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In the hybrid brake system of electric vehicle, due to the limitation of the motor braking force when the motor is at high speed and the failure of the regenerative braking force when the motor is at low speed, there are three transitional conditions in hybrid braking: the hydraulic brake system intervenes the braking, the hydraulic brake system withdraws the braking and the regenerative braking force withdraws the braking. Due to the response speed of the hydraulic system is slower than that of the motor, there is a large braking impact (the derivative of braking deceleration) in the transitional conditions of hybrid braking, which deteriorates the smoothness and comfort in braking. Aiming at the impact caused by the poor cooperation between the hydraulic braking force and the motor braking force, a coordinated strategy of double closed-loop feedback and motor force correction is proposed in this paper.

Automatic lane change of intelligent vehicles is a complex process. Besides of safety, feelings of the driver and passengers during the lane change are also very important. In this paper, a lane change trajectory planner is designed to generate an ideal collision-free trajectory to satisfy the driver’s preference. Various lane changing modes, gentle lane change, general lane change, radical lane change and personalized lane change, are designed to meet the needs of different passengers on vehicles simultaneously. In this paper, the condition of the two-lane change is studied. One vehicle is in front of the ego vehicle at the same lane and one is at the rear of the ego vehicle at the target lane. A trajectory planning method is then established based on constant speed offset and sine curve, vehicle distances and speed difference, etc. The key factors which can reflect drivers’ lane change characteristics are then acquired.

With the development of autonomous vehicle technology, there is an increasing tendency toward the application of intelligent sensors in environment-perception system on autonomous vehicle to assist vehicle in intelligent decision making relevant to autonomous driving. As for environment-perception system, a good track management method serves as the foundation, while multi-target tracking and multi-sensor data fusion are recognized as the key. In this paper, a track management method is proposed to deal with multi-target tracking based on the target-level data of multisource environmental sensors for autonomous vehicle. The track management includes four procedures as following: track initiation; point-track association; track update; track deletion. A modified fast algorithm for data association is applied in the point-track association procedure. Afterwards Kalman filter is implemented to update the track information of target. The algorithm has got through a simulation test.

Millimeter wave automotive radar is one of the most important sensors in the Advanced Driver Assistance System (ADAS) and autonomous driving system, which detects the target vehicles around the ego vehicle via processing transmitted and echo signals. However, the sampling rate of classical radar signal processing methods based on Nyquist sampling theorem is too high and the resolution of range, velocity and azimuth can’t meet the requirement of highly autonomous driving, especially azimuth. In spatial domain, targets are sparse distribution in the detection range of automotive radar. To solve these problems, the algorithm for targets location based on compressed sensing for automotive radar is proposed in this paper. Besides, the feasibility of the algorithm is verified through the simulation experiments of traffic scene. The range-doppler-azimuth model can be used to estimate the distance, velocity and azimuth of the target accurately.

The utilization of the SLAM (Simultaneous Localization and Mapping) technique was extended from the robotics to the autonomous vehicles for achieving the positioning. However, SLAM cannot obtain the global position of the vehicle but a relative one to the start. For sake of this, a fast and accurate system was proposed to obtain both the local position and the global position of vehicles based on mono-SLAM which realized the SLAM by using monocular camera with a lower cost and power consumption. Firstly, the rough latitude and longitude of current position was obtained by using common GPS without differential signal. Then, the Mono-SLAM operated on the consecutive video frames to generate the localization and local trajectory and its accuracy was further improved by utilizing the IMU information. After that, a piece of Map centered in the rough position obtained by common GPS was downloaded from the Open Street Map.

Target detection is essential to the advanced driving assistance system (ADAS) and automatic driving. And the data fusion of millimeter wave radar and camera could provide more accurate and complete information of targets and enhance the environmental perception performance. In this paper, a method of vehicle and pedestrian detection based on the data fusion of millimeter wave radar and camera is proposed to improve the target distance estimation accuracy. The first step is the targets data acquisition. A deep learning model called Single Shot MultiBox Detector (SSD) is utilized for targets detection in consecutive video frames captured by camera and further optimized for high real-time performance and accuracy. Secondly, the coordinate system of camera and radar are unified by coordinate transformation matrix. Then, the parallel Kalman filter is used to track the targets detected by radar and camera respectively.

Automotive radar is an important environment perception sensor for advance driving assistance system. It can detect objects around the vehicle with high accuracy and it works in all bad weathers. For traditional automotive radar, it cannot measure the objects’ height. Thus, a manhole cover on the road surface or a guideboard high above the road would be taken erroneously as a non-moving car. In such cases, the adaptive cruise system would decelerate or stop the vehicle erroneously and make the driver uncomfortable. A 3D automotive radar with two-dimensional electronic scanning can measure the targets’ height as well as the targets’ azimuth angle. This paper presents a 79 GHz ultra-wide band automotive 3D imaging radar. Due to the 4 GHz wide bandwidth, the range resolution of this radar can be as small as 3.75 cm.

In order to solve the problem that in the traditional trilateral positioning algorithm, the final positioning error is large when there is a certain error in the measured three-sided distance, a UWB positioning algorithm based on Back Propagation (BP) neural network is proposed. The algorithm utilizes the fast learning characteristic and the ability of approximating any non-linear mapping of neural network, and realizes the location of the mobile label through the TOA measurement value provided by the base station and the BP neural network. By comparing the traditional trilateral positioning algorithm, the BP neural network algorithm based on four distance inputs and the BP neural network algorithm based on four distance inputs with trilateral positioning coordinates, it can be seen that the positioning error of traditional trilateral positioning algorithm is 30 cm, and the positioning error of the positioning algorithm based on the BP neural network proposed in this paper is 10 cm.

Real-time and reliable perception of the surrounding environment is an important prerequisite for advanced driving assistance system (ADAS) and automatic driving. And vision-based detection plays a significant role in environment perception for automatic vehicles. Although deep convolutional neural networks enable efficient recognition of various objects, it has difficulty in accurately detecting special vehicles, rocks, road pile, construction site, fence and so on. In this work, we address the task of traffic scene understanding with semantic image segmentation. Both driveable area and the classification of object can be attained from the segmentation result. First, we define 29 classes of objects in traffic scenarios with different labels and modify the Deeplab V2 network. Then in order to reduce the running time, MobileNet architecture is applied to generate the feature map instead of the original models.

Being one of the most simple and basic driving scenarios, highway scenario can be one of the first scenarios to achieve autonomous driving. Both car following (CF) and lane changing (LC) are the most basic and frequent maneuver during highway driving tasks, and therefore become two key issues to focus on in recent researches about autonomous vehicle (AV). Different from conventional CF and LC researches that attach much importance to the character, psychology, perception ability, and driving experience of human drivers, more timely and accurate reactions based on fast perception and communication technology as well as the automatic actuator are hypotheses for this research. Moreover, based on these hypotheses, a modified intelligent driver model (MIDM) is proposed for AVs’ following behavior to alleviate the fluctuations caused by lane changing behaviors.

Connected and autonomous vehicles are different from traditional vehicles. The communication between vehicles (V2V) or between vehicles and infrastructures (V2I) renders it possible to convey traffic information (e.g. signal timing or speed advisory) from signal controllers to vehicles as well as vehicles to vehicles in real time. Taking this advantage, this paper aims to developing an algorithm which enables the interconnected autonomous vehicles running efficiently on arterials. A set of driving rules determining random behavior and swarm behavior of autonomous vehicles is developed based on swarm intelligence theory. Under control of these rules, each autonomous vehicle follows the same rules, which make it select target vehicle from all the optimal individuals in detection zone according to characteristics of itself, then approach to the target by changing lane, following former car, or accelerating.

Vehicle and pedestrian detection technology is the most important part of advanced driving assistance system (ADAS) and automatic driving. The fusion of millimeter wave radar and camera is an important trend to enhance the environmental perception performance. In this paper, we propose a method of vehicle and pedestrian detection based on millimeter wave radar and camera. Moreover, the proposed method complete the detection of vehicle and pedestrian based on dynamic region generated by the radar data and sliding window. First, the radar target information is mapped to the image by means of coordinate transformation. Then by analyzing the scene, we obtain the sliding windows. Next, the sliding windows are detected by HOG features and SVM classifier in a rough detect. Then using the match function to confirm the target. Finally detecting the windows in a precision detection and merging the detecting windows. The target detection process is carried out in the following three steps.

An experimental study is presented on the evaporation of diluted droplet-laden two-phase jet flows within a heated air channel co-flow. In this study, n-heptane is pre-atomized by an ultrasonic nozzle to produce droplet cluster with a median diameter of about15μm, and a continuous cold air flow is applied to carry the fuel droplet cluster to emerge from a nozzle tube, producing a free turbulent jet of droplet stream. The droplet stream is then introduced as a central jet into a square-shaped channel with heated air co-flow for evaporation investigations. With flexibilities of the initial properties of droplet stream and surrounding conditions of channel flow, the axial evolution of droplet size is determined to characterize the evaporation behavior of n-heptane droplet stream under various boundary conditions. The equivalence ratios of droplet streams are varied by changing both the carrier-air flow rate and the fuel flow rate.

Research studies have suggested that changes to the ignition system are required to generate a more robust flame kernel in order to secure the ignition process for the future advanced high efficiency spark-ignition (SI) engines. In a typical inductive ignition system, the spark discharge is initiated by a transient high-power electrical breakdown and sustained by a relatively low-power glow process. The electrical breakdown is characterized as a capacitive discharge process with a small quantity of energy coming mainly from the gap parasitic capacitor. Enhancement of the breakdown is a potential avenue effectively for extending the lean limit of SI engine. In this work, the effect of high-power capacitive spark discharge on the early flame kernel growth of premixed methane-air mixtures is investigated through electrical probing and optical diagnosis.

In view of the problem of low-frequency (less than 10Hz, such as 0.5Hz, 1.15Hz, 8Hz in this paper) longitudinal vibration exists in a pure electric vehicle, modeling methods of drive-line torsion vibration system are conducted. Firstly, dynamometer test is performed, signals of motor speed and seat rail acceleration are obtained, the frequency characteristics of flutter is determined using the order analysis and time frequency analysis. Then four types of modeling and analysis are investigated facing the drive-line torsion vibration problem, including single model without electromagnetic stiffness, branch model without electromagnetic stiffness, single model considering electromagnetic stiffness and branch model considering electromagnetic stiffness. The results show that, modeling taking into account the electromagnetic stiffness and branches can reflect more low-frequency characteristics helps to reveal the low-frequency longitudinal flutter of the researched electric vehicle.

In order to reduce high-frequency harmonic noise produced by the blower in the auxiliary system of a fuel cell vehicle (FCV), a narrowband active noise control (ANC) method instead of conventional passive mufflers is adopted since the blower demands clean air condition and expects good acoustic performance. However, in ANC practical applications, the frequency difference between reference signal and actual primary signal, i.e., frequency mismatch (FM), can significantly degrade the high-frequency performance of narrowband ANC system. In this paper, a new narrowband ANC system is proposed to compensate for the performance degeneration due to the existence of FM and improve noise reduction at high frequencies. The proposed system consists of two parts: the Filtered Error Least Mean Square (FELMS) algorithm filtering the primary signals at wide frequency range other than those at the targeted frequencies, and the FM removal algorithm proposed by Yegui Xiao.

A novel compound power-split hybrid transmission based on a modified Ravigneaux gear set is presented. The equivalent lever diagrams are used to investigate the electric operating modes for the hybrid powertrain, and then its dynamic and kinematic characteristics as well as efficiency characteristics are described in equations. A brake clutch mounted on the carrier shaft is proposed to enhance the electric driving efficiency for the hybrid transmission. Three types of electric operating mode are analyzed by the simplified combined lever diagrams and the system efficiency and torque characteristics for these electric operating modes are compared. A major influence on output torque of the hybrid transmission derived from the torque capability of motors and brake clutch is depicted.

Even though air compressors for traditional vehicles and fuel cell vehicles (FCVs) share many similarities, a fuel cell vehicle cannot directly employ the effective and mass-produced traditional vehicles' air compressors. This is because the fuel cell vehicles have special requirements, such as oil free, low flow rate with high pressure ratio, high efficiency, and low weight and volume. In order to find suitable air compressors to match the fuel cell system (FCS)'s requirements, different air compressors' performance and physical characteristics are compared. These air compressors include screw compressor with expander, roots compressor with expander, turbocompressor, and scroll compressor with expander. The comparison and analysis is both theoretical and practical. Results show that the turbocompressor and the roots compressor/expander have higher performance compared to the others in the aspects of input power, system efficiency, weight, and volume.

From traditional vehicle to pure electric vehicle and then to range-extended electric vehicle (ReEV), the electrification of vehicle has caused a great change in NVH characteristics. ReEV has the most complex noise and vibration problems because it behaves both like traditional vehicle and pure electric vehicle. In addition, it has its own specific NVH characteristics. Firstly, internal combustion engine drives the generator rather than drives the vehicle. Due to the structure and load change, auxiliary power unit (APU) performs specific NVH characteristics. In addition, the pure electric mode will have the high frequency noise generated from the driveline because the noise couldn't be masked by the engine. In system level, unexpected and confusing sound in passenger compartment may affect the driver's comfort and judgments. Therefore, more and more attentions on these issues should be paid. NVH study and improvement methods on ReEV are emphasized.

Durability and performance evaluation of the ageing catalyzed continuously regenerating trap (CCRT) on solid and volatile particles from diesel bus were studied through a set of transient TSI engine exhaust particle sizer spectrometer based on on-road test. Particle characteristics under stepped steady conditions and during regeneration were discussed in detail. Under idle and stepped steady conditions, total particle number and mass Emission Rate (ER) of each test presented rising trends as speed increase. Total number ERs of all tests showed downtrend as the CCRT aging. The particle number size distributions at different ageing stage showed changing characteristics due to developing filter mechanism. Compared with baseline data, the total number reduction rates at idle condition were incremental, from 91.4% to 98.9% as the CCRT ageing. Percentages of nuclei mode concentrations took higher range from 66.6% to 89.9% compared with the baseline data, 43.2-43.7%.