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The roof sensor system is an indispensable part of intelligent vehicles to observe the environment, however, it deteriorates the aerodynamic and noise performance of the vehicle. In this paper, large eddy simulation and the acoustic perturbation equation are combined to simulate the flow and sound fields of the intelligent vehicle. Firstly, test and simulation differences of aerodynamic drag and pressure coefficients on the roof and rear of the intelligent vehicle without roof sensor system are discussed. It is found that the difference in aerodynamic drag coefficient is 5.5%, and the pressure coefficients’ differences at 21 out of 24 measurement points are less than 0.05. On this basis, under the influence of the sensor system, the aerodynamic drag coefficient of the intelligent vehicle is increased by 23.4%.

High altitudes have a significant effect on the real driving emissions (RDE) of vehicles due to lower pressure and insufficient oxygen concentration. In addition, type approval tests for light-duty vehicles are usually conducted at altitudes below 1000 m. In order to investigate the influence of high altitude on vehicles fuel economy and emissions, RDE tests procedure had been introduced in the China VI emission regulations. In this study, the effect of altitude on fuel economy and real road emissions of three light-duty gasoline vehicles was investigated. The results indicated that for vehicles fuel economy, fuel consumption (L/100 km) for the tested vehicles decreased while the mean exhaust temperature increased with an increase in altitudes. Compared to near sea level, the fuel consumption (L/100 km) of the tested vehicle was reduced by up to 23.28%.

Experimental schemes, frequency characteristics, subjective and objective sound quality evaluation and sound quality prediction model establishment of a certain mass-production SUV (Sport Utility Vehicle, SUV) manual transmission gear rattle phenomenon were analyzed in this paper. Firstly, vehicle experiments, including experiment conditions, vibration acceleration sensor and microphone arrangements and especial considerations in experiments, were described in detail. Secondly, through time-frequency analysis, broadband characteristics of manual transmission gear rattle noise were identified and vibro-impact of gear rattle occurs in the frequency range of 450~4000Hz on the vehicle idle condition and the creeping condition. Thirdly, based on bandwidth filtering processing of gear rattle noise, subjective assessment experiments by a paired comparison method were carried out.

During light to moderate braking at high speed, the local high temperature phenomenon can be observed on the brake disc surfaces, known as hot spots. The occurrence of hot spots will lead to negative effects such as brake performance fade, thermal judder and local wear, which seriously affect the performance of vehicle NVH. In this paper, based on the bench test of a ventilated disc brake, the basic characteristics of hot spots is obtained and the evolution process of temperature field and disc deformation is analyzed in detail. In temperature field, hot bands appear first and grow, migrate from inner and outer radius to the middle, with the growing temperature fluctuation and finally hot spots appear in the middle radius of the brake disc. The stable SRO waviness forms much earlier than the temperature fluctuation. In the stop brake studied in this paper, the SRO waviness stabilizes in main 7 order state which is lower than the final hot spot order.

On the study of reducing the disturbance on driver’s attention induced by low frequency vehicle interior stationary noise, a subjective evaluation is firstly carried out by means of rank rating method which introduces Distraction Level (DL) as evaluation index. A visual-finger response test is developed to help evaluating members better recognize the Distraction Level during the evaluation. A non-linear back propagation artificial neural network (BPANN) is then modeled for the prediction of subjective Distraction Level, in which linear sound pressure RMS amplitudes of five Critical Band Rates (CBRs) from 20 to 500Hz are selected as inputs of the model. These inputs comprise an input vector of BPANN. Furthermore, active noise equalization (ANE) on DL is realized based on Filtered-x Least Mean Square (FxLMS) algorithm that controls the gain coefficients of inputs of trained BPANN.

Reinforcement learning is a promising approach to solve the energy management for hybrid electric vehicles. In this paper, based on the DQN (Deep Q-Network) reinforcement learning algorithm which is widely used at present, double DQN, dueling DQN and learning from demonstration are integrated; states, actions, rewards and the experience pool based on the characteristics of series-parallel multi-speed hybrid powertrain are designed; the hybrid energy management strategy based on D4QN (Double Dueling Deep Q-Network with Demonstrations) algorithm is established. Based on the training results of D4QN algorithm, multi-parameter analysis under state and action space, HCU (Hybrid control unit) application and MIL (Model in-loop) test research are conducted.

Highly accurate parking slot detection methods are crucial for Automated Valet Parking (AVP) systems, to meet their demanding safety and functional requirements. While previous efforts have mostly focused on the algorithms’ capabilities to detect different types of slots under varying conditions, i.e. the detection rate, their accuracy has received little attention at this time. This paper highlights the importance of trustworthy slot detection methods, which address both the detection rate and the detection accuracy. To achieve this goal, an accurate slot detection method and a reliable ground-truth slot measurement method have been proposed in this paper. First, based on a 2D laser range finder, datapoints of obstacle vehicles on both sides of a slot have been collected and preprocessed. Second, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm has been improved to efficiently cluster these unevenly-distributed datapoints.

In the Kmeans cluster segmentation used in traffic scenes, there are often zone optimization and over-segmentation problems caused by the algorithm randomly assigning the initial cluster center. In order to improve the target extraction effect in traffic road scenes, this article proposes an improved Kmeans (IM-Kmeans) method. Firstly, search for the histogram peaks of the whole pixels based on hue, saturation, value (HSV) image, and find the initial cluster centers’ positions and number. Secondly, the noise points which are far away from the center pixel are removed, and then the pixels are classified into the nearest cluster center according to its value. Finally, after the clustering model reaches convergence, the area-clustering method is used for another classification to solve the over-segmentation problem. The simulation and experimental comparisons show that the IM-Kmeans algorithm has higher clustering accuracy than the traditional Kmeans algorithm.

The noise tests of electric motor systems serve as the foundation for studying their acoustic issues. However, there is currently a lack of corresponding method for identifying key noise characteristics, such as the noise frequency range that needs to be considered, the significant noise order, and the resonance band worth paying attention to, based on experimental test data under actual operating conditions. This article proposes a method for identifying the noise characteristics of an electric motor system based on tests conducted under distinct operating conditions, which consists of three parts: identifying the primary frequency range, the significant order, and the important resonance band. Firstly, in order to extract noise with the primary energy, the concept of noise contribution is introduced. The primary frequency range and the significant order under a specific operating condition can be obtained after extraction.

In recent years, with the development of computing infrastructure and methods, the potential of numerical methods to reasonably predict aerodynamic noise in turbocharger compressors of heavy-duty diesel engines has increased. However, aerodynamic acoustic modeling of complex geometries and flow systems is currently immature, mainly due to the greater challenges in accurately characterizing turbulent viscous flows. Therefore, recent advances in aerodynamic noise calculations for automotive turbocharger compressors were reviewed and a quantitative study of the effects for turbulence models (Shear-Stress Transport (SST) and Detached Eddy Simulation (DES)) and time-steps (2° and 4°) in numerical simulations on the performance and acoustic prediction of a compressor under various conditions were investigated.

As a key component of in-vehicle intelligent voice technology, speech enhancement can extract clean speech signals contaminated by environmental noise to improve the perceptual quality and intelligibility of speech. It has extensive applications in the field of intelligent car cabins. Although some end-to-end speech enhancement methods based on time domain have been proposed, there is often limited consideration given to designing model architectures based on the characteristics of the speech signal. In this paper, we propose a new U-Net based speech enhancement framework that utilizes the temporal correlation of speech signals to reconstruct higher-quality and more intelligible clean speech.

Speech enhancement can extract clean speech from noise interference, enhancing its perceptual quality and intelligibility. This technology has significant applications in in-car intelligent voice interaction. However, the complex noise environment inside the vehicle, especially the human voice interference is very prominent, which brings great challenges to the vehicle speech interaction system. In this paper, we propose a speech enhancement method based on target speech features, which can better extract clean speech and improve the perceptual quality and intelligibility of enhanced speech in the environment of human noise interference. To this end, we propose a design method for the middle layer of the U-Net architecture based on Long Short-Term Memory (LSTM), which can automatically extract the target speech features that are highly distinguishable from the noise signal and human voice interference features in noisy speech, and realize the targeted extraction of clean speech.

In the process of automobile industrialization, integrated electric drive systems turn to be the mainstream transmission system of electric vehicles gradually. The main sources of noise and vibration in the chassis are from the gear reducer and motor system, as a replacement of engine. For improving the electric vehicles NVH performance, effective identification and quantitative analysis of the main noise sources are a significant basis. Based on the rotating hub test platform in the semi-anechoic chamber, in this experiment, an electric vehicle equipped with a three-in-one electric drive system is taken as the research object. As well the noise and vibration signals in the interior vehicle and the near field of the electric drive system are collected under the operating conditions of uniform speed, acceleration speed, and coasting with gears under different loads, and the test results are processed and analyzed by using the spectral analysis and order analysis theories.