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Performance testing and evaluation always plays an important role in the developmental process of a vehicle, which also applies to autonomous vehicles. The complex nature of an autonomous vehicle from architecture to functionality demands even more quality-and-quantity controlled testing and evaluation than ever before. Most of the existing testing methodologies are task-or-scenario based and can only support single or partial functional testing. These approaches may be helpful at the initial stage of autonomous vehicle development. However, as the integrated autonomous system gets mature, these approaches fall short of supporting comprehensive performance evaluation. This paper proposes a novel hierarchical and systematic testing and evaluation approach to bridge the above-mentioned gap.

An autonomous vehicle is a comprehensive intelligent system that includes environment sensing, vehicle localization, path planning and decision-making control, of which environment sensing technology is a prerequisite for realizing autonomous driving. In the early days, vehicles sensed the surrounding environment through sensors such as cameras, radar, and lidar. With the development of 5G technology and the Vehicle-to-everything (V2X), other information from the roadside can also be received by vehicles. Such as traffic jam ahead, construction road occupation, school area, current traffic density, crowd density, etc. Such information can help the autonomous driving system understand the current driving environment more clearly. Vehicles are no longer limited to areas that can be sensed by sensors. Vehicles with different autonomous driving levels have different adaptability to the environment.

Effectively detecting road boundaries in real time is critical to the applications of autonomous vehicles, such as vehicle localization, path planning and environmental understanding. To precisely extract the road boundaries from the 3D-LiDAR data, a dedicated algorithm consisting of four steps is proposed in this paper. The steps are as follows: Firstly, the 3D-LiDAR data is pre-processed by employing the RANSAC method, the ground points are quickly separated from the original 3D-LiDAR point cloud to reduce the disturbance from the obstacles on the road, this greatly decreases the size of the point cloud to be processed. Secondly, based on the principle of 3D-LiDAR scanning, the ground points are divided into scan layers. And the road boundary points of each scan layer are detected by using three spatial features based on sliding window.

Velocity prediction on hilly road can be applied to the energy-saving predictive control of intelligent vehicles. However, the existing methods do not deeply analyze the difference and diversity of road slope driving characteristics, which affects prediction performance of some prediction method. To further improve the prediction performance on road slope, and different road slope driving features are fully exploited and integrated with the common prediction method. A rolling prediction-based multi-scale fusion prediction considering road slope transition driving characteristics is proposed in this study. Amounts of driving data in hilly sections were collected by the advanced technology and equipment. The Markov chain model was used to construct the velocity and acceleration joint state transition characteristics under each road slope transition pair, which expresses the obvious driving difference characteristics when the road slope changes.

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.

Autonomous driving technology is more and more important nowadays, it has been changing the living style of our society. As for autonomous driving planning and control, vehicle dynamics has strong nonlinearity and uncertainty, so vehicle dynamics and control is one of the most challenging parts. At present, many kinds of specific vehicle dynamics models have been proposed, this review attempts to give an overview of the state of the art of vehicle dynamics models for autonomous driving. Firstly, this review starts from the simple geometric model, vehicle kinematics model, dynamic bicycle model, double-track vehicle model and multi degree of freedom (DOF) dynamics model, and discusses the specific use of these classical models for autonomous driving state estimation, trajectory prediction, motion planning, motion control and so on.

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.

For the purpose of predicting the interior noise of a passenger automobile at middle and high frequency, an energy finite element analysis (EFEA) model of the automobile was created using EFEA method. The excitations including engine mount excitation and road excitation were measured by road experiment at a speed of 120 km/h. The sound excitation was measured in a semi-anechoic chamber. And the wind excitation was calculated utilizing numeric computation method of computational fluid dynamics (CFD). The sound pressure level (SPL) and energy density contours of the interior acoustic cavity of the automobile were presented at 2000 Hz. Meanwhile, the flexural energy density and flexural velocity of body plates were calculated. The SPL of interior noise was predicted and compared with the corresponding value of experiment.

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.

Three thermo-wires with amplifying circuits have been developed to measure the time-resolved concentration of the exhaust gas recirculated into the intake manifold by a rotary valve-based exhaust gas recirculation (EGR) system of a diesel engine. Good agreement was found between the EGR rates measured by the temperature based system and a conventional CO2 tracing system. The developed EGR measuring system was used to investigate the EGR transient response in a turbo-charged and after-cooled diesel engine with a real-time measure and control system. The EGR response under EGR valve step change and engine transient operating conditions are discussed. At first, the engine was running under a certain steady condition with zero recirculated exhaust gas, then the rotary valve opened to maximum within 0.1s to demonstrate the EGR step change behavior. EGR rate and air intake stabilized in 0.5s.

In this study, an experimental investigation was conducted using a direct injection single-cylinder diesel engine equipped with a test common rail fuel injection system to clarify how dimethyl ether (DME) injection characteristics affect the heat release and exhaust emissions. For that purpose the common rail fuel injection system (injection pressure: 15 MPa) and injection nozzle (0.55 × 5-holes, 0.70 × 3-holes, same total holes area) have been used for the test. First, to characterize the effect of DME physical properties on the macroscopic spray behavior: injection quantity, injection rate, penetration, cone angle, volume were measured using high-pressure injection chamber (pressure: 4MPa). In order to clarify effects of the injection process on HC, CO, and NOx emissions, as well as the rate of heat release were investigated by single-cylinder engine test. The effects of the injection rate and swirl ratio on exhaust emissions and heat release were also investigated.

Tire-road friction condition is crucial to the safety of vehicle driving. The emergence of autonomous driving makes it more important to estimate the friction limit accurately and at the lowest possible excitation. In this paper, an early detection method of tire-road friction coefficient based on pneumatic trail under cornering conditions is proposed using an intelligent tire system. The previously developed intelligent tire system is based on a triaxial accelerometer mounted on the inner liner of the tire tread. The friction estimation scheme utilizes the highly sensitive nature of the pneumatic trail to the friction coefficient even in the linear region and its approximately linear relationship with the excitation level. An indicator referred as slip degree indicating the utilization of the road friction is proposed using the information of pneumatic trail, and it is used to decide whether the excitation is sufficient to adopt the friction coefficient estimate.

To find out the main excitation sources of a bus floor's vibration, modal analysis and spectral analysis were respectively performed in the paper. First we tested the vibration modal of the bus's floor under the full-load condition, and the first ten natural frequencies and vibration modes were obtained for the source identification of the bus floor's vibration. Second the vibration characteristic of the bus floor was measured in an on-road experiment. The acceleration sensors were arranged on the bus's floor and the possible excitation sources of the bus, which includes engine mounting system, driveline system, exhaust system, and wheels. Then the on-road experiment was carefully conducted on a highway under the four kinds of test condition: in-situ acceleration, uniform velocity (90km/h, 100km/h, 110km/h, 120km/h), uniform acceleration with top gear, and stall sliding condition with neutral gear.

This study investigates the fundamental stiffness and damping properties of a self-damped pneumatic suspension system, based on both the experimental and analytical analyses. The pneumatic suspension system consists of a pneumatic cylinder and an accumulator that are connected by an orifice, where damping is realized by the gas flow resistance through the orifice. The nonlinear suspension system model is derived and also linearized for facilitating the properties characterization. An experimental setup is also developed for validating both the formulated nonlinear and linearized models. The comparisons between the measured data and simulation results demonstrate the validity of the models under the operating conditions considered. Two suspension property measures, namely equivalent stiffness coefficient and loss factor, are further formulated.

Unmanned Ground Vehicle (UGV) has a wide range of applications in the military, agriculture, firefighting and other fields. Path planning, as a key aspect of autonomous driving technology, plays an essential role for UGV to accomplish the established driving tasks. At present, there are many global path planning algorithms in grid maps on unstructured roads, while general grid maps do not consider the specific elevation or ground type difference of each grid, and unstructured roads are generally considered as flat and open roads. On the contrary, the unmanned off-road is always a bumpy road with undulating terrain, and meanwhile, the landform is complex and the types of features are diverse. In order to ensure the safety and improve the efficiency of autonomous driving of UGV in off-road environment, this paper proposes a global off-road path planning method for UGV based on the raw image of remote sensing map. Firstly, the raw image is gridded.

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.

A novel non-destroy repeatable-use impact theory based total cylinder sampling system has been established. This system is mainly composed of a knocking body and a sampling valve. The knocking body impacts the sampling valve with certain velocity resulting in huge force to open the sampling valve and most of the in-cylinder gas has been dumped to one sampling bag for after-treatment. The feasibility and sampling response characteristics of this impact theory based total cylinder sampling system were investigated by engine bench testing. Within 0 to 35°CA ATDC (Crank Angle After Top Dead Center) sample timing 50 percent to 80 percent of in-cylinder mass would be sampled, which was a little less compared with the traditional system. The half decay period of pressure drop was 10 to 20 degrees crank angle within 0 to 60°CA ATDC sample timing, which was about 2-3 times of the traditional system.

The incomplete combustion and misfire of diesel engine during starting result in unwanted white smoke. The histories of combustion and emission in different phases under different start conditions were studied in this paper. The optimization of the fuel injection control strategy under start conditions was performed. When the diesel engine is started under low temperature, the control strategy adapted to start the engine with a certain constant fuel mass injected per cycle, there may be misfire cycles in the initial period or in the transitional process, which is mainly caused by the mismatch between the fuel mass injected per cycle and the instantaneous engine speed. Therefore, an optimized control strategy was put forward, namely, the engine starts with high fuel mass injection in the first several cycles and then decreases step by step during the transitional period until it operates at idle condition. This strategy was validated to decrease significantly the misfire cycles.

The basic theory of statistical energy analysis (SEA) is introduced, a commercial heavy duty truck cab is divided into 35 subsystems applying SEA method, and a three dimensional SEA model of the commercial heavy duty truck cab is created. Three basic parameters including modal density, damping loss factor and coupling loss factor are calculated with analytical and experimental methods. The modal density of the regular wall plate of the cab is calculated with traditional formula. The damping loss factors of the regular and complicated plates are obtained using analytical method and steady energy stream method. Meanwhile, the coupling loss factors of structure-structure, structure-sound cavity, and cavity-cavity are also calculated. Four kinds of excitations are in the SEA model, including sound radiation excitation of engine, engine mount vibration excitation, road excitation and wind excitation.