Analysis of stability and cooperation in traffic flow with connected automated vehicles under perceptual uncertainty

This study aims to investigate the stability and cooperation of traffic flow comprising connected automated vehicles (CAVs) in perceptual uncertainty environment. Specifically, an extended version of the cooperative intelligent driver model (CIDM), referred to as the weighting dynamic CIDM (WD-CIDM), is developed to describe the dynamics of CAV traffic flow under perceptual uncertainty. The proposed model incorporates perceptual uncertainty levels to quantify the extent to which various control inputs diverge from their true values, while introducing a weighting parameter to capture a spectrum of CAV traffic flow patterns under varying degrees of perceptual reliability within the system. Especially, the real-time number of interactive vehicles is leveraged to reflect CAV's cooperative capability, allowing for the evaluation of the interplay between perceptual uncertainty, cooperation, and stability in CAV traffic flow. Then, a head-to-tail transfer function approach is applied to derive the stability criterion. Sensitivity analysis shows that positive uncertainty levels in velocity and relative velocity enhance stability, whereas positive gap uncertainty level and higher weighting parameter reduce stability. Correspondingly, negative levels of these uncertainties have the opposite effects. Another interesting finding is that, while increasing the number of interactive vehicles helps stabilize traffic flow, the marginal benefit diminishes with scale. Numerical tests with an open boundary condition validate the stability analysis by examining both the dynamic evolution of traffic flow and the variations in spacing error. Furthermore, an inherent trade-off in CAV traffic flow under perceptual uncertainty is revealed: strong cooperation and high stability cannot be simultaneously ensured.