Vehicle platoon offers numerous benefits in terms of road safety, energy efficiency, and traffic management in urban freight delivery. Privacy preservation is critical here: location information ties to customer confidentiality and reputation guarantees platoon reliability, yet most existing platoon management schemes fail to preserve privacy while achieving vehicle location-matching. Meanwhile, traditional distance calculation methods such as Euclidean distance are unsuitable for urban road layouts, and most schemes assume member vehicles must follow to unified endpoints, a rigid constraint conflicting with the scenario's needs. In this paper, we propose a privacy-preserving platoon management scheme for flexible platoon splitting in urban freight delivery (PPFPS). In detail, the PPFPS scheme leverages location and reputation to achieve flexible platoon splitting in platoon management while preserving vehicle privacy. Specially, we design an encrypted Manhattan distance calculation method (EMC) by combining bloom filters and Paillier cryptosystem, which is tailored to the road layouts in urban environments and deployed on cloud servers. The EMC method enables privacy-preserving location matching to achieve flexible platoon splitting, and reputation is used to ensure the reliability of vehicle platoon. Furthermore, the EMC method significantly minimizes the involvement of the trusted authority by introducing cloud-assisted approaches. Theoretical analysis demonstrates that the PPFPS scheme effectively preserves privacy and defends a variety of potential attacks. Simulation evaluation confirms that the PPFPS scheme supports more functions while significantly reducing computation overheads by 66.59% to 78.72% on the TA side, and maintains communication overheads of the similar order of magnitude as the existing schemes.

Prescribed-time stable systems frequently suffer from infinite gain issues that lead to practical infeasibility. This paper investigates the problem of stabilization for uncertain Euler-Lagrangian systems and proposes a singularity-free prescribed-time stabilization controller. Based on the time space deformation approach, the designed controller ensures singularity avoidance, effectively eliminating the existence of infinite gain. Besides, the considered Euler-Lagrangian systems are subject to unknown nonlinear functions and derivative-bounded external disturbances with unknown bounds. To achieve system stabilization under such uncertainty issue, this paper formulates multiple sliding manifolds with prescribed-time stability. The controller’s effectiveness is validated through simulation studies on a rendezvous formation problem.