Longitudinal control systems in intelligent connected vehicle (ICV) significantly reduce driving workload; however, most existing designs overlook the driver’s psychological perception of car-following safety. This neglect can elevate physiological stress and erode trust in the system. To address this, this paper proposes a human-centered Connected Adaptive Cruise Control (C-ACC) strategy that integrates the driver’s individualized cognitive safety analysis into the control loop within a connected environment. Using naturalistic driving data that jointly capture traffic context, vehicle states, and driver physiology, we construct an individualized safety boundary in the speed–distance domain. The boundary is parameterized by a Sigmoid function fitted to physiological activation features and is incorporated as a soft constraint within a Model Predictive Control (MPC) framework. Crucially, the controller leverages Vehicle-to-Vehicle (V2V) communication to optimize tracking performance while proactively limiting states associated with elevated activation. The strategy was validated through driver-in-the-loop experiments using two real vehicles enabled with V2V communication. Experimental results demonstrate that, compared with baseline strategies, the proposed approach effectively reduces physiological activation and psychological tension while ensuring ride comfort and control stability, thereby enhancing the driver’s perceived safety and trust. This framework provides a principled path for personalized C-ACC design based on cognitive state estimation and is scalable to broader connected automated driving applications.

Given any algorithm for convex optimization that uses exact first-order information (i.e., function values and subgradients), we show how to use such an algorithm to solve the problem with access to inexact first-order information. This is done in a “black-box” manner without knowledge of the internal workings of the algorithm. This complements previous work that considers the performance of specific algorithms like (accelerated) gradient descent with inexact information. In particular, our results apply to a wider range of algorithms beyond variants of gradient descent, e.g., projection-free methods, cutting-plane methods, or any other first-order methods formulated in the future. Further, they also apply to algorithms that handle structured nonconvexities like mixed-integer decision variables.

We show that one can enumerate the vertices of the convex hull of integer points in polytopes whose constraint matrices have bounded and nonzero subdeterminants, in time polynomial in the dimension and encoding size of the polytope. This improves upon a previous result by Artmann et al. who showed that integer linear optimization in such polytopes can be done in polynomial time.