Platoon Trajectory Completion in a Mixed Traffic Environment Under Sparse Observation

Obtaining sufficient trajectory data of human-driven vehicles (HDVs) is critical for effective control of connected automated vehicles (CAVs) in mixed traffic of HDVs and CAVs. However, due to limited sensing and communication capabilities, only a fraction of HDVs' trajectories are often observed. This paper proposes a completion method to recovery all HDVs' trajectories in a mixed platoon based on partial observations. The trajectory completion problem is formulated as an optimization problem, aiming to minimize the error between observed and completed trajectories with car-following constraints defined by Newell's simplified car-following model. The method also allows various model parameters of different drivers, which is known as the inter-driver heterogeneity, to reduce the completion error. Validation using empirical trajectory data shows that the proposed method greatly lowers the completion error than other typical trajectory completion methods under sparse observation (6s/sample).

Automated summary

This paper proposes a method to complete all trajectories of human-driven vehicles in a mixed platoon. By formulating the completion problem as an optimization problem and introducing car-following constraints, the method can reduce completion error. Validation using empirical data shows that this method has lower completion error compared to other methods under sparse observation.