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The prediction of agents' future trajectory is a crucial task in supporting advanced driver-assistance systems (ADAS) and plays a vital role in ensuring safe decisions for autonomous driving (AD). Currently, prevailing trajectory prediction methods heavily rely on high-definition maps (HD maps) as a source of prior knowledge. While HD maps enhance the accuracy of trajectory prediction by providing information about the surrounding environment, their widespread use is limited due to their high cost and legal restrictions. Furthermore, due to object occlusion, limited field of view, and other factors, the historical trajectory of the target agent is often incomplete This limitation significantly reduces the accuracy of trajectory prediction. Therefore, this paper proposes ETSA-Pred, a mapless trajectory prediction model that incorporates enhanced temporal modeling and spatial self-attention.

Aiming at the problem of weak communication, strong interference, cross-domain, and large-scale environment, it is difficult to achieve efficient decision-making and planning in the collaborative operation of intelligent groups. Based on the SOM algorithm, this paper proposes a dual-selection allocation and distributed vectorized trajectory planning. Form a collaborative planning algorithm that can be updated with high frequency and a rational decision-making mechanism. Provide technical support for collaborative search and detection of intelligent groups. At the same time, based on the principle of minimum consistency, this paper proposes a clock synchronization model under spatial coordination and conducts simulation experiments to verify it. The result proves the efficiency and practicability of the collaborative intelligent decision-making plan proposed in this paper.

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.

Visual Place Recognition (VPR) excels at providing a good location prior for autonomous vehicles to initialize the map-based visual SLAM system, especially when the environment changes after a long term. Condition change and viewpoint change, which influences features extracted from images, are two of the major challenges in recognizing a visited place. Existing VPR methods focus on developing the robustness of global feature to address them but ignore the benefits that local feature can auxiliarily offer. Therefore, we introduce a novel hierarchical place recognition method with both global and local features deriving from homologous VLAD to improve the VPR performance. Our model is weak supervised by GPS label and we design a fine-tuning strategy with a coupled triplet loss to make the model more suitable for extracting local features.

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.

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.

Computational fluid dynamics (CFD) modeling has many potentials for the design and calibration of modern and future engine concepts, including facilitating the exploration of operation conditions and casting light on the involved physical and chemical phenomena. As more attention is paid to the matching of different fuel types and combustion strategies, the use of detailed chemistry in characterizing auto-ignition, flame stabilization processes and the formation of pollutant emissions is becoming critical, yet computationally intensive. Therefore, there is much interest in using tabulated approaches to account for detailed chemistry with an affordable computational cost. In the present work, the tabulated flamelet progress variable approach (TFPV), based on flamelet assumptions, was investigated and validated by simulating constant-volume Diesel combustion with primary reference fuels - binary mixtures of n-heptane and iso-octane.

With the development of economic globalization, the speed of development of container terminals is also very rapid. Under the pressure brought by the surge in throughput, the unmanned and intelligent terminals will become the future development direction of terminals. As the cornerstone of the unmanned terminal, the positioning technology provides the most basic position information for system scheduling, path planning, real-time correction, and loading and unloading. Therefore, this paper is aimed to design a low-cost, high-precision, and easy-to-maintain unmanned dock positioning system in order to better solve the problem of unmanned dock positioning. The main research content of this paper is to design a positioning algorithm for unmanned terminal Automated Guided Vehicle (AGV) based on single-line lidar, including point cloud data acquisition, background filtering, point cloud clustering, vehicle position extraction, and result optimization.

Precise trajectory prediction of surrounding vehicles is critical for decision-making of autonomous vehicles, and learning-based approaches are well recognized for the robustness. However, state-of-the-art learning-based methods ignore (1) the feasibility of the vehicle’s multimodal state information for prediction and (2) the mutually exclusive relationship between the global traffic scene receptive fields and the local position resolution when modeling vehicles’ interactions, which may influence prediction accuracy. Therefore, we propose a “vehicle descriptor”-based long short-term memory (LSTM) model with the dilated convolutional social pooling (VD+DCS-LSTM) to cope with the above issues.

In recent years, with the increase of traffic accidents and traffic jams, lane change, as one of the most important and commonly automatic driving operations for autonomous vehicles, is receiving attention in academia. It is considered to be one of the important solutions that play an important role in improving road traffic safety and efficiency. However, most existing lane-changing models are rule-based lane-changing models. These models only assume a one-direction impact of surrounding vehicles on the lane-changing vehicle. In fact, lane change is a process of mutual interaction between vehicles due to the complexity and uncertainty of the traffic environment. Moreover, the safety and efficiency of existing lane-changing decision algorithms need to be improved. In this paper, we proposed a multivehicle cooperative control approach with a distributed control structure to control the model.

Applications of methanol and biodiesel in internal combustion engines have raised widespread concerns, but there is still huge scope for improvement in efficiency and emissions. The brand-new combustion mode, named as Intelligent Charge Compression Ignition (ICCI) combustion, was proposed with methanol-biodiesel dual fuel direct injection. In this paper, effects of injection parameters such as two-stage split-injections, injection timings, injection pressure and intake pressure on engine combustion and emissions were investigated at IMEP = 8, 10, and 12 bar. Results show that the indicated thermal efficiency up to 53.5% and the NOx emissions approaching to EURO VI standard can be obtained in ICCI combustion mode.

Developing advanced combustion mode has been the active area for high efficiency and ultra-low emissions of the next-generation internal combustion engines. In this paper, a series of experiments were conducted in a modified single-cylinder compression ignition engine for operating a brand-new combustion mode denoted as intelligent charge compression ignition (ICCI) mode. By using two common-rail systems, commercial gasoline and diesel were alternately directly injected into the cylinder through multi-injection strategies in the injection timing range of 50~320 °CA BTDC. Thus, the in-cylinder stratified condition can be flexibly and accurately adjusted in this unique combustion mode. The key injection parameters, such as gasoline injection timing and diesel split ratio, were investigated to explore their effects on engine combustion, emissions, and fuel consumption.

The energy management strategies (EMS) for hybrid electric vehicles (HEV) have a great impact on the fuel economy (FE). The Pontryagin's minimum principle (PMP) has been proved to be a viable control strategy for HEV. The optimal costate of the PMP control can be determined by the given information of the driving conditions. Since the full knowledge of future driving conditions is not available, this paper proposed a dynamic optimization method for PMP costate without the prediction of the driving cycle. It is known that the lower fuel consumption the method yields, the more efficiently the engine works. The selection of costate is designed to make the engine work in the high efficiency range. Compared with the rule-based control, the proposed method by the principle of Hamiltonian, can make engine working points have more opportunities locating in the middle of high efficiency range, instead of on the boundary of high efficiency range.

The increasingly stringent restrictions on vehicle emissions and fuel consumption are driving the development of gasoline engines towards lean combustion. Increasing ignition energy has been considered an effective way to achieve lean operation conditions. To further improve the lean limit of engine combustion, the influence of the spark plug orientation on the combustion stability under lean operation should be explored. In this investigation, the original machine spark plug orientation, 90 degrees clockwise rotation, and 180 degrees clockwise rotation are studied to analyze the impact of spark plug orientation. The combustion experiment was carried out under the condition of low excess air ratio of the original machine and high excess air ratio with a 450 mA high energy ignition.

Accuracy of heat flux models is critical to clutch design in case of excessive temperatures due to large amounts of friction heat generated in the narrow space. Pressure distribution on the clutch friction interface is an important factor affecting heat flux distribution, thus affecting temperature distribution. In this paper, an experiment is conducted to obtain the pressure distribution for one typical dry clutch equipped with a set of diaphragm spring. Considering that the frictional interface is in contact, this study makes use of pressure sensitive film and acquires data based on image processing techniques. Then a polynomial mathematical model with dimensionless parameters is developed to fit the pressure distribution on the friction disc. After that, the proposed pressure model is applied to a thermal model based on finite element method. In addition, two conventional thermal models (i.e., uniform heat flux model and uniform pressure model), are implemented for comparison.

It is well known that engine downsizing is still the main energy-saving technology for spark-ignition direct-injection (SIDI) engine. However, with the continuous increase of the boosting ratio, the gasoline engine is often accompanied by the occurrence of knocking, which has the drawback to run the engine at retarded combustion phasing. Besides, in order to protect the turbine blades from being sintered by high exhaust temperature, the strategies of fuel enrichment are often taken to reduce the combustion temperature, which ultimately leads to a high level of particulate number emission. Therefore, to address the issues discussed above, the port water injection (PWI) techniques on a 1.2-L turbocharged, three-cylinder, SIDI engine were investigated. Measurements indicate that the optimization of spark timing has a significant impact on its performance.

Increasingly stringent emission standards have promoted the interest in alternate fuel sources. Because of the comparable energy density to the existing fossil fuels and renewable production, alcohol fuels may be a suitable replacement, or an additive to the gasoline/diesel fuels to meet the future emission standards with minimal modification to current engine geometry. In this research, the combustion characteristics of neat n-butanol are analyzed under spark ignition operation using a single cylinder SI engine. The fuel is injected into the intake manifold using a port-fuel injector. Two modes of charge dilution were used in this investigation to test the limits of stable engine operation, namely lean burn using excess fresh air and exhaust gas recirculation (EGR). The in-cylinder pressure measurement and subsequently, heat release analysis are used to investigate the combustion characteristics of the fuel under low load SI engine operation.

Using highly efficient powertrain is one of the most important and effective approaches to increase the driving distance of electric vehicles (EVs). In this paper, a novel two-speed dual-clutch transmission (DCT) is proposed. The transmission is comprised of two traditional friction clutches and two-stage planetary gear sets. One clutch connects the input sun gear and the other connects the input carrier. The Simulink models including an electric motor and two-speed DCT are established. Gearshift schedule based on fuzzy logic which reflects the driver’s intensions is adopted to improve the dynamic and economic performance of the novel transmission. The simulation model is built using MATLAB/Simulink® to validate the effectiveness of the proposed gearshift schedule compared with the conventional two-parameter gearshift schedule. Simulation results show that both the dynamic and economic performance of the novel DCT for EVs are improved with the proposed fuzzy logic gearshift schedule.