Optimal Feature Subset Selection Verification Strategy for Coordinated Lane Change Scenario of Intelligent Connected Vehicle

The multi-vehicle coordinated lane change is one typical application of intelligent connected vehicle(ICV), which must be systematically and thoroughly verified before across-the-board commercial application. Existing evaluation frameworks face challenges in effectively verifying multi-vehicle coordinated lane change algorithm, whose decision-making process is more complex and needs to consider more complex surrounding environments. This complexity introduces the "curse of dimensionality" into the verification process, adversely impacting verification efficiency. To address the aforementioned challenge, an efficient verification strategy with optimal feature subset selection is proposed in this study. Initially, the subset feature is defined by the integrals of position probability density function between host vehicle and surrounding vehicles across various decision-making phases of coordinated lane change algorithm. Following this, the optimal feature subset selection method is presented for verification in different decision-making phases of coordinated lane change algorithm. Subsequently, the verification strategy is delineated. Finally, the optimal feature subset selection verification strategy is implemented within a coordinated lane change scenario. A multi-start search algorithm is employed to explore the feasible domain of the multi-vehicle coordinated lane change algorithm. Verification through simulation is then executed, and its efficiency is compared with a widely used evaluation framework based on Test Matrix. Notably, the proposed strategy demonstrates a minimum efficiency improvement of 85%. The verification results underscore the effectiveness the proposed method in verification of phased multi-vehicle coordinated lane change decision-making algorithm, particularly within high-dimensional and complex environments.