With the recent developments of autonomous vehicles, extensive studies were conducted on Adaptive Cruise Control (ACC, for short), which is an essential component of advanced driver-assistant systems (ADAS). The safety assessment must be performed on the ACC system before it goes to market. The validation process is generally conducted via simulation due to insufficient on-road data and the diversity of driving scenarios. Our paper aims to develop an optimization-based reference generation model for ACC, which can be used as a benchmark for assessment and evaluation. The model minimizes the difference between the actual and reference inter-car distance while respecting constraints about vehicle dynamics and road regulations. ACC sensors can be impacted by external factors, e.g., weather conditions and produce inaccurate data. To handle the resulting uncertainty, we propose a copula-based chance-constrained stochastic model in order to model the dependence between the random variables. Our numerical experiments show the performances of our model on randomly generated driving scenarios.