Search Results

In order to establish the correlation between objective and subjective evaluation of brake pedal feel for passenger cars, road tests of brake pedal feel were carried out and an evaluation method was proposed. In the road tests, subjective scores and objective measurements were obtained under the conditions of uniform and emergency braking. The objective measurements include pedal preload, low deceleration pedal force and travel, moderate deceleration pedal force and travel, brake response time and brake linearity. Using the theory of analytic hierarchy process (AHP), the design process of the evaluation method was established. Key setups including the hierarchical structure model, the judgement matrix and the score calculation method of objective measurements were described in detail. Then, the correlation between subjective and objective scores was analyzed. It can be concluded that the evaluation method is effective and it can be applied to brake pedal feel assessment and adjustment.

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.

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.

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.

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.

After obtaining the optimal trajectory through the lane change decision and trajectory planning, the last key technology for the automatic lane change assist system is to carry out the precise and rapid steering actuation according to the front wheel angle demand. Therefore, an automatic lane change system model including a BLDCM (brushless DC motor) model, a steering system model and a vehicle dynamics model is first established in this paper. Electromagnetic characteristics of the motor, the moment of the inertia and viscous friction etc. are considered in these models. Then, a SMC (Sliding Mode Control) algorithm for the steering system is designed to follow the steering angle input. The control torque of the steering motor is obtained through the system model according to steering angle demand. After that, the control current is calculated considering of electromagnetic characteristics of the BLDCM. Debugging and optimization of the control algorithm are done through simulations.

The automatic lane change assist system is an intelligent driving assistance technology oriented to traffic safety, which requires trajectory planning of the lane change maneuver based on the lane change decision. A typical scene of lane change for overtaking is selected, where the front vehicle in the same lane and the rear vehicle in the left lane are deemed to be potential dangerous vehicles through the lane change. Lane change trajectory equation is first established according to the general law of steering wheel angle through lane changes. Based on the relative position, velocity and acceleration information of the dangerous vehicles and the lane change vehicle, motions of these surrounding dangerous vehicles are predicted. At the same time, a multi-objective optimization function is established based on the relative longitudinal safety boundary. The objectives are the minimum safety distance, the lane change time and the front wheel angle.

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.

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.

In this paper, by analyzing multiple electro-hydraulic brake system schemes in detail, the idea of dual-motor electro-hydraulic brake system is proposed. As a new solution, the dual-motor electro-hydraulic brake system can actively simulate pedal feel, make the most of pedal power (from the driver), and reduce the maximum power output of each active power source remarkably, which is a distinctive innovation compared to most current electro-hydraulic brake systems. Following the proposed concept, a general research thought and method is conceived, and then a dual-motor electro-hydraulic brake system is designed. Finally, the simulation model is set up in AMESim software and its feasibility is simulated and verified.

Accurate control of both the timing and quantity of injection events is critical for engine performance and emissions. The most serious problem which reduces the accuracy of the control operation in such systems is a time delay of the responsiveness for the opening and closing operation of the electromagnetic valve. Modern electronic control systems should be capable of driving high speed solenoid injectors at a very fast switch frequency with high efficiency and acceptable power requirements. In this paper, the dynamic characteristic of a high speed servo-hydraulic solenoid injector for diesel engine, with different driving circuits and control methods, is investigated. A pre-energizing control strategy based on a dual power supply is applied to speed up the opening response time of the injectors.

Torque Vectoring Control for distributed drive electric vehicle is studied. A handling improvement algorithm for normal cornering maneuvers is proposed based on state variable feedback control: Yaw rate feedback together with steer angle feedforward is employed to improve transient response and steady gain of the yaw rate, respectively. According to the feedback coefficient's influence on the transient response, an optimization function is proposed to obtain optimum feedback coefficients under different speeds. After maximum feedforward coefficients under different speeds are obtained from the constraint of the motor exterior characteristic, final feedforward coefficients are calculated according to an optimal steering characteristic. A torque distribution algorithm is presented to help the driver to speed up during the direct yaw moment control.

The control in transient conditions when hydraulic brake and regenerative brake switch mutually is the key technical issue about electric vehicle hybrid brake system, which has a direct influence on the braking feel of driver and vehicle braking comfort. A coordination control system has been proposed, including brake force distribution correction module and motor force compensation module. Brake force distribution correction module has fixed the distribution results in hydraulic brake force intervention condition, hydraulic brake force evacuation condition and regenerative brake force low speed evacuation condition. Motor compensation module has compensated hydraulic system with motor system, which has fast and accurate response, thus the response of whole hybrid system has been improved.

The undesired shifting phenomenon(USP) occurs easily under the braking or climbing conditions etc., and its impact is the discomfort to the passengers or cause of vehicle's state contrary to the driver's intention, meanwhile, the wear of the clutch and synchronizer is increased, so their lifetime are greatly shortened. To the vehicle with dual clutch transmission (DCT), undesired shifting phenomenon will lead to frequent and unnecessary actuation of synchronizer for the use of pre-engagement synchronizer in the shifting control; therefore, its occurrence should be eliminated as far as possible. In this paper, the process of the undesired shifting of the vehicle with DCT is elaborated, then the generating cause of USP is described based on directed graph.

This set combines the book Road Vehicle Dynamics with its corresponding workbook companion, Road Vehicle Dynamics: Problems and Solutions. Road Vehicle Dynamics provides a detailed overview of the dynamics of road vehicle systems, giving readers an understanding of how physical laws, human factor considerations, and design choices affect ride, handling, braking, acceleration, and vehicle safety. Chapters cover analysis of dynamic systems, tire dynamics, ride dynamics, vehicle rollover analysis, handling dynamics, braking, acceleration, total vehicle dynamics, and accident reconstruction. The workbook will enable students and professionals from a variety of disciplines to engage in problem-solving exercises based on the material covered in each chapter of that book. It presents systematic rules of analysis that students can follow in a step-by-step manner to understand the efficiencies or shortcomings of various techniques.

Although Hydrostatic Transmission System (HTS) had been used in many places, such as machine tools, agriculture machinery, construction machinery, and vehicles, it had not been used in good performance. Twenty years ago many people began to design new hydrostatic transmission with higher efficiency. Hydrostatic Energy Storage Transmission System (HESTS) is one of new hydrostatic transmission system with higher efficiency. HESTS is more fit for being used in vehicle that is always running in undulating ground or starting and braking frequently. Construction of vehicular HESTS was analyzed, mathematical model of vehicular HESTS was established. The needed control strategies of vehicular HESTS were analyzed because there are many variables would be controlled in the new transmission system.

In this paper, the system identification theory and method using dynamic neural networks are presented, the multilayer feedforward networks employed, the backpropagation with adaptive learning rate algorithms proposed. Finally the comparision of network output with that of the hydrostatic drive system of secondary regulation is given, and output error, sum-squared error et al, or the results that embody the effect of system identification given sine input to it are provided.