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Currently, model predictive control (MPC) for adaptive cruise control (ACC) systems relies on the prediction of the leader’s motion to plan the follower’s trajectory. However, such predictions must be accurate to guarantee string stability, which represents an ongoing challenge for machine learning approaches. This issue can be circumvented by simply incorporating the leader’s history, which follows from Newell’s car-following (CF) model where a trajectory under congestion corresponds to a temporal–spatial shift of the leader’s past trajectories. By leveraging this insight, this paper develops a family of MPC models based on Newell’s CF model, labeled Newell MPCs, which are safe and can reduce traffic congestion. Specifically, We first present baseline Newell MPCs to replicate the original Newell’s CF model, including the X b o u n d-Model, which uses the shifted leader trajectory as an upper envelope; and the X r …