenger comfort,we constructed a lower controller that combines the Bezier curve fitting method and an MPC controller.Finally,the proposed method was simulated via the car learning to act(CARLA)simulator,which is based on an unreal engine.Random urban traffic-flow test scenarios were used to simulate a real urban road-traffic environment.The simulation results illustrate that the proposed method can complete the vehicle trajectory planning and tracking task well.Compared with the existing RL methods,our proposed method has the lowest collision rate of 1.5%and achieves an average speed improvement of 7.04%.Moreover,our proposed method has better comfort performance and lower fuel consumption during the driving process.展开更多
基金supported by the National Natural Science Foundation of China(32272727)the Technology Innovation Program of the Chinese Academy of Agricultural Sciences(CAAS-ASTIP-IVFCAAS).
文摘Dear Editor,Allele conversion technology has broad application value in plant breeding.Here,we report a user-friendly allele conversion technology based on clustered regularly interspaced short palindromic repeats(CRISPR)-Cas9 and hyperrecombination lines(HRLs)(Figure 1A).Many agronomically important traits are controlled by single nucleotide polymorphisms(SNPs),insertions or deletions,and other types of small variations between alleles.Traditionally,breeders have relied on backcrossing and selection to introduce a useful allele into the genome of another variety.
基金supported in part by the Jiaxing Public Welfare Research Program(Grant No.2023AY11034)the Zhejiang Provincial Natural Science Foundation of China under(Grant No.LTGS23F030002)+1 种基金the National Natural Science Foundation of China(Grant No.61603154)the Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(Grant No.ICT2022B52).
文摘To improve the driving efficiency of unmanned vehicles in a complex urban traffic flow environment and the safety and passenger comfort of vehicles when changing lanes,we propose a hierarchical reinforcement learning(HRL)-based vehicle trajectory planning and tracking method.First,we present a hierarchical control framework for vehicle trajectory tracking that is based on deep reinforcement learning(DRL)and model predictive control(MPC).We design an upper-level decision model based on the trust region policy optimization algorithm integrated with long short-term memory to obtain more accurate strategies.Second,to improve stability and passenger comfort,we constructed a lower controller that combines the Bezier curve fitting method and an MPC controller.Finally,the proposed method was simulated via the car learning to act(CARLA)simulator,which is based on an unreal engine.Random urban traffic-flow test scenarios were used to simulate a real urban road-traffic environment.The simulation results illustrate that the proposed method can complete the vehicle trajectory planning and tracking task well.Compared with the existing RL methods,our proposed method has the lowest collision rate of 1.5%and achieves an average speed improvement of 7.04%.Moreover,our proposed method has better comfort performance and lower fuel consumption during the driving process.
