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Driving style and skill classification are of great significance in human-oriented advanced driver-assistance system (ADAS) development. In this paper, we propose Fully-Connected Deep Neural Networks (FC-DNN) to classify drivers’ styles and skills with naturalistic driving data. Followed by the data collection and pre-processing, FC-DNN with a series of deep learning optimization algorithms are applied. In the experimental part, the proposed model is validated and compared with other commonly used supervised learning methods including the k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), and multilayer perceptron (MLP). The results show that the proposed model has a higher Macro F1 score than other methods. In addition, we discussed the effect of different time window sizes on experimental results. The results show that the driving information of 1s can improve the final evaluation score of the model.

Advanced driver assistance systems (ADAS) are being developed for more and more complicated application scenarios, which often require more predictive strategies with better understanding of driving environment. Taking traffic vehicles’ maneuvers into account can greatly expand the beforehand time span for danger awareness. This paper presents a maneuver-based strategy to vehicle collision threat assessment. First, a maneuver-based trajectory prediction model (MTPM) is built, in which near-future trajectories of ego vehicle and traffic vehicles are estimated with the combination of vehicle’s maneuvers and kinematic models that correspond to every maneuver. The most probable maneuvers of ego vehicle and each traffic vehicles are modeled and inferred via Hidden Markov Models with mixture of Gaussians outputs (GMHMM). Based on the inferred maneuvers, trajectory sets consisting of vehicles’ position and motion states are predicted by kinematic models.

The conventional radar modeling methods for automotive applications were either function-based or physics-based. The former approach was mainly abstracted as a solution of the intersection between geometric representations of radar beam and targets, while the latter one took radar detection mechanism into consideration by means of “ray tracing”. Although they each has its unique advantages, they were often unrealistic or time-consuming to meet actual simulation requirements. This paper presents a combined geometric and physical modeling method on millimeter-wave radar systems for Frequency Modulated Continuous Wave (FMCW) modulation format under a 3D simulation environment. With the geometric approach, a link between the virtual radar and 3D environment is established. With the physical approach, on the other hand, the ideal target detection and measurement are contaminated with noise and clutters aimed to produce the signals as close to the real ones as possible.

Lane detection is one of the most important part in ADAS because various modules (i.e., LKAS, LDWS, etc.) need robust and precise lane position for ego vehicle and traffic participants localization to plan an optimal routine or make proper driving decisions. While most of the lane detection approaches heavily depend on tedious pre-processing and great amount of assumptions to get reasonable result, the robustness and efficiency are deteriorated. To address this problem, a novel framework is proposed in this paper to realize robust and real-time lane detection. This framework consists of two branches, where canny edge detection and Progressive Probabilistic Hough Transform (PPHT) are introduced in the first branch for efficient detection.

With the increasing number of urban cars, parking has become the primary problem that people face in daily life. Therefore, many scholars have studied the automatic parking system. In the existing research, most of the path planning methods use the combined path of arc and straight line. In this method, the path curvature is not continuous, which indirectly leads to the low accuracy of path tracking. The parking path designed using the fifth-order polynomial is continuous, but its curvature is too large to meet the steering constraints in some cases. In this paper, a continuous-curvature parking path is proposed. The parking path tracker based on Model Predictive Control (MPC) algorithm is designed under the constraints of the control accuracy and vehicle steering. Firstly, in order to make the curvature of the parking path continuous, this paper superimposes the fifth-order polynomial with the sigmoid function, and the curve obtained has the continuous and relatively small curvature.

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.

This paper presents a study of LPG lean burn in a motorcycle SI engine. The lean burn limits are compared by several ways. The relations of lean burn limit with the parameters, such as engine speed, compression ratio and advanced spark ignition etc. are tested. The experimental results show that larger throttle opening, lower engine speed, earlier spark ignition timing, larger electrode gap and higher compression ratio will extend the lean burn limit of LPG. The emission of a LPG engine, especially on NOx emission, can be significantly reduced by means of the lean burn technology.

Acetone-Butanol-Ethanol (ABE), an intermediate product in the ABE fermentation process for producing bio-butanol, is considered a promising alternative fuel because it not only preserves the advantages of oxygenated fuel which typically emit less pollutants compared to conventional diesel, but also lowers the cost of fuel recovery for each individual component during the fermentation. With the development of advanced ABE fermentation technology, the volumetric percentage of acetone, butanol and ethanol in the bio-solvents can be precisely controlled. In this respect, it is desirable to estimate the performance of different ABE blends to determine the best blend and optimize the production process accordingly. ABE fuels with different component ratio, (A: B: E: 6:3:1; 3:6:1; 0:10:0, vol. %), were blended with diesel and tested in a constant volume chamber.

Proper tire pressure is very important for multiple driving performance of a car, and it is necessary to monitor and warn the abnormal tire pressure online. Indirect Tire Pressure Monitoring System (TPMS) monitors the tire pressure based on the wheel speed signals of Anti-lock Braking System (ABS). In this paper, an indirect TPMS method is proposed to estimate the tire pressure according to its resonance frequency of circumferential vibration. Firstly, the errors of ABS wheel speed sensor system caused by the machining tolerance of the tooth ring are estimated based on the measured wheel speed using Recursive Least Squares (RLS) algorithm and the measuring errors are eliminated from the wheel speed signal. Then, the data segments with drive train torsional vibration are found out and eliminated by the methods of correlation analysis.

The global adaptive cruise control (ACC) market is expected to witness a compound annual growth rate of 18.3% during the forecast period to reach $15,290 million by 2023 [1]. The driver uses an ACC system to reduce the driving burden and improve safety. The ACC mode in a car is fixed, but different drivers have different driving habits. This paper will verify this through experiments and divide drivers into three categories according to the drivers’ driving habits. Therefore, we will design a personalized ACC, wherein an ACC system, under the same working conditions, can have different acceleration and deceleration to meet the needs of different types of drivers. Therefore, this paper collects driver data, analyzes model data and identifies its parameters, and finally verifies the different effects of personalized ACC through simulation.

Intelligent driving, aimed for collision avoidance and self-navigation, is mainly based on environmental sensing via radar, lidar and/or camera. While each of the sensors has its own unique pros and cons, camera is especially good at object detection, recognition and tracking. However, unpredictable environmental illumination can potentially cause misdetection or false detection. To investigate the influence of illumination conditions on detection algorithms, we reproduced various illumination intensities in a photo-realistic virtual world, which leverages recent progress in computer graphics, and verified vehicle detection effect there. In the virtual world, the environmental illumination is controlled precisely from low to high to simulate different illumination conditions in the driving scenarios (with relative luminous intensity from 0.01 to 400). Sedan cars with different colors are modelled in the virtual world and used for detection task.

This paper presents experimental studies of particulate emissions in a small SI engine fueled with LPG and gasoline fuels. A single cylinder, four-stroke, water-cooled, 125cc EFI engine with gasoline fuel is used as the baseline engine. Characteristics of the particulate emissions of the two fuels are compared. Test results show that: there are great quantities of particulate emissions for both fuels, but the total numbers of particulate emissions for the two fuels are generally in the same level. The distribution of the particulate sizes is in bimodal type for the gasoline, but for the LPG its first peak is not markedly in some conditions. The particulate sizes of the second peak for the two fuels appear at about the same size. At middle loads and 3000r/min, the particulate emissions for both of the two fuels are the greatest.

For the purpose of lessening fuel consumption, engine noise, shift jerk and clutch friction work in the shift process of Automatic Mechanical Transmission (AMT), a fuzzy-bang bang dual mode control strategy for engine rotate speed is put forward in this paper, which takes the advantages of time optimal control and fuzzy control. The combined control strategy is applied to the shift process control of AMT test minibus named SC6350 and proved to be successful by the experimental results.

This paper presents an experimental study of the emission characteristics of a small Spark-Ignited, LPG engine. A single cylinder, four-stroke, water-cooled, 125cc SI engine for motorcycle is modified for using LPG fuel. The power output of LPG is above 95% power output of gasoline. The emission characteristics of LPG are compared with the gasoline. The test result shows that LPG for small SI engine will help to reduce the emission level of motorcycles. The HC and CO emission level can be reduced greatly, but NOx emissions are increased. The emission of motorcycle using LPG shows the potential to meet the more strict regulation.

An air-cooled, four-stroke, 125 cc electronic gasoline fuel injection SI engine for motorcycles is altered to burn ethanol fuel. The effects of nozzle orifice size, fuel injection duration, spark timing and the excess air/ fuel ratio on engine power output, fuel and energy consumptions and engine exhaust emission levels are studied on an engine test bed. The results show that the maximum engine power output is increased by 5.4% and the maximum torque output is increased by 1.9% with the ethanol fuel in comparison with the baseline. At full load and 7000 r/min, HC emission is decreased by 38% and CO emission is decreased 46% on average over the whole engine speed range. However, NOx levels are increased to meet the maximum power output. The experiments of the spark timing show that the levels of HC and NOx emission are decreased markedly by the delay of spark timing.

Driver steering feature clustering aims to understand driver behavior and the decision-making process through the analysis of driver steering data. It seeks to comprehend various steering characteristics exhibited by drivers, providing valuable insights into road safety, driver assistance systems, and traffic management. The primary objective of this study is to thoroughly explore the practical applications of various clustering algorithms in processing driver steering data and to compare their performance and applicability. In this paper, principal component analysis was employed to reduce the dimension of the selected steering feature parameters. Subsequently, K-means, fuzzy C-means, the density-based spatial clustering algorithm, and other algorithms were used for clustering analysis, and finally, the Calinski-Harabasz index was employed to evaluate the clustering results. Furthermore, the driver steering features were categorized into lateral and longitudinal categories.

The automotive industry is aggressively pursuing fuel efficiency improvements through hybridization of production vehicles, and there are an increasing number of racing series adopting similar architectures to maintain relevance with current passenger car trends. Hybrid powertrains offer both performance and fuel economy benefits in a motorsport setting, but they greatly increase control complexity and add additional degrees of freedom to the design optimization process. The increased complexity creates opportunity for performance gains, but simulation based tools are necessary since hybrid powertrain design and control strategies are closely coupled and their optimal interactions are not straightforward to predict. One optimization-related advantage that motorsports applications have over production vehicles is that the power demand of circuit racing has strong repeatability due to the nature of the track and the professional skill-level of the driver.

Hybrid Electric Vehicles with a power split system provide a variety of possibilities to promote the fuel economy of vehicles and better adapt to various driving conditions. In this paper, a new power split system of a hybrid electric bus which consists of double planetary gear sets and a clutch is introduced. The system is able to decouple both the torque and speed of the engine from the road load, which makes it possible for the engine to operate on its optimal operation line (OOL). Considering the features of the system configuration and bus driving cycle, the driving mode of the bus is divided into Electric Vehicle (EV) mode, Electric Variable Transmission (EVT) mode and Parallel mode. By controlling the engagement of the clutch at high vehicle speed (after the mechanical point), the system operates in the parallel mode rather than EVT mode. This avoids the problem that the system efficiency sharply declines in high speed region which EVT configurations are generally faced with.

The platooning of connected automated vehicles (CAV) possesses the significant potential of reducing energy consumption in the Intelligent Transportation System (ITS). Moreover, with the rapid development of eco-driving technology, vehicle platooning can further enhance the fuel efficiency by optimizing the efficiency of the powertrain. Since road grade is a main factor that affects the energy consumption of a vehicle, the estimation of the road grade with high accuracy is the key factor for a connected vehicle platoon to optimize energy consumption using vehicle-to-vehicle (V2V) communication. Commonly, the road grade is quantified by single consumer grade global positioning system (GPS) with the geodetic height data which is rough and in the meter-level, increasing the difficulty of precisely estimating the road grade.

As one of advanced driver assistance systems (ADAS), automatic parking system has great market prospect and application value. In this paper, based on an intelligent vehicle platform, an automatic parking system is designed by using multi-point preview theory. The vehicle kinematics model was established, based on Ackermann steering principle. By analyzing working conditions of parallel parking, complex constraint condition of parking trajectory is established and reference trajectory based on sine wave is proposed. In addition, combined with multi-point preview theory, the design of trajectory following controller for automatic parking is completed. The cost function is designed, which consider the trajectory following effect and the degree of easy handling. The optimization of trajectory following control is completed by using the cost function.