To eliminate the defects of incomplete detection and high false detection rate caused by insignificant pedestrian target features, dense small targets and complex background in infrared images, this paper proposes an infrared pedestrian target detection algorithm based on improved YOLOv7. Firstly, the original Spatial Pyramid Pooling (SPP) module is replaced by the Channel Attention based Spatial Pyramid Pooling (CASPP) module based on the YOLOv7-tiny model, so that the model could pay more attention to the extraction of pedestrian features; then, the convolution module CBM based on the Meta-ACON activation function is introduced, which further suppressed the background noise and preserved the details of the pedestrians; finally, an alpha fusion data enhancement method is proposed to enrich the diversity of samples and improve the stability of the model in complex environments. The validation based on the FLIR dataset shows that the proposed method improves the accuracy by 3% and reduces the computation by 38% compared with the YOLOv7-tiny algorithm, which is more suitable for infrared pedestrian target detection scenarios.

In response to the multilane lines driving environments such as lane line occlusion, road shadows, where the extractes lane line feature information is missing, resulting in blurry and discontinuous predicted lane lines, this paper proposes a lightweight U²-Net network for lane line detection algorithm. Firstly, the Residual U-blocks (RSU) module of the lightweight U²-Net network and multi feature scale fusion are used to obtain globally informative lane line features; secondly, pixel-by-pixel threshold judgment is performed on lane line features, and the least square method is selected combined with the lane line cluster of Region Of Interest (ROI) to fit lane line, to achieve multilane line detection and determine the self lane line area; finally, the proposed lane detection algorithm is validated and analyzed in the TuSimple dataset. The results show that the average accuracy of the proposed lane line detection algorithm reaches 98.4%. Compared with other lane line detection networks, this algorithm has fewer network parameters and higher accuracy.

To satisfy the requirement of low power consumption vehicle computing platform for lane detection, this paper proposes a low computing power dependent real-time lane recognition method. Considering the variation of illumination during vehicle driving, a color separation method based on adaptive illumination to extract lane characteristics is proposed. The effective edge point form is defined and the lane lines are determined by edge point voting based on the classical edge detection and Hough transform algorithm. The lane lines are used to filter and supplement the edge points and the lane curve equation is obtained by using the random sample consensus algorithm. The results show that the proposed method achieves a recognition accuracy of over 98% and computation speed of 38 frames per second on a low power processor. Furthermore, the method has proven to be stable and robust in a variety of scenarios.

To address the issue of extensive blind spots during right turns due to the oversized nature of dump trucks, this paper proposes a dynamic detection algorithm for risk targets in the right-turn blind spots of dump trucks. The algorithm improves the YOLOv8 model by enhancing the C2f module and lossing calculation module to refine the model’s detection accuracy. Additionally, four position threshold lines are preset in the blind spots, the risk warning module of the blind spots of the dump truck is added, and the auxiliary driving system of the blind spots of the dump truck is established. The results indicate that the proposed dynamic detection algorithm can recognize various types of targets, including cars, trucks, buses, pedestrians and electric bicycles, with a mean Average Precision (mAP50) of 0.87 at a 50% intersection over union threshold for all categories of targets. The right-turn blind spots assisted driving system of the dump truck can make different degrees of early warning according to the position of the risk target box in the image.

To address the issue of large amount of computation, slow arithmetic speed of the current defogging video model that can only process single image, this article proposed a defogging simplified model for vehicular video based on guide filtering. Firstly, the law of atmospheric attenuation index of light energy and physical model of haze degradation were introduced, then the defogging simplified model was obtained by correcting the attenuation law and the degradation model, and two parameters of the fog concentration factor and ambient air light were determined with the gray scale method and guided filtering method. Finally, the model was verified by computer simulation. The simulation results show that the defogging simplified model works effectively to defog for continuous video.

This paper systematically sorts out the generation methods of simulation test scenarios for autonomous vehicle, summarizes the latest research progress in the fields of autonomous vehicle simulation test scenario definition, scenario deconstruction, scenario generation based on data driven, and scenario generation based on mechanism modeling, and summarizes the relevant evaluation and application of test scenarios. Finally, the paper proposes that future research should focus on integrating the characteristics of Chinese driving scenarios, deepening the research on edge scenario generation strategies, and accelerating the construction of the standard system of scenario construction.

This paper takes the autonomous driving scenario library as the research object,and by analyzing shortcomings of autonomous driving scenario library and industry demand, and formes a set of operation guidelines for autonomous driving scenarios construction based on real world traffic data. Firstly, the elements and formats of autonomous driving scenarios have been illustrated and standardized. Secondly, scenario datasets are generated by a three-step procedure, including scenario mining, automated annotating and data compliance desensitization. Finally, a set of safe, compliant, high-quality, and high-value city level challenging autonomous driving scenarios was ultimately formed by risk assessment of real collection scenarios and standardized processing of abnormal events.

To test the steering function of autonomous vehicles, a vehicle in the loop virtual simulation testing system design is designed. A rotating plate driven by a linear motor is added to the front axle drum group on the traditional drum/brake testing platform to achieve a maximum 10° steering function during vehicle driving. Combined with software simulation technology, dynamic braking force testing is carried out for the speed reduction of autonomous vehicles from 80 km/h to 20 km/h during steering operation, as well as braking friction force testing during the speed reduction to 0 km/h. The test results are analyzed and verified. The analysis results indicate that the testing system can achieve the expected braking ability of autonomous vehicles within a steering angle range of ±10°, and control the testing braking force accuracy within the range of ±5 N, whitch further verifies the effectiveness of the testing system.

In order to investigate the effects of Ionic Liquid (IL) additives on the tribological properties of the key pairs of internal combustion engine, nitriding cylinder liner and molybdenum spray piston ring under high-reinforcement conditions, the lubricating oil with 2% ionic liquid mass fraction is used to lubricate the pairs. By analyzing the surface morphology, composition and tribological chemical reaction products of the worn pairs, the paper investigates wear behavior and relates mechanism of pair under ionic liquid lubrication, and evaluates its influence on friction, wear and wear characteristics of pairs. The experimental results show that the ionic liquid additive can improve the tribological properties of nitriding cylinder liner and molybdenum injection piston ring pair. The ionic liquid has the best tribological properties at 180 ℃, which is related to the tribological chemical reaction of ionic liquid on the surface of cylinder liner and its products.

In order to analyze the dynamic characteristics of electric vehicle body, a modal test is used to identify the modal parameters of an electric vehicle Body-In-White (BIW), and the test results are compared with traditional modal test results through the acceleration sensor. The results show that the maximum error of natural frequency of BIW obtained through 3D laser vibrometry is less than 1.3% and have a high consistency with the vibration mode obtained from the traditional modal test, which verifies the feasibility and accuracy of this test method.