As the scale and complexity of software for industrial equipment increases, the risk of defects being introduced into the software also increases. Over-the-air (OTA) technology eliminates software defects by regularly updating and maintaining the software equipment. However, downloading an upgraded file is time-consuming. The differential compression algorithm can rapidly complete OTA software upgrades. To solve the low-efficiency problem of suffix array sorting in the BsDiff algorithm, a fusion algorithm based on the suffix array induced sorting and BsDiff methods was proposed to reduce the time consumption in the OTA upgrade process. The execution of the difference algorithm in the cloud was simulated using bench experiments. Subsequently, the function and performance of the proposed algorithm were experimentally evaluated. The results showed that the optimized algorithm could save more than 35% of the time compared to the original algorithm, which improved the OTA upgrade efficiency to a certain extent.

Mandatory lane-changing in complex driving scenarios poses significant challenges for autonomous driving systems due to complex vehicle interactions and strict safety requirements. Existing methods often rely on handcrafted rules or extensive expert demonstrations, which increase data collection costs and provide limited safety guarantees during learning. To address these issues, this paper proposes a rule-guided reinforcement learning framework for lane-changing policy optimization. A lightweight rule-based controller is employed to generate initial experience, guiding the training of an improved Distributional Soft Actor–Critic with Three Refinements (DSAC-T), while a safety-aware constraint controller filters high-risk actions to ensure stable and safe learning. The proposed method is evaluated in Regular Lane Change and Lane Merging scenarios under mixed traffic composed of aggressive and conservative vehicles within a simulation environment. Simulation results show that although lane-changing success rates decrease as traffic aggressiveness increases, the proposed method consistently outperforms SAC and TD3. Notably, under highly aggressive traffic conditions with an aggressiveness ratio of 0.7, the proposed approach improves the success rate by 17.13% compared to SAC and by 10.49% compared to TD3, demonstrating superior robustness and safety in complex, high-conflict lane-changing scenarios. The present study is conducted solely in simulation and requires further validation before application to real-world traffic environments.

The wheels of lunar rovers are prone to bouncing during travel in the low gravity and rugged terrain conditions of the lunar surface, and poor matching of wheel alignment parameters can easily lead to tire wear in such conditions. Focusing on the double-wishbone suspension of lunar rovers, this study presents a wheel alignment parameter optimization method for tire wear reduction. First, a tire brush model is established, and it is determined that the toe angle and camber angle are the main factors affecting the tire wear work. And as the camber angle and toe angle increase, the tire wear work becomes greater. Then, a multi-body dynamic model of the double-wishbone independent suspension in a low-gravity environment is established. Taking the minimum tire wear as the optimization objective, the optimal solution set of alignment parameters such as the tire camber angle and toe angle obtained and the optimal hardpoint coordinate positions are determined. The variation range of the toe angle is optimized from [−0.55°, 1.58°] to [−0.37°, 1.32°]. After optimization, the variation in the toe angle is reduced by 20.4%, the change rate of the camber angle becomes smoother, and the comprehensive wear work of the tire is reduced by 17.47%. The research results provide theoretical guidance for the optimization of wheel alignment parameters of the double-wishbone suspension of the lunar rover.

A fast recovery from disruptions is of vital importance for the reliability and sustainability of urban zone transit systems. It’s a complex task to coordinate multiple transit departments to mitigate disruption. There are many ways to response but it’s not always obvious how to combine them in an optimized manner. This study presents a new attempt to tackle this problem in a comprehensive and hierarchical way. At phase (i), a network-scale strategy selection optimization model is formulated as a joint routing and resource allocation (nJRRA) problem. This model produces solutions for efficient allocation of network resources to facilitate inter-department coordination. By constraining the problem further into an ϵ -constrained nJRRA problem, classic solution algorithms can be applied to solve the quadratically constrained quadratic program (QCQP). On top of this “basic model”, we propose adding a decision to delay the resource allocation decisions up to a maximum initiation time when the incident duration is stochastic. To test the models, a quasi-dynamic evaluation program with a given incident duration distribution is constructed using discretized time steps and discrete distributions. Five different demand patterns and four different disruption duration distributions (20 combinations) are tested on a small transit network. The results show that the two models outperform benchmark strategies such as using only line level adjustment or only bus bridging. They also highlight conditions when delaying the decision is preferred.