How predictive-forward-collision-warning reduces the collision risk of leading vehicle driver

Mixed platoon with a human-driven leading vehicle may be a transition mode prior to the widespread adoption of fully autonomous platoon. Enhancing the driving safety of the leading vehicle driver is crucial for improving the overall operational safety of the mixed platoon. Predictive-Forward-Collision-Warning (PFCW), an emerging technology in transportation, holds promise in mitigating collision risks for drivers by presenting traffic information beyond their immediate visual range. However, the influence characteristics of this function and how it influences the evolution of collision risk in leading vehicle driver remain unclear. Therefore, this paper attempts to analyse the quantitative impact of PFCW on the collision risk of leading vehicle driver. A test platform for connected mixed platoon was built utilizing driving simulation technology, alongside the development of a connected Human-Machine Interface (HMI) incorporating PFCW functionality. To evaluate the longitudinal collision risk of leading vehicle driver, a time-frequency analysis method was employed, focusing on key indicators: deceleration rate to avoid collision (DRAC), time to collision (TTC), and proportion of stopping distance (PSD). The time-domain analysis results indicated that PFCW can significantly mitigate the collision risk of leading vehicle. Wavelet transform results demonstrated that PFCW can ameliorate drivers' abnormal driving behavior and mitigate the collision risk in emergency situation of impending collision moment. Meanwhile, PFCW can enhance the overall operation safety of the mixed platoon. This paper leverages driving simulation technology and multidimensional indicators to analyze the quantitative impact of PFCW on the collision risk of leading vehicle driver during rapid deceleration of preceding vehicles. The findings can guide the development of test standards for connected mixed platoon, the promotion and application of PFCW, and the advancement of Navigate on Autopilot (NOA). Additionally, the test platform and framework developed in this study can accommodate various experimental needs for connected mixed platoon testing.