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The fusion of multi-modal perception in autonomous driving plays a pivotal role in vehicle behavior decision-making. However, much of the previous research has predominantly focused on the fusion of Lidar and cameras. Although Lidar offers an ample supply of point cloud data, its high cost and the substantial volume of point cloud data can lead to computational delays. Consequently, investigating perception fusion under the context of 4D millimeter-wave radar is of paramount importance for cost reduction and enhanced safety. Nevertheless, 4D millimeter-wave radar faces challenges including sparse point clouds, limited information content, and a lack of fusion strategies. In this paper, we introduce, for the first time, an approach that leverages Graph Neural Networks to assist in expressing features from 4D millimeter-wave radar point clouds. This approach effectively extracts unstructured point cloud features, addressing the loss of object detection due to sparsity.

To reduce the energy consumption level of electric vehicles, the working range of the regenerative braking system will gradually expand to the high state of charge of the battery. The time delay in the control signal transmission path of the high state of charge regenerative braking control process will affect the regenerative braking. At the same time, regenerative braking under a high state of charge puts forward higher requirements for the control accuracy of regenerative current. In the research of this paper, the motor model, battery model, and vehicle dynamics model are firstly established by using MATLAB/Simulink, and the dynamic relationship between regenerative current and regenerative braking torque is analyzed at the same time. Considering the system time delay, this paper proposes a high-charge regenerative braking control strategy (SPPC) that combines Smith prediction and prescribed performance control.

Electrification is the well-accepted solution to address carbon emissions and modernize vehicle controls. Batteries play a critical in the journey of electrification and modernization with battery voltage prediction as the foundation for safe and efficient operation. Due to its strong dependency on prior information, battery voltage was estimated with recurrent neural network methods in the recent literatures exploring a variety of deep learning techniques to estimate battery behaviors. In these studies, standard recurrent neural networks, gated recurrent units, and long-short term memory are popular neural network architectures under review. However, in most cases, each neural network architecture is individually assessed and therefore the knowledge about comparative study among three neural network architecture is limited. In addition, many literatures only studied either the dynamic voltage response or the voltage relaxation.

Due to the poor ability of millimeter wave radar in recognizing distant static objects, target loss and incomplete information will occur when it recognizes the static target in front, thus increasing the false alarm rate and missing alarm rate of the radar-dependent driving assistant system, which will reduce the driving safety and the acceptability of the assistant system. Aiming at the radar's poor ability to recognize static targets, this paper uses a model based on machine learning algorithm to recognize and track targets. The radar signals are collected and processed in different conditions, and the results show that the radar has a poor recognition effect when the distance is more than 100 meters and the speed is more than 19m/s.

HTL is a kind of biodiesel converted from wet biowaste via hydrothermal liquefaction (HTL), which has drawn increasing attention in recent years due to its wide range of raw materials (algae, swine manure, and food processing waste). However, from the previous experiments done in a constant volume chamber, it was observed that the presence of 20% of HTL in the blend produced as much soot as pure diesel at in chamber environment oxygen ratio of 21%, and even more soot at low oxygen ratios. It was also observed that n-butanol addition could reduce the soot emission of diesel significantly under all tested conditions. In this work, the spray and combustion characteristics of HTL and diesel blends with n-butanol added were investigated in a constant volume chamber. The in-chamber temperature and oxygen ranged from 800 to 1200 K and 21% to 13%, respectively, covering both conventional and low-temperature combustion (LTC) regimes.

Controllability is defined in ISO 26262 as a driver’s ability to avoid a specified harm caused by a malfunction of electrical and electronic systems installed in road vehicles. According to Annex C of Part 12 of ISO 26262, simulation is one of the techniques that the Controllability Classification Panel (CCP) can use to evaluate comprehensively the controllability class (C class) of motorcycles. With outputs of (i) an index for the success of harm avoidance and (ii) the magnitude of the rider’s compensatory control action required to avoid harm, the simulation is useful for evaluating the C class of the degrees of malfunction that cannot be implemented in practice for the sake of the test rider’s safety. To aim at supplying data that the CCP can use to judge the C class, we try to estimate the vehicle behavior and a rider’s compensatory control actions following a malfunction using vehicle dynamics simulations.

Flash boiling has drawn much attention recently for its ability to enhance spray atomization and vaporization, while providing better fuel/air mixing for gasoline direct injection engines. However, the behaviors of flash boiling spray with multi-component fuels have not been fully discovered. In this study, isooctane, ethanol and the mixtures of the two with three blend ratios were chosen as the fuels. Measurements were performed with constant fuel temperature while ambient pressures were varied to adjust the superheated degree. Macroscopic and microscopic characteristics of flash boiling spray were investigated using Diffused Back-Illumination (DBI) imaging and Phase Doppler Anemometry (PDA). Comparisons between flash boiling sprays with single component and binary fuel mixtures were performed to study the effect of fuel properties on spray structure as well as atomization and vaporization processes.

The spray structure and vaporization processes of flash-boiling sprays in a constant volume chamber under a wide range of superheated conditions were experimentally investigated by a high speed imaging technique. The Engine Combustion Network’s Spray G injector was used. Four fuels including gasoline, ethanol, and gasoline-ethanol blends E30 and E50 were investigated. Spray penetration length and spray width were correlated to the degree of the superheated degree, which is the ratio of the ambient pressure to saturated vapor pressure (pa/ps). It is found that parameter pa/ps is critical in describing the spray transformation under flash-boiling conditions. Three distinct stages namely the slight flash-boiling, the transition flash-boiling, and the flare flash-boiling are identified to describe the transformation of spray structures.

The study of vehicle coupling state estimation accuracy especially in observer-based vehicle chassis control for improving road handling and ride comfort is a challenging task for vehicle industry under various driving conditions. Due to a large amount of life safety arising from vehicle roll behavior, how to precisely acquire vehicle roll state and rapidly provide for the vehicle control system are of great concern. Simultaneously, uncertainty is unavoidable for various aspects of a vehicle system, e.g., varying sprung mass, moment of inertia and position of the center of gravity. To deal with the above issues, a novel dual observer approach, which combines adaptive Unscented Kalman Filter (AUKF) and Takagi-Sugeno (T-S), is proposed in this paper. A full-car nonlinear model is first established to describe vehicle lateral and vertical coupling roll behavior under various road excitation.

The advanced Pedestrian Legform Impactor (aPLI) incorporates a number of enhancements for improved lower limb injury prediction capability with respect to its predecessor, the FlexPLI. The aPLI also incorporates a simplified upper body part (SUBP), connected to the lower limb via a mechanical hip joint, that expands the impactor’s applicability to evaluate pedestrian’s lower limb injury risk also in high-bumper cars.As the aPLI has been developed to be used in standardized testing, further considerations on the impactor’s manufacturability, robustness, durability, usability, and repeatability need to be accounted for.. The aim of this study is to define and verify, by means of numerical analysis, a battery of design modifications that may simplify the manufacturing and use of physical aPLIs, without reducing the impactors’ biofidelity. Eight candidate parameters were investigated in a two-step numerical analysis.

ISO 26262, an international functional safety standard of electrical and/or electronic systems (E/E systems) for motor vehicles, was published in November 2011 and it is expected that the scope will be extended to motorcycles in a second edition of ISO 26262 going to be published in 2018. ISO/DIS 26262 second edition published in 2016 has Part 12 as a new part in order to apply ISO 26262 to motorcycle. Proper estimation of Exposure, Controllability, and Severity in accordance with ISO/DIS 26262 Part 12, are key factors to determine Motorcycle Safety Integrity Level. To estimate precise these factors, there would be a case that it might not be appropriate to apply studies done for passenger car to motorcycle, and it would be necessary to apply motorcycle specific knowledge and estimation methods. In our previous studies we clarified these motorcycle specific issues and studied the method for the adaptation.

Considering the randomness and instability of the oil pressure in the lubrication system, a new approach for fault detection and diagnosis of diesel engine lubrication system based on support vector machine optimized by particle swarm optimization (PSO-SVM) model and centroid location algorithm has been proposed. Firstly, PSO algorithm is chosen to determine the optimum parameters of SVM, to avoid the blindness of choosing parameters. It can improve the prediction accuracy of the model. The results show that the classify accuracy of PSO-SVM is improved compared with SVM in which parameters are set according to experience. Then, the support vector machine classification interface is fitted to a curve, and the boundary conditions of fault diagnosis are obtained. Finally, diagnose algorithm is achieved through analyzing the centroid movement of features. According to Performance degradation data, degenerate trajectory model is established based on centroid location.

ISO 26262 was established in 2011 as a functional safety standard for road vehicles. This standard provides safety requirements according to ASIL (Automotive Safety Integrity Level) in order to avoid unreasonable residual risk caused by malfunctioning behavior of electrical and/or electronic systems. The ASIL is determined by considering the estimate of three factors including injury severity. While applicable only to passenger cars at present, motorcycles will be included in the scope of application of ISO 26262 in the next revision. Therefore, our previous study focused on severity class evaluation for motorcycles. A method of classifying injury severity according to vehicle speed was developed on the basis of accident data. In addition, a severity table for motorcycles was created using accident data in representative collision configurations involved with motorcycles in Japan.

ISO 26262, an international functional safety standard of electrical and/or electronic systems (E/E systems) for motor vehicles, was published in November 2011 and it is expected that the scope will be extended to motorcycles in a second edition of ISO 26262 going to be published in 2018. In order to apply ISO 26262 to motorcycle, proper estimation of Exposure, Controllability, and Severity are key factors to determine Motorcycle Safety Integrity Level (MSIL). Exposure is a factor to indicate the probability of the state of an operational situation that can be hazardous with the E/E system malfunction. And it is not easy to estimate the motorcycle Exposure due to less availability of back ground data in actual operational situation compared to motor vehicle. Therefore real traffic situation should be investigated in order to provide rationales for MSIL determination.

The ever-growing number of interacting electronic vehicle control systems requires new control algorithms to manage the increasing system complexity. As a result, torque-based control architecture has been popular for its easy extension as the torque demand variable is the only interface between the engine control algorithms and other vehicle control systems. Under the torque-based control architecture, the engine and AT coordinated control for upshift process is investigated. Based on the dynamics analysis, quantitative relationship between the turbine torque of HTC and output shaft torque of AT has been obtained. Then the coordinated control strategy has been developed to smooth the torque trajectory of AT output shaft. The designed control strategy is tested on a powertrain simulation model in MATLAB/Simulink and a test bench. Through simulation, the shift time range in which the engine coordinated control strategy is effective is acquired.

Controllability (C class) represents the level of the ability to avoid harm and is one of the parameters that determine the Automotive Safety Integrity Level in the ISO 26262 functional safety standard, which applies to the electrical and/or electronic systems. This study aimed to consider an appropriate C class evaluation technique for expert riders in applying ISO 26262 to motorcycles. This study attempted to show a C class evaluation method without deviation by the riders and presented examples of the evaluation of three hazardous events in actual vehicle tests. In addition, riders' comments regarding their understanding of the circumstances that resulted in the evaluation were collected, and the correspondence of these comments was examined. We selected “unintended acceleration” or “unintended deceleration” due to the malfunction of the electronic throttle control system as hazard examples and conducted tests to reproduce hazardous events.

To verify the appropriateness of the vibration test conditions of ISO 12405, we performed tailoring to derive power spectrum densities and test durations as vibration test conditions. Vehicles used for tailoring included two electric vehicles and one plug-in hybrid electric vehicle. Those vehicles were equipped with accelerometers and were run on seven different road types at different speeds while data on the acceleration of the battery packs were recorded. The power spectrum densities for three axes that were derived from the obtained acceleration data were similar in form to the power spectrum densities of ISO 12405, and almost the same root mean square accelerations were obtained, confirming that they are appropriate. However, both experiments and theory suggest that the test duration for the Z-axis exceeds those of the X- and Y-axes.

The cylinder-by-cylinder variations have many bad impacts on the engine performance, such as increasing the engine speed fluctuation, enlarging the torsional vibration and noise. To deal with this problem, the impact mechanism of cylinder-by-cylinder variations on low order torsional vibration has been studied in this paper, and subsequently a new individual cylinder control strategy was designed by processing the instantaneous crankshaft rotation speed signal, detecting the cylinder-by-cylinder variation and using feed-back control. The acceleration characteristics of each cylinder in each engine cycle were compared with each other to extract the variation index. The feed-back control algorithm was based on the regulation of the fuel injection according to the detected variation level.

Since their inception, the design of airbag sensing systems has continued to evolve. The evolution of air bag sensing system design has been rapid. Electromechanical sensors used in earlier front air bag applications have been replaced by multi-point electronic sensors used to discriminate collision mechanics for potential air bag deployment in front, side and rollover accidents. In addition to multipoint electronic sensors, advanced air bag systems incorporate a variety of state sensors such as seat belt use status, seat track location, and occupant size classification that are taken into consideration by air bag system algorithms and occupant protection deployment strategies. Electronic sensing systems have allowed for the advent of event data recorders (EDRs), which over the past decade, have provided increasingly more information related to air bag deployment events in the field.

The low cycle fatigue experiment is extensively used to test the reliability and durability of turbocharger. Low cycle fatigue test is mainly the switching between high and low speed. As the result of the experiment, the fatigue life is shorter as the difference between high and low speed becomes greater. In the traditional low cycle fatigue test, a large air compressor is needed to drive the turbocharger under different operating conditions, which consume large amounts of electric power. This paper presents a new experiment device which has double chambers and double turbochargers. This device can be self-circulating, without the large air compressor, to realize high and low speed switching on the premise of not exceeding the limitation of turbine entry temperature. First, a detailed model is established in GT-Power and self-circulation test data has been used to validate the model.