To address the challenges faced by domestic automotive chip manufacturers in developing the Microcontroller Abstraction Layer (MCAL) for the Automotive Open System Architecture (AUTOSAR)-such as insufficient technical expertise and prolonged development cycles, this paper proposes an innovative layered architecture design. This approach redefines the MCAL into 3 distinct layers. Through functional decoupling and standardized interfaces, it achieves compatibility with both AUTOSAR and traditional Software Development Kit (SDK) development models. Validation on Xiaohua Semiconductor's XC277 chip demonstrates that all modules passed unit and integration tests with 100% coverage (including statement, branch, and Modification Condition/Decision Condition Coverage (MC/DC), and critical task execution times reached microsecond levels. This research provides a well-validated solution for technical innovation in domestic AUTOSAR MCAL, holding significant practical implications for enhancing the autonomous and controllable capabilities of China's automotive electronic software.

Based on the SMIC 12 nm Fin Field-Effect Transistor (FinFET) process, this paper analyzes the harmonic distortion and bandwidth performance indicators of the track/hold circuit at the front end of the Analog-to-Digital (A/D) converter. By reasonably selecting the type and size of the track/hold switch, the harmonic distortion caused by the temperature variation of the track/hold switch is reduced, and a new type of switch output buffer is proposed to improve the linearity of the track/hold circuit and the driving capability of the A/D converter. Test results show that: using the gate and parasitic capacitances of N-type Metal-Oxide-Semiconductor (NMOS) and P-type Metal-Oxide-Semiconductor (PMOS) transistors as sampling capacitances, the overall front-end circuit bandwidth is increased to 34 GHz through simulation verification with Cadence Virtuoso; when the sampling frequency f_s=10 GHz and the input signal f_in=9.8 GHz, the Spurious-Free Dynamic Range (SFDR) and Total Harmonic Distortion (THD) reach 67 dB and -59.5 dB, respectively. The proposed design scheme can be applied to the built-in self-test and self-check safety system of autonomous driving, reducing the failure rate of the autonomous driving system and improving the reliability of the embedded vehicle chip safety mechanism.

Regarding failure cases of Silicon Carbide Metal-Oxide-Semiconductor Field-Effect Transistors (SiC MOSFETs) during thermal shock testing, failure defects and causes are analyzed through electrical testing, ultrasonic scanning microscopy, super-depth three-dimensional microscopy, scanning electron microscopy, and other methods. Corresponding solutions are provided, which hold value for enhancing the reliability of SiC MOSFETs and ensuring stable operation of the full vehicle.

Facing the technical bottlenecks in ensuring the safety and reliability of environmental perception in autonomous driving, this paper systematically reviews the technological roadmap and recent developments of LiDAR-camera fusion-based object detection. By analyzing the limitations of single-sensor perception under complex road conditions, the study demonstrates the inevitability of multimodal sensor fusion. The findings indicate that sensor fusion can significantly improve object detection performance in scenarios involving occlusion, long distances, and complex environments. Experiments on public datasets such as nuScenes and KITTI further verify that sensor fusion effectively enhances perception robustness and detection accuracy in diverse scenes. However, current technologies still face challenges such as insufficient real-time performance, high algorithmic complexity, and cost constraints. Future research should focus on improving the precision and efficiency of fusion algorithms and promoting the standardization of multimodal collaborative perception.

To avoid the missing detection phenomenon caused by loss of local features due to the occlusion and truncation under complex traffic scenes, a LiDAR-based 3D object detection algorithm based on an improved PointPillars framework is proposed. First, the point cloud feature encoding network in PointPillars is enhanced to better preserve important structural information and the relationships within the point cloud data. Then, multi-scale point cloud features are extracted and aggregated through stacked sparse convolution modules and submanifold convolution modules. On the bird's-eye-view feature map that fuses these multi-scale features, 3D bounding boxes of objects are classified and regressed, improving the network's operational efficiency. Finally, experimental tests are conducted on the KITTI dataset. The results show that compared to the traditional PointPillars algorithm, the proposed 3D object detection method improves the Average Precision (AP) of 3D detection for challenging vehicle samples by 6.02 percentage points, and increases the AP for simple pedestrian and cyclist samples in the bird's-eye view by 3.35 and 3.18 percentage points respectively, effectively enhancing 3D object detection accuracy.

To enhance the accuracy of environmental perception and object detection for intelligent vehicles, this paper proposes a novel feature fusion-based 3D object detection method. First, the Transformer model is improved by leveraging a multi-head self-attention mechanism to fully capture local spatial feature information while preserving diverse feature weights, enabling the extraction of point cloud and image features. An Unscented Kalman Filter (UKF) is then utilized to design a multimodal fusion system that combines image and point cloud data, ultimately achieving 3D object detection. The proposed model is trained and tested on both public and private datasets and is compared against several state-of-the-art algorithms. Experimental results demonstrate that the proposed feature fusion-based 3D object detection model achieves an improvement in mean Average Precision (mAP) of 0.34 percentage points and 3.03 percentage points compared to widely used algorithms such as F-ConvNet and F-PointNet, respectively. For vehicle and cyclist detection tasks, the mAP increases by 2.52 percentage points and 9.32 percentage points, respectively. Moreover, the inference results on real-world vehicle data align closely with the training evaluation outcomes. These findings indicate that the proposed algorithm improves the accuracy of object detection in perception systems to a certain extent and provides a valuable reference for the design of object detection systems in autonomous driving.

To address the challenges faced by conventional lidar systems in accurately filtering out water spray and dust, highly reflective expansion crosstalk noise, and the suboptimal removal of large-scale outliers, a laser point cloud denoising algorithm based on ground fitting and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is proposed. Initial noise location information is obtained through ground point cloud segmentation, an enhanced DBSCAN algorithm, and a three-dimensional bounding box estimation method. Noise is then marked and filtered out based on its geometric and physical characteristics. The experimental results demonstrate that in dust and water spray scenarios, the noise-reduced Chamfer Distance (CD) decreases from 0.037 4 m to 0.019 8 m, while the Point-to-Mesh (P2M) distance reduces from 0.158 m to 0.088 m. The overall geometric deviation of the point cloud is significantly diminished, with improved conformity to the true surface. For processing complex point cloud data, the optimised DBSCAN clustering achieves a computational time of merely 6 ms compared to the conventional DBSCAN algorithm, representing a 77.8% improvement in computational efficiency. This effectively resolves the suboptimal outlier filtering performance of traditional laser point cloud denoising algorithms at large scales.

By using the calculation method of one-dimensional-three-dimensional joint simulation, the influences of AGS layout form on the intake air volume of the front cabin, the heat dissipation of the cooling module and the energy consumption of the air conditioning compressor were explored. The research finds that when the distance between AGS blades and the grille is 57~97 mm, the effects of direct and non-direct arrangements on heat dissipation performance and energy consumption tend to be consistent. The attenuation of intake air volume is approximately 2.4%, the reduction of heat dissipation is less than 1%, and there is almost no impact on the energy consumption of the compressor. When the distance is less than 57 mm (close to the grille), the direct arrangement is superior to the staggered arrangement. This is reflected in the fact that the intake air volume increases by 3.16% compared to the staggered arrangement, the heat dissipation increases by approximately 2%, and the energy consumption of the compressor decreases by about 2%. When the distance is greater than 97 mm (close to the cooling module), the adoption of a staggered arrangement is more conducive to reducing the air intake attenuation caused by AGS blades.