This paper presents the Optimized Kalman Particle Swarm (OKPS) filter. This filter results from two years of research and improves the Swarm Particle Filter (SPF). The OKPS has been designed to be both cooperative and...This paper presents the Optimized Kalman Particle Swarm (OKPS) filter. This filter results from two years of research and improves the Swarm Particle Filter (SPF). The OKPS has been designed to be both cooperative and reactive. It combines the advantages of the Particle Filter (PF) and the metaheuristic Particle Swarm Optimization (PSO) for ego-vehicles localization applications. In addition to a simple fusion between the swarm optimization and the particular filtering (which leads to the Swarm Particle Filter), the OKPS uses some attributes of the Extended Kalman filter (EKF). The OKPS filter innovates by fitting its particles with a capacity of self-diagnose by means of the EKF covariance uncertainty matrix. The particles can therefore evolve by exchanging information to assess the optimized position of the ego-vehicle. The OKPS fuses data coming from embedded sensors (low cost INS, GPS and Odometer) to perform a robust ego-vehicle positioning. The OKPS is compared to the EKF filter and to filters using particles (PF and SPF) on real data from our equipped vehicle.展开更多
Encouraging and motivating travelers to opt for more efficient and low-carbon last-mile transportation options is a crucial strategy for increasing the share of public transportation.This study aims to understand trav...Encouraging and motivating travelers to opt for more efficient and low-carbon last-mile transportation options is a crucial strategy for increasing the share of public transportation.This study aims to understand travelers’preferences for the new travel mode combination of “shared autonomous(SAVs)+subway”and to explore effective incentive policies to encourage heterogeneous population with diverse demographics to adopt this mode.Grounded in social cognitive theory(SCT)the study establishes a structural equation model(SEM)encompassing four latent variables:low-carbon knowledge,low-carbon habits influ-enced by policy incentives,external environmental factors,and low-carbon travel inten-tion,to analyze the factors influencing individual transportation mode choice.Prospect theory is proposed to calculate prospect values rather than utility values,and a discrete choice model is constructed to estimate the risk preference coefficients of various traveler types under different incentive measures,facilitating a comparison of the effectiveness of these incentives.The findings indicate that residents of mega-cities and low-income groups are more responsive to policy incentives and more inclined to choose the combined transportation mode.In mega-cities,travelers show a higher preference for public trans-portation recharge rewards,whereas cash rewards are more attractive to travelers in second-tier cities and low-income groups.High-income groups exhibit a stronger prefer-ence for commodity shopping vouchers.Incorporating these insights into the incentive measures of decarbonization platforms will enhance the promotion and adoption of the combined transportation mode.展开更多
文摘This paper presents the Optimized Kalman Particle Swarm (OKPS) filter. This filter results from two years of research and improves the Swarm Particle Filter (SPF). The OKPS has been designed to be both cooperative and reactive. It combines the advantages of the Particle Filter (PF) and the metaheuristic Particle Swarm Optimization (PSO) for ego-vehicles localization applications. In addition to a simple fusion between the swarm optimization and the particular filtering (which leads to the Swarm Particle Filter), the OKPS uses some attributes of the Extended Kalman filter (EKF). The OKPS filter innovates by fitting its particles with a capacity of self-diagnose by means of the EKF covariance uncertainty matrix. The particles can therefore evolve by exchanging information to assess the optimized position of the ego-vehicle. The OKPS fuses data coming from embedded sensors (low cost INS, GPS and Odometer) to perform a robust ego-vehicle positioning. The OKPS is compared to the EKF filter and to filters using particles (PF and SPF) on real data from our equipped vehicle.
基金supported by Nature Science Foundation of China[Grant number:52302441]the Science and Technology Commission of Shanghai Municipality[Grant number:22dz1207500].
文摘Encouraging and motivating travelers to opt for more efficient and low-carbon last-mile transportation options is a crucial strategy for increasing the share of public transportation.This study aims to understand travelers’preferences for the new travel mode combination of “shared autonomous(SAVs)+subway”and to explore effective incentive policies to encourage heterogeneous population with diverse demographics to adopt this mode.Grounded in social cognitive theory(SCT)the study establishes a structural equation model(SEM)encompassing four latent variables:low-carbon knowledge,low-carbon habits influ-enced by policy incentives,external environmental factors,and low-carbon travel inten-tion,to analyze the factors influencing individual transportation mode choice.Prospect theory is proposed to calculate prospect values rather than utility values,and a discrete choice model is constructed to estimate the risk preference coefficients of various traveler types under different incentive measures,facilitating a comparison of the effectiveness of these incentives.The findings indicate that residents of mega-cities and low-income groups are more responsive to policy incentives and more inclined to choose the combined transportation mode.In mega-cities,travelers show a higher preference for public trans-portation recharge rewards,whereas cash rewards are more attractive to travelers in second-tier cities and low-income groups.High-income groups exhibit a stronger prefer-ence for commodity shopping vouchers.Incorporating these insights into the incentive measures of decarbonization platforms will enhance the promotion and adoption of the combined transportation mode.
