Condition-awareness regarding electrical and electronic components is not only significant for predictive maintenance of automotive vehicles but also plays a crucial role in ensuring the operational safety by supporting the detection of anomalies, faults, and degradations over lifetime. In this paper, we present a novel control strategy that combines stochastic dynamic control method with condition-awareness for safe automated driving. In particular, the effectiveness of condition-awareness is supported by two distinct condition-monitoring functions. The first function involves the monitoring of a vehicle's internal health condition using model-based approaches. The second function involves the monitoring of a vehicle's external surrounding conditions, using machine learning and artificial intelligence approaches. For the quantification of current conditions, the results from these monitoring functions are used to create system health indices, which are then utilized by a safety control function for dynamic behavior regulation. The design of this safety control function is based on a chance-constrained model predictive control model, combined with a control barrier function for ensuring safe operation. The novelty of the proposed method lies in a systematic integration of monitored external and internal conditions, estimated component degradation, and remaining useful life, with the controller's dynamic responsiveness. The efficacy of the proposed strategy is evaluated with adaptive cruise control in the presence of various sensory uncertainties.