Accurate gradient and heavy vehicle mass estimation are crucial to improve vehicle control effects. However, significant obstacles remain in the way of accurately estimating gradient and mass in real-time, mainly reflected in the connection between vehicle mass and gradient. to instantly calculate the vehicle's mass and the gradient, this paper proposes a double variable forgetting factor recursive least squares(VFFRLS) algorithm based on vehicle longitudinal dynamics. Firstly, considering the deep coupling of mass and gradient, a multivariate VFFRLS algorithm is designed to obtain a joint mass-slope estimation, to determine the precise, steady vehicle mass and the uneven gradient. Secondly, because the slope of the road changes more frequently than the vehicle's mass, the upper layer estimation mass is used as input to design the lower layer VFFRLS algorithm to realize the fine estimation of road slope. The influence of cumulative error and coupling relationship on slope estimation accuracy is effectively reduced. Finally, To get an exact estimate of the slope of the road, the rough and fine estimates of the slope are distributed according to a specific proportion. The Trucksim and Matlab/Simulink simulations confirm the efficacy of this algorithm. The findings indicate that the real-time estimation algorithm designed can achieve a mass estimation error is less than 4%, and a slope estimation error is less than 3%.

The intelligent electric vehicle with wheel corner module not only exhibits exceptional mobility and stability, but also encounters the challenge of controlling excessive degrees of freedom. In order to enhance yaw stability, the yaw control method grounded in the concept of dynamic stability region is introduced. And the corresponding controllers are built based on the hierarchical control theory. The upper layer control uses the multi-constrained sideslip angle-yaw rate (β–w) phase curve to construct vehicle's dynamic stability region, the vehicle status is categorized into zones of absolute stability, transition and instability range. The stability margin which determines intervention time of the yaw control is designed according to the stability region. The middle layer control generates generalized force to sustain vehicle stability utilizing sliding mode algorithm. The bottom layer control is configured to allocate driving force of each wheel based on the wheel load. The proposed approach's effectiveness has been confirmed through a collaborative simulation involving MATLAB/Simulink and Carsim. The findings indicate that employing the yaw stability control method rooted in the dynamic stability region achieves a 55% reduction in sideslip angle error, thereby significantly enhancing yaw stability.

In order to improve the obstacle avoidance effect of smart car under communication delay, a local obstacle avoidance control strategy of smart car based on remote control considering communication delay is proposed. This paper first classifies the foreseeable risks in the process of driving, and then designs obstacle avoidance strategies and tracking control methods corresponding to different risk levels according to the results of risk classification. Finally, an experiment is designed to verify the effectiveness of the obstacle avoidance strategy proposed in this paper. The results show that the local obstacle avoidance control strategy proposed in this paper can improve the accuracy and real-time performance of the smart car control system in the case of communication delay in remote control.

Nowadays, factors such as global climate change , environmental damage, and the impact of human activities have led to an increase in natural disasters, and the frequency of natural disaster problems is increasing, which poses a great threat to the safety of people’s lives and property. The increased frequency of natural disasters has increased the need to focus on the ability to prevent and respond to disasters. The increased frequency of natural disasters has increased the need to focus on the capacity for disaster prevention and mitigation. All parties need to take appropriate measures to reduce losses and provide assistance after a disaster occurs. Among them, volunteers are a newly emerged important force for rescue, but volunteers have characteristics that normal rescue organizations do not have, such as voluntary and fragmented nature, therefore, volunteer allocation becomes an important issue today. This study tries to construct a volunteer assignment method, which takes into account several factors in the assignment, such as volunteers’ own willingness to the disaster site, the competency of volunteers’ various abilities, the demand of the disaster site for the task and the time satisfaction of the disaster victims, etc. L-T2FNs are introduced in the assignment process to enhance the degree of certainty; Prospect theory’s value function to consider the disaster victims’ psychology; This study proposes an algorithm to reduce evidence fusion conflicts. This algorithm uses a differential evolutionary algorithm based on the Dempster–Shafer theory to train the lowest conflict index (determined by K and evidence distance) for BPA fusion. Subsequently, the assignment is demonstrated by using the 7.8 magnitude earthquake in Turkey in 2023 as an arithmetic example to provide a solution to the problem of how to allocate volunteers to the appropriate disaster sites.

Commit Message Generation (CMG) approaches aim to automatically generate commit messages based on given codediffs, which facilitate collaboration among developers and play a critical role in Open-Source Software (OSS). Very recently, Large Language Models (LLMs) have been applied in diverse code-related tasks owing to their powerful generality. Yet, in the CMG field, few studies systematically explored their effectiveness. This paper conducts the first comprehensive experiment to investigate how far we have been in applying LLM to generate high-quality commit messages and how to go further beyond in this field. Motivated by a pilot analysis, we first construct a multi-lingual high-quality CMG test set following practitioners’ criteria. Afterward, we re-evaluate diverse CMG approaches and make comparisons with recent LLMs. To delve deeper into LLMs’ ability, we further propose four manual metrics following the practice of OSS, including Accuracy, Integrity, Readability, and Applicability for assessment. Results reveal that LLMs have outperformed existing CMG approaches overall, and different LLMs carry different advantages, where GPT-3.5 performs best. To further boost LLMs’ performance in the CMG task, we propose an Efficient Retrieval-based In-Context Learning (ICL) framework, namely ERICommiter, which leverages a two-step filtering to accelerate the retrieval efficiency and introduces semantic/lexical-based retrieval algorithm to construct the ICL examples, thereby guiding the generation of high-quality commit messages with LLMs. Extensive experiments demonstrate the substantial performance improvement of ERICommiter on various LLMs across different programming languages. Meanwhile, ERICommiter also significantly reduces the retrieval time while keeping almost the same performance. Our research contributes to the understanding of LLMs’ capabilities in the CMG field and provides valuable insights for practitioners seeking to leverage these tools in their workflows.