Jianjun Yang’s research while affiliated with Xihua University and other places

With the popularity of intelligent automobiles, the comfort of the cockpit has been improved. This paper focuses on the comfort of smart cabin, combines the physical environment factors of the cabin and the level of intelligentization of the cabin, and proposes an integrated assessment method for the comfort of smart cabin. Firstly, to establish an integrated assessment system for automobile smart cabin comfort, the study identified five factors that reflect the comfort of an automobile smart cabin: sound, light, thermal, air quality and intelligentization. Eight experimental vehicles were tested on the road, and experts were invited to evaluate the cabin comfort under various operating conditions. The relationship between each comfort index and its corresponding score was then established and a fitting formula derived. Two objective weights were obtained using the EWM and LightGBM, and game theory was used to combine them with the subjective weights obtained using the FAHP, resulting in two combined weights. In addition, the weights were optimized based on the CV coefficient and the KMO measure. Finally, by combining the optimal weighting scheme and the fitting formula, the integrated evaluation model for the automotive smart cabin was established. The effectiveness and feasibility of the model were verified through field studies.

Cloud model similarity algorithm is an important part of cloud modelling theory. Most of the existing cloud model similarity algorithms suffer from poor discriminability, poor classification, unstable results, and low time efficiency. In this paper, a new similarity algorithm is proposed that considers the triangular cloud model distance and shape. First, according to the ${{D}}_{\text{T}}$ D T distance formula, a new exponential closeness measure is defined, with which the distance similarity of cloud models is characterized. Then, the shape similarity is calculated according to the variance of the cloud model cloud drops. Finally, the two similarities are synthesized to define a similarity algorithm for determining the distance from the ${{D}}_{\text{T}}$ D T distance formula and shape based on the triangular cloud model (DD T STCM). In this paper, discriminability, stability, efficiency and theoretical interpretability are taken as the evaluation indices. Equipment security system capability evaluation experiment, cloud model differentiation simulation experiment and time series classification accuracy experiment are set up to verify the effectiveness of the algorithm in terms of the four above aspects. The experimental results show that DD T STCM has good differentiation and excellent classification effects. In the classification experiment for the time series, the average classification accuracy of DD T STCM reaches 91.78%, which is at least 2.78% higher than those of the other seven commonly used algorithms. The CPU running efficiency of DD T STCM is also extremely high, and the average CPU running time of group training is always on the order of milliseconds, which effectively reduces the time cost. Finally, a case study is conducted to analyse a risk assessment problem for China’s island microgrid industry, and the evaluation results based on DD T STCM are in line with human cognition and have good value for engineering applications. Graphical abstract

With the popularization of intelligent cars, users’ understanding of the value of cars gradually changes from a travel tool to a "third living space", and cabin comfort is becoming a criterion for evaluating the goodness of cars. In this paper, we start from the physical environment and human-computer interaction environment that affect the comfort of the intelligent cockpit of a car and establish a comprehensive comfort evaluation model of the intelligent cockpit of a car based on the support vector machine (SVM) algorithm in machine learning by conducting experiments on the comfort evaluation of the intelligent cockpit of a car and compare it with several classical machine learning algorithms. The mean square error ( MSE )of the model based on the SVM algorithm is 0.00096, and the coefficient of determination ( R ² ) reaches 0.966, which is better than several other algorithms. The results show that the established evaluation model has good generalization ability and can evaluate the comprehensive comfort of the intelligent cockpit of the car, thus helping the cockpit to make timely and accurate comfort adjustments to ensure the occupant’s riding experience. This project provides a reference direction for the comprehensive evaluation of cockpit comfort, which is of great significance for the future development of intelligent cockpit comfort. In addition, the comfort model can be applied to a variety of comfort evaluation scenarios, which has great practical value.

It is important to be able to measure the similarity between two uncertain concepts for many real-life AI applications, such as image retrieval, collaborative filtering, risk assessment, and data clustering. Cloud models are important cognitive computing models that show promise in measuring the similarity of uncertain concepts. Here, we aim to address the shortcomings of existing cloud model similarity measurement algorithms, such as poor discrimination ability and unstable measurement results. We propose an EPTCM algorithm based on the triangular fuzzy number EW -type closeness and cloud drop variance, considering the shape and distance similarities of existing cloud models. The experimental results show that the EPTCM algorithm has good recognition and classification accuracy and is more accurate than the existing Likeness comparing method (LICM), overlap-based expectation curve (OECM), fuzzy distance-based similarity (FDCM) and multidimensional similarity cloud model (MSCM) methods. The experimental results also demonstrate that the EPTCM algorithm has successfully overcome the shortcomings of existing algorithms. In summary, the EPTCM method proposed here is effective and feasible to implement.

With the rapid development of the automobile industry, the comfort of the cockpit has become the standard for judging the quality of the car. People have also put forward higher requirements for cockpit comfort. In the process of driving, the cockpit environment will constantly change, and the comfort will also change. When the comprehensive comfort level of the cockpit decreases and the occupants feel uncomfortable, the cockpit comfort should be adjusted. In this article, a cockpit comfort evaluation model is established to realize the evaluation of cockpit comfort. In addition, we elaborate the theory of optimal state distance, where the numerical magnitude of the optimal state distance is used to reflect the extent to which an indicator deviates from its optimal state. Also, a cockpit optimal adjustment strategy identification model is established based on the theory, which can obtain the optimal adjustment strategy in a certain cockpit operating environment, facilitate the timely adjustment of the corresponding actuator, and realize the dynamic monitoring and adjustment of cockpit comfort. This project provides a reference direction for cockpit comfort adjustment, which is of great significance for future research and development of automotive cockpit comfort.

Aiming at the comfort evaluation of automobile intelligent cockpit, an evaluation model based on improved combination weighting-cloud model is established. By consulting relevant literature, 4 first-class indexes and 15 second-class indexes, including noise and vibration, light environment, thermal environment and human-computer interaction, are selected to establish a comfort evaluation system. Later the subjective and objective weights obtained by improved Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) are combined by Game Theory. Considering the fuzziness and randomness of the index system, the combination weights obtained by Game Theory are combined with the cloud model. The floating cloud algorithms is used to determine the first-class and second-class index clouds and the comprehensive evaluation cloud parameters. Improvements were made in two commonly used similarity calculation methods, the expectation curve method (ECM) and the maximum boundary curve method (MCM). A new similarity calculation method is defined to optimize the evaluation results and determine the final comfort evaluation grade. Lastly, a 2021 Audi intelligent car under a certain working condition was selected to verify the correctness and rationality of the model using the fuzzy evaluation method. The results show that the cockpit comfort evaluation model based on the improved combination weighting-cloud model can better reflect the comprehensive comfort of automobile cockpit.

This paper used the data of automobile traffic accidents from 2018 to 2020 in the Chinese National Automobile Accident In-Depth Investigation System. The prediction features of traffic accident severity are innovated. Four accident features that did not participate in the importance ranking were added: accident location, accident form, road information, and collision speed. Eight accident features (engine capacity, hour of day, age of vehicle, month of year, day of week, age band of drivers, vehicle maneuver, and speed limit) have been used in previous studies. Random forest was used to rank the importance of 12 accident features, and 7 important accident features were finally adopted. By comparing the algorithms and optimizing the results, the prediction model of traffic accident degree with higher accuracy is finally obtained.

With the continuous improvement of people's living standards, the number of cars has also increased dramatically. While cars are convenient for people to travel, they also lead to increasingly serious traffic safety problems. For this reason, this paper uses the fault tree and Bayesian network methods to conduct an in-depth study on the causes of pedestrian-vehicle traffic accidents from three aspects: people, vehicle, road and the environment. In this paper, the occurrence of pedestrian-vehicle traffic accidents is divided into 29 basic events. The basic events of each of the 381 pedestrian-vehicle traffic accidents were Classified by 0–1. A fault tree model leading to pedestrian-vehicle traffic accidents is established, which is then transformed into a Bayesian network model, and Bayesian network inference, sensitivity analysis is performed with the help of Netica software. Our results suggest that illegal crossing of traffic lanes, speeding, rainy day, slippery road, braking is not timely, visual impairment are the main causes of pedestrian-vehicle traffic accidents. These results can not only provide a reference for transportation technology, but also provide a basis for government legislation.

Under the background of automobile intelligence, cockpit comfort is receiving increasing attention, and intelligent cockpit comfort evaluation is especially important. To study the intelligent cockpit comfort evaluation model, this paper divides the intelligent cockpit comfort influencing factors into four factors and influencing indices: acoustic environment, optical environment, thermal environment, and human–computer interaction environment. The subjective and objective evaluation methods are used to obtain the subjective weights and objective weights of each index by the analytic hierarchy process and the improved entropy weight method, respectively. On this basis, the weights are combined by using the game theory viewpoint to obtain a comprehensive evaluation model of the intelligent automobile cockpit comfort. Then, the cloud algorithm was used to generate the rank comprehensive cloud model of each index for comparison. The research results found that among the four main factors affecting the intelligent automobile cockpit comfort, human–computer interaction has the greatest impact on it, followed by the thermal environment, acoustic environment, and optical environment. The results of the study can be used in intelligent cockpit design to make intelligent cockpits provide better services for people.

With the development of intelligence and network connectivity, the development of the automotive industry is also moving toward intelligent systems. For passengers, the utility of intelligence is to achieve more convenience and comfort. The intelligent cockpit is the place where passengers directly interact with the car, which directly affects the experience of passengers in the car. For the intelligent cockpits that have emerged in recent years, a reasonable and accurate comfort evaluation model is urgently needed. Therefore, in this article, from the passenger’s perspective, a subjective evaluation experiment was set up to collect data on four important indicators affecting the comfort of the intelligent cockpit: sound, light, heat, and human–computer interaction. The subjective evaluation weights were derived from a questionnaire, and the entropy weighting method was used to obtain the objective weights. Finally, the two weights were combined using the idea of game theory combination assignment to get the final accurate weights. Using the idea of penalty type substitution, the four index models were then synthesized to get the final evaluation model. The feasibility of the model was verified when measuring the car cockpit. The feasibility of the method means it can evaluate the comfort level of an intelligent cockpit more reasonably, facilitate the enhancement and improvement of the model, and promote the development of the model to achieve maximum passenger comfort.