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Technical Paper

Interior Noise Prediction and Analysis of Heavy Commercial Vehicle Cab

2011-09-13
2011-01-2241
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.
Technical Paper

Automatic Drive Train Management System for 4WD Vehicle Based on Road Situation Identification

2018-04-03
2018-01-0987
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.
Journal Article

A Novel Method of Radar Modeling for Vehicle Intelligence

2016-09-14
2016-01-1892
The conventional radar modeling methods for automotive applications were either function-based or physics-based. The former approach was mainly abstracted as a solution of the intersection between geometric representations of radar beam and targets, while the latter one took radar detection mechanism into consideration by means of “ray tracing”. Although they each has its unique advantages, they were often unrealistic or time-consuming to meet actual simulation requirements. This paper presents a combined geometric and physical modeling method on millimeter-wave radar systems for Frequency Modulated Continuous Wave (FMCW) modulation format under a 3D simulation environment. With the geometric approach, a link between the virtual radar and 3D environment is established. With the physical approach, on the other hand, the ideal target detection and measurement are contaminated with noise and clutters aimed to produce the signals as close to the real ones as possible.
Technical Paper

A Comprehensive Testing and Evaluation Approach for Autonomous Vehicles

2018-04-03
2018-01-0124
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.
Technical Paper

Studies on Drivers’ Driving Styles Based on Inverse Reinforcement Learning

2018-04-03
2018-01-0612
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.
Journal Article

Physical Modeling of Shock Absorber Using Large Deflection Theory

2012-04-16
2012-01-0520
In this paper, a shock absorber physical model is developed. Firstly, a rebound valve model which is based on its structure parameters is built through using the large deflection theory. The von Karman equations are introduced to discover the physical relationships between the load and the deflection of valve discs. An analytical solution of the von Karman equations is then deducted via perturbation method. Secondly, the flow equations and the pressure equations of the shock absorber operating are investigated. The relationship between fluid flow rate and pressure drop of rebound valve is analyzed based on the analytical solution of valve discs deflection. Thirdly, an inter-iterative process of flow rate and pressure drop is employed in order to adequately consider the influence of fluid flow on damping force. Finally, the physical model is validated by comparing the experimental data with the simulation output.
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