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Roundabouts are one of the most complex traffic environments in urban roads, and a key challenge for intelligent driving decision-making. Deep reinforcement learning, as an emerging solution for intelligent driving decisions, has the advantage of avoiding complex algorithm design and sustainable iteration. For the decision difficulty in roundabout scenarios, this paper proposes an artificial potential field based Soft Actor-Critic (APF-SAC) algorithm. Firstly, based on the Carla simulator and Gym framework, a reinforcement learning simulation system for roundabout driving is built. Secondly, to reduce reinforcement learning exploration difficulty, global path planning and path smoothing algorithms are designed to generate and optimize the path to guide the agent.

This paper addresses the issues of long-term signal loss in localization and cumulative drift in SLAM-based online mapping and localization in autonomous valet parking scenarios. A GPS, INS, and SLAM fusion localization framework is proposed, enabling centimeter-level localization with wide scene adaptability at multiple scales. The framework leverages the coupling of LiDAR and Inertial Measurement Unit (IMU) to create a point cloud map within the parking environment. The IMU pre-integration information is used to provide rough pose estimation for point cloud frames, and distortion correction, line and plane feature extraction are performed for pose estimation. The map is optimized and aligned with a global coordinate system during the mapping process, while a visual Bag-of-Words model is built to remove dynamic features.

In the field of autonomous driving trajectory planning, it’s virtual to ensure real-time planning while guaranteeing feasibility and robustness. Current widely adopted approaches include decoupling path planning and velocity planning based on optimization method, which can’t always yield optimal solutions, especially in complex dynamic scenarios. Furthermore, search-based and sampling-based solutions encounter limitations due to their low resolution and high computational costs. This paper presents a novel spatio-temporal trajectory planning approach that integrates both search-based planning and optimization-based planning method. This approach retains the advantages of search-based method, allowing for the identification of a global optimal solution through search. To address the challenge posed by the non-convex nature of the original solution space, we introduce a spatio-temporal semantic corridor structure, which constructs a convex feasible set for the problem.

Semi-active suspension system (SASS) could enhance the ride comfort of the vehicle across different operating conditions through adjusting damping characteristics. However, current SASS are often calibrated based on engineering experience when selecting parameters for its controller, which complicates the achievement of optimal performance and leads to a decline in ride comfort for the vehicle being controlled. Linear quadratic constrained optimal control is a crucial tool for enhancing the performance of semi-active suspensions. It considers various performance objectives, such as ride comfort, handling stability, and driving safety. This study presents a control strategy for determining optimal damping force in SASS to enhance driving comfort. First, we analyze the working principle of the SASS and construct a seven-degree-of-freedom model.

With the progressing autonomy of driving technology, machine is assuming greater responsibility for driving tasks to enhance safety. Leveraging this potential, this paper introduces a novel human-machine co-steering control strategy based on model predictive control. The strategy is designed to address the difficulties faced by drivers when driving on surfaces with low adhesion. Firstly, the proposed strategy utilizes a parallel human-machine co-steering framework with a weight allocation concept between the controller and the driver. Moreover, the nonlinear controller dynamics model and linear driver dynamics model are developed to characterize the interaction behaviors between human and machine under low-adhesion road surface conditions. And a nonlinear game optimization problem is formulated to capture the cooperative interaction relationship between human and machine.

Ammonia has attracted the attention of a growing number of researchers in recent years. However, some properties of ammonia (e.g., low laminar burning velocity, high ignition energy, etc.) inhibit its direct application in engines. Several routes have been proposed to overcome these problems, such as oxygen enrichment, partial fuel cracking strategy and co-combustion with more reactive fuels. Improving the reactivity of ammonia from the oxidizer side is also practical. Ozone is a highly reactive oxidizer which can be easily and rapidly generated through electrical plasma and is an effective promoter applicable for a variety of fuels. The dissociation reaction of ozone increases the concentration of reactive radicals and promotes chain-propagating reactions. Thus, obtaining accurate rate constants of reactions related to ozone is necessary, especially at elevated to high pressure range which is closer to engine-relevant conditions.

To achieve higher efficiencies and lower emissions, dual-fuel strategies have arisen as advanced engine technologies. In order to fully utilize engine fuels, understanding the combustion chemistry is urgently required. However, due to computation limitations, detailed kinetic models cannot be used in numerical engine simulations. As an alternative, approaches for developing reduced reaction mechanisms have been proposed. Nevertheless, existing simplified methods neglecting the real engine combustion processes, which is the ultimate goal of reduced mechanism. In this study, we propose a novel simplified approach based on fuel reactivity. The high-reactivity fuel undergoes pyrolysis first, followed by the pyrolysis and oxidation of the low-reactivity fuel. Therefore, the simplified mechanism consists of highly lumped reactions of high-reactivity fuel, radical reactions of low-reactivity fuel and C0-C2 core mechanisms.

In order to meet increasingly stringent emission regulations, it is significance to establish a control- oriented transient NOx and PM emission prediction model and improve the accuracy and real-time performance. In this study, the prediction model of transient PM emissions based on Transformer is established. In terms of model accuracy and real-time performance, Transformer emission prediction model is compared with Multilayer perceptron (MLP) neural network and Long-Short Term Memory (LSTM) emission prediction model. The results show that the performance of Transformer transient emission prediction model is superior to other model structures, it can be used for real-time prediction.

Considering the change of vehicle future power demand in the process of energy distribution can improve the fuel saving effect of hybrid system. However, current studies are mostly based on historical information to predict the future power demand, where it is difficult to guarantee the accuracy of prediction. To tackle this problem, this paper combines hybrid energy management with predictive cruise control, proposing a hierarchical control strategy of predictive energy management (PEM) that includes two layers of algorithms for speed planning and energy distribution. In the interest of decreasing the energy consumed by power components and ensuring transportation timeliness, the upper-level introduces a predictive cruise control algorithm while considering vehicle weight and road slope, planning the future vehicle speed during long-distance driving.

The hippocampus plays a crucial role in brain function and is one of the important areas of concern in closed head injury. Hippocampal injury is related to a variety of factors including the strength of mechanical load, animal age, and helmet material. To investigate the order of these factors on hippocampal injury, a three-factor, three-level experimental protocol was established using the L9(34) orthogonal table. A closed head injury experiment regarding impact strength (0.3MPa, 0.5MPa, 0.7MPa), rat age (eight- week-old, ten-week-old, twelve-week-old), and helmet material (steel, plastic, rubber) were achieved by striking the rat's head with a pneumatic-driven impactor. The number of hippocampal CA3 cells was used as an evaluation indicator. The contribution of factors to the indicators and the confidence level were obtained by analysis of variance.

With the aim of addressing the problem of inconsistency of the traditional proportion integration (PI) driver model with the actual driving behavior, a longitudinal driver model based on the elastic net regression (ENR) and adaptive network fuzzy inference system (ANFIS) method is proposed. First, longitudinal driving behavior data are collected through bench tests to extract the characteristic parameters that affect driving behavior. A quadratic regression model is established after considering the nonlinear characteristics of the driver behavior. The multi-collinear problem of high-dimensional variables in the regression model is solved by the ENR method, and the parameters with significant influence on driving behavior selected. A longitudinal driver model of ANFIS was established with the selected characteristic parameters as input. Finally, the validity of the model is verified by comparing it with the PI and ENR driver models.

The RON(Research Octane Number) is the most important indicator of motor petrol, and the petrol refining process is one of the important links in petrol production. However, RON is often lost during petrol refining and RON Loss means the value of RON lost during petrol refining. The prediction of the RON loss of petrol during the refining process is helpful to the improvement of petrol refining process and the processing of petrol. The traditional RON prediction method relied on physical and chemical properties, and did not fully consider the high nonlinearity and strong coupling relationship of the petrol refining process. There is a lack of data-driven RON loss models. This paper studies the construction of the RON loss model in the petrol refining process.

Increasingly strict emission regulations and unfavorable economic climate bring severe challenges to the energy conservation of marine low-speed engine. Besides traditional methods, the energy and exergy analysis could acknowledge the losses of fuel from a global perspective to further improve the engine efficiency. Therefore, the energy and exergy analysis is conducted for a marine low-speed engine based on the experimental data. Energy analysis shows the exhaust gas occupies the largest proportion of all fuel energy waste, and it rises with the increment of engine load. The heat transfer consumes the second largest proportion, while it is negatively correlated to engine load. The energy analysis indicates that the most effective way to improve the engine efficiency is to reduce the energy wasted by exhaust gas and heat transfer. However, the latter exergy analysis demonstrates that there are other effective approaches to improve the engine efficiency.

To improve the combustion and emission characteristics of the large-bore marine engines, the spray is usually designed as an inter-spray impingement to promote the fuel-air mixing process, which implies frequent droplet collisions. Properly describing the collision dynamics of liquid droplets has been of interest in the field of spray modeling for marine engine applications. In this context, this work attempts to develop an accurate and efficient methodology for modeling impinging sprays under engine-like conditions. Experimental validations in terms of spray penetration and morphology are initially carried out at different operating conditions considering the parametric variations of ambient temperature and pressure, where the measurements are performed on a large-scale constant volume chamber with two symmetrical injectors.

Fuel film deposited on fuel injector tips used in gasoline direct injection engines, otherwise known as nozzle tip wetting, has been identified as an essential source of particle emissions. Attempts have been made to reduce nozzle tip wetting by the optimization design of nozzle geometry parameters. However, relevant investigations are still limited to emission measurements and corresponding indirect analysis. Due to the lack of related visualization research, the mechanism of nozzle tip wetting formation and its link with nozzle internal flow are still unclear. To clarify the influence of spray hole length on nozzle tip wetting and the underlying mechanisms, the dynamic formation process and the fuel film area evolution of nozzle tip wetting were visualized directly using laser-induced fluorescence technique and photomicrography technique.

To quickly and efficiently match the planetary gearset characteristic parameter of power-spilt hybrid vehicles so that their oil-saving potential can be maximized, this study proposes a parameter matching method that comprehensively considers energy management strategy and driving cycle based on an analysis of vehicle instantaneous efficiency. The method is used to match the planetary characteristic parameter of a power-split hybrid light truck. The relevant conclusions are compared with the influence of various planetary characteristic parameters on fuel consumption obtained through simulation under typical operating conditions. The simulation results show that the influence laws of the various planetary characteristic parameters on vehicle average efficiency are similar to those on fuel consumption. The proposed parameter-matching method based on vehicle efficiency analysis can effectively match the planetary characteristic parameter for power-split hybrid powertrains.

In the face of energy and environmental problems, how to improve the economy of fuel cell vehicles (FCV) effectively and develop intelligent algorithms with higher hydrogen-saving potential are the focus and difficulties of current research. Based on the Toyota Mirai FCV, this paper focuses on the short-term speed prediction algorithm based on the back propagation neural network (BP-NN) and carries out the research on the short-term speed prediction algorithm based on BP-NN. The definition of NN and the basic structure of the neural model are introduced briefly, and the training process of BP-NN is expounded in detail through formula derivation. On this basis, the speed prediction model based on BP-NN is proposed. After that, the parameters of the vehicle speed prediction model, the characteristic parameters of the working condition, and the input and output neurons are selected to determine the topology of the vehicle speed prediction model.

Homogeneous lean combustion is expected to be a key technology to further improve the combustion and reduce emissions of spark-ignition direct-injection engines. The application of lean combustion is facing many challenges such as slow flame propagation and combustion fluctuations. Under severe operating conditions such as low-speed lean-burn conditions, the weak in-cylinder airflow worsens the fuel and air mixing yielding difficulties in stable flame kernel initiation and consequently deteriorating flame propagation. In this study, the effect of flash boiling spray on flame kernel generation, flame propagation, engine performance, and exhaust emissions of the spark ignition direct injection (SIDI) engine under homogenous lean-burn conditions are investigated. A single-cylinder four-stroke optical SIDI engine was used in this study. The in-cylinder flash boiling and subcooled sprays during engine operation were compared using the Mie scattering technique.

To alleviate the shortage of petroleum resources and the air pollution caused by the burning of fossil fuels, the development of renewable fuels has attracted widespread attention. Among the various renewable fuels, ethanol can be produced from biomass and does not require much modification when applied to practical engines, so it has been widely used. However, ethanol fuel has a higher heat of vaporization than gasoline, it is difficult to evaporate and atomize under cold start conditions. Besides, the catalyst has not reached the conversion temperature at this time, resulting in lower conversion efficiency. These factors all lead to higher pollutant emission levels in ethanol-gasoline blends. To solve the above problems, this research used visualization techniques to compare the effects of flash boiling and high-energy ignition technologies on the in-cylinder combustion process and pollutant emission of ethanol-gasoline blends fuel.

In the field of automatic driving, the combination of 3D LIDAR and inertial measurement unit (IMU) is a common sensor configuration scheme in laser point-cloud localization, high-precision map making and point-cloud target detection. So it is critical to calibrate LIDAR and IMU accurately. At present, due to the large volume and high cost of 3D LIDAR with high-line-number(Such as 64 lines or 128 lines), the configuration scheme of using multiple low-line-number 3D LIDARs appears in the automatic driving vehicle sensing system. However, the common calibration methods are not suitable for multi 3D LIDARs and IMU parameters calibration on autonomous vehicle, which have the disadvantages of cumbersome implementation and low accuracy. In this paper, a joint calibration test platform composed of dual LIDARs and IMU is assembled, and a method of precise automatic calibration based on GPS/RTK data is proposed.