For the Internet of Vehicles (IoV), driving safety applications require reliable and up-to-date knowledge of the state of vehicles and traffic. A single vehicle cannot meet all the reliability requirements because of the limited capability of information acquisition. Thus, cooperation among vehicles for information sharing is essential. However, due to the high dynamic network topology and harsh channel conditions, maintaining long-term cooperation is not feasible. Only the messages that most affect the driving state can obtain the transmission opportunity for avoiding network congestion. In this paper, we propose a cooperative safety-enhanced control framework (SCF). This framework concentrates on the construction of dynamic and adaptive cooperation among vehicles and evaluates the key feature parameters to achieve an optimal safety utility for feedback control over the driving state. We construct a general multi-layer solution framework for driving assistance in SCF. First, we construct multiple temporary cooperative platoons to coordinate adjacent vehicles and realize a relatively uniform driving state. The cooperative platoon maintains short-term stability for vehicle sensing and tracing. Second, we propose a utility evaluation model for extracting the key feature parameters related to the driving state, which is the basis of the optimization for message transmission and driving control. Third, we design a two-level joint optimization mechanism for the deep fusion of the multi-source heterogeneous data to maximize the total utility of driving safety. Finally, we propose an adaptive feedback control model for the cooperative platoon, which actively adjusts the driving control strategy and the message transmission strategy in a real-time manner. Then the optimal driving assistant decision can be made. Extensive simulation results show that SCF outperforms related communication mechanisms for safe driving in the IoV, demonstrating that SCF can effectively enhance driving assistance control.

Modern solvers for solving mixed integer programming (MIP) often rely on the branch-and-bound (B&B) algorithm which could be of high time complexity, and presolving techniques are well designed to simplify the instance as pre-processing before B&B. However, such presolvers in existing literature or open-source solvers are mostly set by default agnostic to specific input instances, and few studies have been reported on tailoring presolving settings. In this paper, we aim to dive into this open question and show that the MIP solver can be indeed largely improved when switching the default instance-agnostic presolving into instance-specific presolving. Specifically, we propose a combination of supervised learning and classic heuristics to achieve efficient presolving adjusting, avoiding tedious reinforcement learning. Notably, our approach is orthogonal from many recent efforts in incorporating learning modules into the B&B framework after the presolving stage, and to our best knowledge, this is the first work for introducing learning to presolve in MIP solvers. Experiments on multiple real-world datasets show that well-trained neural networks can infer proper presolving for arbitrary incoming MIP instances in less than 0.5s, which is neglectable compared with the solving time often hours or days.