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Recently, electro-hydraulic brake systems (EHB) has been developed to take place of the vacuum booster, having the advantage of faster pressure build-up and continuous pressure regulation. In contrast to the vacuum booster, the pressure estimation for EHB is worth to be studied due to its abundant resource (i.e. electric motor) and cost-effective benefit. This work improves an interconnected pressure estimation algorithm (IPEA) based on actuator characteristics by introducing the vehicle dynamics and validates it via vehicle tests. Considering the previous IPEA as the prior pressure estimation, the wheel speed feedback is used for modification via a proportional-integral (PI) observer. Superior to the IPEA based on actuator characteristics, the proposed PEA improves the accuracy by more than 20% under the mismatch of pressure-position relation.

Safety is the cornerstone for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems (ADS). To assess the safety of a traffic situation, it is essential to predict motion states of traffic participants in the future with mathematic models. Accurate vehicle trajectory prediction is an important prerequisite for reasonable traffic situation risk assessment and appropriate decision making. Vehicle trajectory prediction methods can be generally divided into motion model based methods and maneuver model based methods. Vehicle trajectory prediction based on motion models can be accurate and reliable only in the short term. While vehicle trajectory prediction based on maneuver models present more satisfactory performance in the long term, these maneuver models rely on machine learning methods. Abundant data should be collected to train the maneuver recognition model, which increases complexity and lowers real-time performance.

In this paper, a novel method is proposed to establish the vehicle yaw stability criterion based on the sideslip angle-yaw rate (β-r) phase plane method. First, nonlinear two degrees of freedom vehicle analysis model is established by adopting the Magic Formula of nonlinear tire model. Then, according to the model in the Matlab/Simulink environment, the β-r phase plane is gained. Emphatically, the effects of different driving conditions (front wheels steering angle, road adhesion coefficient and speed) on the stability boundaries of the phase plane are analyzed. Through a large number of simulation analysis, results show that there are two types of phase plane: curve stability region and diamond stability region, and the judgment method of the vehicle stability domain type under different driving conditions is solved.

Vehicle sideslip angle estimation is of great importance to the vehicle stability control as it could not be measured directly by ordinary vehicle-mounted sensors. As a result, researchers worldwide have carried out comprehensive research in estimating the vehicle sideslip angle. First, as the attitude would affect the acceleration information measured by the IMU directly, different kinds of vehicle attitude estimation methods with multi-sensor fusion are presented. Then, the estimation algorithms of the vehicle sideslip angle are classified into the following three aspects: kinematic model based method, dynamic model based method, and fusion method. The characteristics of different estimation algorithms are also discussed. Finally, the conclusion and development trend of the sideslip angle estimation are prospected.

Vehicle sideslip angle is significant for electronic stability control devices and hard to estimate due to the nonlinear and uncertain vehicle and tire dynamics. In this paper, based on the two track vehicle dynamic model considering the tire pneumatic trail variation, the vehicle sideslip angle estimation method was proposed. First, the extra steering angle of each wheel caused by kinematics and compliance characteristics of the steering system and suspension system was analyzed. The steering angle estimation method was designed. Since the pneumatic trail would vary with different tire slip angle, distances between the center of gravity (COG) and front&rear axle also change with the tire slip angle. Then, based on the dynamic pneumatic trail and estimated steering angle, we modified the traditional two track vehicle dynamic model using a brush tire model. This model matches the vehicle dynamics more accurately.

High-speed vehicles in low illumination environments severely blur the images used in object detectors, which poses a potential threat to object detector-based advanced driver assistance systems (ADAS) and autonomous driving systems. Augmenting the training images for object detectors is an efficient way to mitigate the threat from motion blur. However, little attention has been paid to the motion of the vehicle and the position of objects in the traffic scene, which limits the consistence between the resulting augmented images and traffic scenes. In this paper, we present a vehicle kinematics-based image augmentation algorithm by modeling and analyzing the traffic scenes to generate more realistic augmented images and achieve higher robustness improvement on object detectors against motion blur. Firstly, we propose a traffic scene model considering vehicle motion and the relationship between the vehicle and the object in the traffic scene.

In order to avoid the influence of the change of the camera pitch angle and the variation of the height of the ground on the ranging accuracy, improve the real-time performance of the algorithm by substituting the current widely-used monocular vision ranging algorithm which builds the optical model based on the shadow of the vehicle floor and the lane line, as well as avoid the classification of vehicle detection, a vehicle distance measurement algorithm based on monocular vision and license plate width is established. Firstly, the target image acquisition and preprocessing are studied. Then the paper studies the license plate image location segmentation method based on accelerated template matching. On this basis, the algorithm for obtaining the ratio of license plate width to image width is studied, and the function of vehicle distance and license plate ratio width is established.

With the increase of motor speed and the deterioration of operating environment, it is more difficult to predict the transient temperature field (TTF). Meanwhile, it is difficult to obtain the temperature test dataset of key nodes under various complete road conditions, so the cost of bench test or real vehicle test is high. Therefore, it is of great significance to establish a high fidelity, lightweight temperature prediction model which can be applied to real vehicle thermal management for ensuring the safe and stable operation of motor. In this paper, a physical model simulating electromagnetic-heat-flow multi-physical coupling of permanent magnet synchronous motor (PMSM) in electric drive gearbox (EDG) is established, and the correctness of the model is verified by the actual EDG bench test.

Estimating the motion state of peripheral targets is a very important part in the environment perception of intelligent vehicles. The accurate estimation of the motion state of the peripheral targets can provide more information for the intelligent vehicle planning module which means the intelligent vehicle is able to anticipate hazards ahead of time. To get the motion state of the target accurately, the target’s range, velocity, orientation angle and yaw rate need to be estimated. Three-dimensional high-resolution automotive millimeter wave radar can measure radial range, radial velocity, azimuth angle and elevation angle about multiple reflections of an extended target. Thus, the three-dimensional range information and three-dimensional velocity information can be obtained. With multiple three-dimensional high-resolution automotive millimeter-wave radar, it is possible to measure information in various directions of a target.

Carbon fiber reinforced plastic (CFRP) composite materials have gained particular interests due to their high specific modulus, high strength, lightweight and perfect corrosion resistance. However, in reality, CFRP composite materials cannot be used alone in some critical places such as positions of joints with hinges, locks. Therefore, metal reinforcements are usually necessary in local positions to prevent structure damage. Besides, if uncertainties present, obtained optimal structures may experience in failures as the optimization usually pushes solutions to the boundaries of constraints and has no room for tolerance and uncertainties, so robust optimization should be considered to accommodate the uncertainties in practice. This paper proposes a mixed topology method to optimize metal and carbon fiber reinforced plastic composite materials simultaneously under nondeterministic load with random magnitude and direction.

The cab is an essential part of a light truck, and its fatigue durability performance plays an important role in the design and development stage. Accelerated fatigue bench test has been widely applied to product development of carmakers for its low cost and short development cycle. However, in reality, interference exists generally in torsional conditions for the light truck cab when tested on the 4-post vehicle road simulation system. To solve this problem and minimize the lateral force applied on the hydraulic cylinders, the direction and size combinations of displacement release about front and rear suspensions were discussed based on multi-body dynamics simulation and fixture design theory in this paper. Through comparative study, the optimum design and layout scheme of fixtures was determined to conduct the next test procedure. The weak positions of the light truck cab were firstly predicted by utilizing finite element method (FEM) and fatigue analysis theory.

The foundation of both advanced driving assistance system(ADAS) and automated driving (AD) is an accurate environment perception system(EPS). However, evaluation and test method of EPS are seldom studied. In this paper, naturalistic driving environment was studied and test scenarios for EPS under rear-end collision risk were proposed accordingly. To describe driving environment, a new concept named environment perception element(EPE) was first proposed in this paper, which refers to all the objects that the EPS must perceive during driving. Typical environment perception elements include weather and light conditions, road features, road markings, traffic signs, traffic lights, other vehicles, pedal cyclists and pedestrians and others. Driving behaviors collected in Shanghai, China were classified and rear-end collision risk scenarios were obtained and described using EPEs. Probability distribution of EPEs was therefore obtained.

The price of fossil fuels and the increasing inexorable energy crisis have become vital issues for everyone. Tongji University EconoPower Racing Team was established to participate in the “Honda EconoPower Cup” annually. Every contestant in the competition must finish a certain distance in the fixed time, with the gasoline supplied by the committee. After that the committee will measure the fuel consumption of every team and calculate the distance per liter fuel (the farther the better) to determine the champion. In order to enhance the EP vehicle's achievement we've made some improvements, such as framework, body, engine's optimization and so on. In this passage we mainly state some details of our research approaches in framework, steering, transmission, shape and driving strategy. The main technologies were: friction reduction, lightweight, enhancement of power train efficiency, tire selection and driving strategy.

This article conducts a research on the active control differential (ACD) yaw moment stability control for central motor driven automobiles. By calculation, the active control differential yaw moment generation ability which is limited by the maximum differential twist ratio and the motor output torque is not enough compared with traditional Electronic Stability Program (ESP). A Matlab and CarSim joint simulation is applied on double lane change and sine wave steering input condition, through which the active control differential effect is analyzed. It is concluded that yaw moment control using active control differential has improved the steering sensitivity and yaw rate tracking effect to some extent in double lane change test and it also has been verified that it works effectively to keep the stability of the vehicle in sine wave test.

In this paper, a speed tracking controller is designed for the All-terrain vehicles. The method of feedforward with state variable feedback based on conditional integrators is adopted by the proposed control algorithm. The feedforward is designed considering the influence of the road slope on the longitudinal dynamics, which makes the All-terrain vehicles satisfy the acceleration demand of the upper controller when it tracks the desired speed on the road with slope varying greatly. The road slope is estimated based on a combined kinematic and dynamic model. This method solves the problem that road slope estimation requires an accurate vehicle dynamic model and are susceptible to acceleration sensor bias. Based on the vehicle dynamic model and the nonlinear tire model, the method of conditional integration is used in the state variable feedback, which considers the saturation constraint of the actuator with the intention of preventing the divergent integral operation.

Hydrogen energy is a kind of secondary energy with an abundant source, wide application, green, and is low-carbon, which is important for building a clean, low-carbon, safe, and efficient energy system and achieving the goal of carbon peaking and being carbon neutral. In this paper, the effect of nozzle position, hydrogen injection timing, and ignition timing on the in-cylinder combustion characteristics is investigated separately with the 13E hydrogen engine as the simulation object. The test results show that when the nozzle position is set in the middle of the intake and exhaust tracts (L2 and L3), the peak in-cylinder pressure is slightly higher than that of L1, but when the nozzle position is L2, the cylinder pressure curve is the smoothest, the peak exothermic rate is the lowest, and the peak cylinder temperature is the lowest.

The camera-based advanced driver assistance systems (ADAS) like lane departure warning system (LDWS) and lane keeping assist (LKA) can make vehicles safer and driving easier. Lane detection is indispensable for these lane-based systems for achieving vehicle local localization and behavior prediction. Since the vision is vulnerable to the variable environment conditions such as bad weather, occlusions and illumination, the robustness is important. In this paper, a robust algorithm for detecting and tracking multiple lanes with arbitrary shape is proposed. We extend the previously lane detection and tracking process from the space domain to the temporal-spatial domain by using a more robust and general multi-lane model. First, new slice images containing temporal information are generated from image sequences. Instead of binarization process, we use a more general detector for extracting the lane marker candidates with prior knowledge to generate the binary slice image.

Aiming at the problems of ineffective collision avoidance and vehicle instability in the process of vehicle emergency braking in road conditions with low adhesion and sudden change in adhesion coefficient, a stability-coordinated emergency braking and collision avoidance control system SEBCACS) is proposed. First, according to the motion of the ego vehicle and the target vehicle as well as the road adhesion conditions, a collision time model is proposed for evaluating the vehicle collision risk, and the expected deceleration required to avoid the collision is calculated. Then, the MPC method is used to calculate the yaw moment generated by the four-wheel braking force required to maintain vehicle stability according to the actual and reference yaw rate and side slip angle deviation. Then it is decided whether to implement additional yaw moment control according to the body stability evaluation results.

Accurate and reliable localization in GNSS-denied environments is critical for autonomous driving. Nevertheless, LiDAR-based and camera-based methods are easily affected by adverse weather conditions such as rain, snow, and fog. The 4D Radar with all-weather performance and high resolution has attracted more interest. Currently, there are few localization algorithms based on 4D Radar, so there is an urgent need to develop reliable and accurate positioning solutions. This paper introduces RIO-Vehicle, a novel tightly coupled 4D Radar/IMU/vehicle dynamics within the factor graph framework. RIO-Vehicle aims to achieve reliable and accurate vehicle state estimation, encompassing position, velocity, and attitude. To enhance the accuracy of relative constraints, we introduce a new integrated IMU/Dynamics pre-integration model that combines a 2D vehicle dynamics model with a 3D kinematics model.

In order to realize driving safety in highway scenarios, autonomous vehicles need to predict and reason about the driving intentions and motion trajectories of surrounding target vehicles in the near feature. Essentially, trajectory prediction of target vehicles can be viewed as a typical time series generation problem, which predicts the future trajectory of the vehicle through analyzing the input of historical trajectory information or its control signals. In actual traffic scenarios, the movement between vehicles is a process of mutual game and cooperation, namely the future trajectory of a vehicle is not only related to its own historical trajectory, but also to surrounding vehicles motion. However, different surrounding traffic participants have different influence on the target vehicle, and the future motion of the vehicle is often affected by some specific surrounding traffic agents deeply.