Search Results

This paper proposes an intelligent car testing and evaluation method based on digital twins, which is crucial for ensuring the proper functioning of autonomous driving systems. This method utilizes digital twin testing technology to effectively map and integrate real vehicles in real-world testing scenarios with virtual test environments. By enriching the testing and validation environment for smart cars, this approach improves testing efficiency and reduces costs. This study connects real test vehicles with simulation software testing toolchains to build a digital twin autonomous driving testing platform. This platform facilitates the validation, testing, and evaluation of functional algorithms, and case study is conducted through testing and validation of an emergency collision avoidance system. By rapidly applying digital twin testing and evaluation techniques for intelligent cars, this approach accelerates the development and deployment of autonomous vehicles.

The temperature management of the proton exchange membrane fuel cell cooling system is very important to the stability and life of the engine. The size and cycle switching of the cooling system and the rapid temperature rise of the system are inseparable from the thermostat. In this paper, a simulation model of the fuel cell cooling system is built for a 30KW fuel cell system, and its accuracy is verified by experiments. The temperature fluctuation of the system in the confluence mode is mainly studied, and the error is about 0.9 °C. The influence of the converging mode of the thermostat on the temperature of the cooling system is researched through the simulation platform. Based on the simulation model, the influence of thermostats with different external environments and opening degrees on the size cycle switching of cooling system is predicted, which provides optimization and guidance for the system control strategy.

Hydrogen safety is of great importance in proton exchange membrane fuel cell (PEMFC) systems. Anode pressure control has become a focus point in recent years. The differential pressure between anode and cathode in PEMFC system needs to be carefully controlled under a suitable threshold. In practice, the anode pressure is usually controlled about 20–30kPa higher than the cathode pressure to minimize nitrogen crossover and improve cell stability. High differential pressure could lead to irreversible damage in proton exchange membrane. PID control was the dominant method to control the anode pressure in the past. However, the anode pressure’s fluctuation when hydrogen mass flow suddenly changes is a long-term challenge. As the requirements of control precision are increasingly high, the traditional PID control needs to be improved. Several new control algorithms are presented in recent researches, however, mostly are theoretical and experimental.

Traffic accidents avoidance is one of the main advantages for automated vehicles. As one of the main causes of vehicle collision accidents, lane changing of the ego vehicle in case that the obstacle vehicles appear in the blind spot with uncertain motion intentions is one of the main goals for the automated vehicle. An intention-aware lane changing collision assistance strategy basing on traffic situation assessment in the complex traffic scenarios is proposed in this paper. Typical Regions of Interest (ROI) within the detection range of the blind spots are selected basing on the road topology structures and state space consisting of the ego vehicle and the obstacle vehicles. Then the motion intentions of the obstacle vehicles in ROI are identified basing on Gaussian Mixture Models (GMM) and the corresponding motion trajectories are predicted basing on the state equation.

Intelligent and connected vehicle (ICV) and new energy vehicle (NEV) will be two important directions of the automotive technology in the future, and the coordinated development of these two directions reflects relevantly the higher requirements put forward by nowadays society and people. Through the use of intelligent and connected technology (ICT), NEVs can exchange various traffic information data with the outside world (e.g. other running vehicles, road infrastructure, internet, etc.) in real time, which is so-called Vehicle to Everything (V2X). Based on the further analysis of the mutual traffic information, the vehicles can identify the current driving conditions and predict the future driving conditions effectively, which can realize the real time optimization of the energy management strategies (EMSs) of vehicles’ powertrain system, so as to meet the driving requirements of vehicles under different driving conditions.

Minimum energy consumption with maximum comfort driving experience define the ideal human mobility. Recent technological advances in most Advanced Driver Assistance Systems (ADAS) on electric vehicles not only present a significant opportunity for automated eco-driving but also enhance the safety and comfort level. Understanding driving styles that make the systems more human-like or personalized for ADAS is the key to improve the system comfort. This research focuses on the personalized and green adaptive cruise control for intelligent electric vehicle, which is also known to be MyEco-ACC. MyEco-ACC is based on the optimization of regenerative braking and typical driving styles. Firstly, a driving style model is abstracted as a Hammerstein model and its key parameters vary with different driving styles. Secondly, the regenerative braking system characteristics for the electric vehicle equipped with 4-wheel hub motors are analyzed and braking force distribution strategy is designed.

The paper analyzes the mechanism of automobile millimeter wave radar noise, this paper does not study radar noise from the angle of signal processing, but from the level of false detection and missed detection, at the same time, the noise mechanism is modeled and verified. Firstly, the purpose and significance of the research of radar vehicle noise are described, and then, we summarize and outline the macro phenomenon and the specific characteristics of the automobile millimeter wave radar noise.

With rapid increase of vehicles on the road, safety concerns have become increasingly prominent. Since the leading cause of many traffic accidents is known to be by human drivers, developing autonomous vehicles is considered to be an effective approach to solve the problems above. Although trajectory tracking plays one of the most important roles on autonomous driving, handling the coupling between trajectory-tracking control and ESP under certain driving scenarios remains to be challenging. This paper focuses on trajectory-tracking control considering the role of ESP. A vehicle model is developed with two degrees of freedom, including vehicle lateral, and yaw motions. Based on the proposed model, the vehicle trajectory is separated into both longitudinal and lateral motion. The coupling effect of the vehicle and ESP is analyzed in the paper. The lateral trajectory-tracking algorithm is developed based on the preview follower theory.

In this article, the regenerative braking system is designed, which can realize the torque allocation between electric braking and hydraulic braking, where the cost function designed in this article considers factors of braking torque following effect, energy regenerative power, and hydraulic braking consumed power. In addition, a complete anti-lock braking system (ABS) is designed, which is based on regenerative braking. With the optimal slip ratio as control target, target wheel speed, control wheel speed, braking torque control strategy, and enable/disenable control logic of ABS are determined. By MATLAB/Simulink-DYNA4 co-simulation and real vehicle test, the feasibility and applicability of ABS based on regenerative braking are verified, under the condition of small severity of braking.

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.

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.

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.

Based on the research and analysis of the current brake systems, this paper presents a novel electro-hydraulic brake system, which can better meet the functional requirements. The system mainly contains a master cylinder, two brake hydraulic cylinders and drive motors, two transmission mechanisms, thirteen solenoid valves, pedal force simulator, etc. Since the proposed brake system uses a dual motor along with two brake hydraulic cylinders, it has advantages in providing fast pressure response, flexible working modes, high precision and strong fault tolerance. In order to facilitate the study of pressure control algorithm for the proposed brake system, a mathematical model of the brake system is firstly established, then a multiplexed time-division pressure control algorithm is proposed to realize the simultaneous or partially simultaneous pressure control, which ensures the high precision and short response time.

CORUN CHS has recently developed a new power-split hybrid transmission platform CHS-HT2800, which has added 2 clutches compared to CHS-HT1800. With involvement of 2 E-motors CHS-HT2800 has 9 forward gears, including 3 electric forward gears and 6 hybrid forward gears. Among the 6 hybrid gears 4 gears are fixed-ratio gears and the remaining 2 gears are eCVT gears. This paper describes a strategy, designed for CHS-HT2800, how to select the optimal mode, the optimal gear and the optimal work point of the engine to achieve the best economy performance in the real traffic. Because it is universally accepted, that the WLTC cycle can represent real world vehicle operation on urban and extra-urban roads, motorways, and freeways. The introduced strategy will be validated with the help of the WLTC cycle.

The slip ratio of vehicle driving wheels is easily beyond a reasonable range in the complex and changeable driving conditions. In order to achieve the adaptive acceleration slip regulation of four-wheel driving (4WD) vehicle, a fuzzy control strategy of Automatic Drive Train Management (ADM) system based on road situation identification was proposed in this paper. Firstly, the influence on the control strategy of ADM system was analyzed from two aspects, which included the different road adhesion coefficients and the vehicle’s ramp driving state. In the meantime several quantitative expressions of relevant control parameters were derived. Secondly, the fuzzy logic control algorithm was adopted to design a road situation identification subsystem and a ramp driving state identification subsystem respectively. The former was based on the μ-S curve model, and the latter was based on the vehicle driving equilibrium equation.

In order to improve drivers’ acceptance to advanced driver assistance systems (ADAS) with better adaptation, drivers’ driving behavior should play key role in the design of control strategy. Adaptive cruise control systems (ACC) have many factors that can be influenced by different driving behavior. It is important to recognize drivers’ driving behavior and take human-like parameters to the adaptive cruise control systems to assist different drivers effectively via their driving characteristics. The paper proposed a method to recognize drivers’ behavior and intention based on Gaussian Mixture Model. By means of a fuzzy PID control method, a personalized ACC control strategy was designed for different kinds of drivers to improve the adaptabilities of the systems. Several typical testing scenarios of longitudinal case were created with a host vehicle and a traffic vehicle.

Although advanced driver assistance systems (ADAS) have been widely introduced in automotive industry to enhance driving safety and comfort, and to reduce drivers’ driving burden, they do not in general reflect different drivers’ driving styles or customized with individual personalities. This can be important to comfort and enjoyable driving experience, and to improved market acceptance. However, it is challenging to understand and further identify drivers’ driving styles due to large number and great variations of driving population. Previous research has mainly adopted physical approaches in modeling drivers’ driving behavior, which however are often very much limited, if not impossible, in capturing human drivers’ driving characteristics. This paper proposes a reinforcement learning based approach, in which the driving styles are formulated through drivers’ learning processes from interaction with surrounding environment.

Due to growing interest in automated driving, the need for better understanding of human driving behavior in uncertain environment, such as driving behavior at un-signalized crossroad and roundabout, has further increased. Driving behavior at roundabout is greatly influenced by different dynamic factors such as speed, distance and circulating flow of the potentially conflicting vehicles, and drivers should choose whether to leave or wait at the upcoming exit according to these factors. In this paper, the influential dynamic factors and driving behavior characteristics at the roundabout is analyzed in detail, random forest method is then deployed to predict the driving behavior. For training the driving behavior model, four typical roundabout layouts were created under a real-time driving simulator with PanoSim-RT and dSPACE. Traffic participants with different motion style were also set in the simulation platform to mimic real driving conditions.

Most of the Advanced Driver Assistance System (ADAS) applications are aiming at improving both driving safety and comfort. Understanding human drivers' driving styles that make the systems more human-like or personalized for ADAS is the key to improve the system performance, in particular, the acceptance and adaption of ADAS to human drivers. The research presented in this paper focuses on the classification and identification for personalized driving styles. To motivate and reflect the information of different driving styles at the most extent, two sets, which consist of six kinds of stimuli with stochastic disturbance for the leading vehicles are created on a real-time Driver-In-the-Loop Intelligent Simulation Platform (DILISP) with PanoSim-RT®, dSPACE® and DEWETRON® and field test with both RT3000 family and RT-Range respectively.

When automobiles are at the threat of collisions, steering usually needs shorter longitudinal distance than braking for collision avoidance, especially under the condition of high speed or low adhesion. Thus, more collision accidents can be avoided in the same situation. The steering assistance is in need since the operation is hard for drivers. And considering the dynamic characteristics of vehicles in those maneuvers, the real-time and the accuracy of the assisted algorithms is essential. In view of the above problems, this paper first takes lateral acceleration of the vehicle as the constraint, aiming at the collision avoidance situation of the straight lane and the stable driving inside the curve, and trajectory of the collision avoidance is derived by a quintic polynomial.