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Technical Paper

An Adaptive PID Controller with Neural Network Self-Tuning for Vehicle Lane Keeping System

2009-04-20
2009-01-1482
Vehicle lane keeping system is becoming a new research focus of drive assistant system except adaptive cruise control system. As we all known, vehicle lateral dynamics show strong nonlinear and time-varying with the variety of longitudinal velocity, especially tire’s mechanics characteristic will change from linear characteristic under low speed to strong nonlinear under high speed. For this reason, the traditional PID controller and even self-tuning PID controller, which need to know a precise vehicle lateral dynamics model to adjust the control parameter, are too difficult to get enough accuracy and the ideal control quality. Based on neural network’s ability of self-learning, adaptive and approximate to any nonlinear function, an adaptive PID control algorithm with BP neural network self-tuning online was proposed for vehicle lane keeping.
Technical Paper

Analysis of Illumination Condition Effect on Vehicle Detection in Photo-Realistic Virtual World

2017-09-23
2017-01-1998
Intelligent driving, aimed for collision avoidance and self-navigation, is mainly based on environmental sensing via radar, lidar and/or camera. While each of the sensors has its own unique pros and cons, camera is especially good at object detection, recognition and tracking. However, unpredictable environmental illumination can potentially cause misdetection or false detection. To investigate the influence of illumination conditions on detection algorithms, we reproduced various illumination intensities in a photo-realistic virtual world, which leverages recent progress in computer graphics, and verified vehicle detection effect there. In the virtual world, the environmental illumination is controlled precisely from low to high to simulate different illumination conditions in the driving scenarios (with relative luminous intensity from 0.01 to 400). Sedan cars with different colors are modelled in the virtual world and used for detection task.
Technical Paper

Driver Behavior Characteristics Identification Strategy for Adaptive Cruise Control System with Lane Change Assistance

2017-03-28
2017-01-0432
Adaptive cruise control system with lane change assistance (LCACC) is a novel advanced driver assistance system (ADAS), which enables dual-target tracking, safe lane change, and longitudinal ride comfort. To design the personalized LCACC system, one of the most important prerequisites is to identify the driver’s individualities. This paper presents a real-time driver behavior characteristics identification strategy for LCACC system. Firstly, a driver behavior data acquisition system was established based on the driver-in-the-loop simulator, and the behavior data of different types of drivers were collected under the typical test condition. Then, the driver behavior characteristics factor Ks we proposed, which combined the longitudinal and lateral control behaviors, was used to identify the driver behavior characteristics. And an individual safe inter-vehicle distances field (ISIDF) was established according to the identification results.
Technical Paper

Driving Behavior Prediction at Roundabouts Based on Integrated Simulation Platform

2018-04-03
2018-01-0033
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.
Technical Paper

Feasibility Study of Using WLTC for Fuel Consumption Certification of Chinese Light-Duty Vehicles

2018-04-03
2018-01-0654
This paper presents the feasibility study of using the worldwide harmonized light vehicles test cycle (WLTC) for the fuel consumption certification of Chinese Light-duty (LD) vehicles. First, the key steps and the technical routes of the development process of WLTC are summarized. Second, the operation data of 3082 vehicles in 41 typical cities of China are collected throughout the year. Then, the characteristics of the acquisition data are compared with WLTC. Finally, the feasibility of using WLTC for fuel consumption certification of Chinese LD vehicles is analyzed in three aspects, includes operation characteristics, weighting factors and fuel consumption. The result shows that there is obvious difference between WLTC and Chinese reality, and WLTC is not suitable for the fuel consumption certification of Chinese LD vehicles.
Technical Paper

Identification of Driver Individualities Using Random Forest Model

2017-09-23
2017-01-1981
Driver individualities is crucial for the development of the Advanced Driver Assistant System (ADAS). Due to the mechanism that specific driving operation action of individual driver under typical conditions is convergent and differentiated, a novel driver individualities recognition method is constructed in this paper using random forest model. A driver behavior data acquisition system was built using dSPACE real-time simulation platform. Based on that, the driving data of the tested drivers were collected in real time. Then, we extracted main driving data by principal component analysis method. The fuzzy clustering analysis was carried out on the main driving data, and the fuzzy matrix was constructed according to the intrinsic attribute of the driving data. The drivers’ driving data were divided into multiple clusters.
Technical Paper

Mode Transition Dynamic Control for Dual-Motor Hybrid Driving System

2013-10-14
2013-01-2487
Coordinated control of mode transition is an important part of the multi-mode hybrid vehicles' control strategy, combined with a vehicle torque distribution strategy to realize an optimal working condition of the power sources, as well as achieve smooth mode switching. This paper builds hybrid electric vehicle driveline dynamics model and depth analyzes drive mode transition process, coordinated control methods were provided to solve three types of mode switching, neural network algorithm was provided to estimate the engine torque. The results show that coordinated control can reduce torque fluctuations and decrease jerk during the transition of different modes to improve the vehicle drivability.
Technical Paper

Multi-Objective Optimization of Interior Noise of an Automotive Body Based on Different Surrogate Models and NSGA-II

2018-04-03
2018-01-0146
This paper studies a multi-objective optimization design of interior noise for an automotive body. An acoustic-structure coupled model with materials and properties was established to predict the interior noise based on a passenger car. Moreover, three kinds of approximation models related damping thickness and the root mean square of the driver’s ear sound pressure level were established through Latin hypercube method and the corresponding experiments. The prediction accuracy was analyzed and compared for the approximate response surface model, Kriging model and Radial Basis Function neural network model. On this basis, multi-objective optimization of the vehicle interior noise was conducted by using NSGA-II. According to the optimization results, the damping composite structure was applied on the car body structure. Then, the comparison of sound pressure level response at driver’s ear location before and after optimization was performed at speed of 60 km/h on a smooth road.
Journal Article

Objective Evaluation of Interior Sound Quality in Passenger Cars Using Artificial Neural Networks

2013-04-08
2013-01-1704
In this research, the interior noise of a passenger car was measured, and the sound quality metrics including sound pressure level, loudness, sharpness, and roughness were calculated. An artificial neural network was designed to successfully apply on automotive interior noise as well as numerous different fields of technology which aim to overcome difficulties of experimentations and save cost, time and workforce. Sound pressure level, loudness, sharpness, and roughness were estimated by using the artificial neural network designed by using the experiment values. The predicted values and experiment results are compared. The comparison results show that the realized artificial intelligence model is an appropriate model to estimate the sound quality of the automotive interior noise. The reliability value is calculated as 0.9995 by using statistical analysis.
Journal Article

Prediction of the Sound Absorption Performance of Polymer Wool by Using Artificial Neural Networks Model

2014-04-01
2014-01-0889
This paper proposes a new method of predicting the sound absorption performance of polymer wool using artificial neural networks (ANN) model. Some important parameters of the proposed model have been adjusted to best fit the non-linear relationship between the input data and output data. What's more, the commonly used multiple non-linear regression model is built to compare with ANN model in this study. Measurements of the sound absorption coefficient of polymer wool based on transfer function method are also performed to determine the sound absorption performance according to GB/T18696. 2-2002 and ISO10534- 2: 1998 (E) standards. It is founded that predictions of the new model are in good agreement with the experiment results.
Technical Paper

Research on Steering Performance of Steer-By- Wire Vehicle

2018-04-03
2018-01-0823
With the popularity of electrification and driver assistance systems on vehicle dynamics and controls, the steering performance of the vehicle put forward higher requirements. Thus, the steer-by-wire technology is becoming particularly important. Through specific control algorithm, the steer-by-wire system electronic control unit can receive signals from other sensors on the vehicle, realize the personalized vehicle dynamics control on the basis of understanding the driver’s intention, and grasp the vehicle movement state. At the same time, to make these driver assistance systems better cooperate with human drivers, reduce system frequent false warning, full consideration of mutual adaptation for the systems and the driver’s characteristics is critical. This paper focuses on the steering performance of steer-by-wire vehicle. Feature parameters are obtained from the virtual turning experiment designed on the driving simulator experimental platform.
Technical Paper

Research on a Neural Network Model Based Automatic Shift Schedule with Dynamic 3-Parameters

2005-04-11
2005-01-1597
To reach the goal of optimal performance match between engine and transmission, the dynamic characteristics of engine should be taken into consideration. In the paper, the dynamic torque and fuel consumption models of engine, described by a multi-layers feed forward neural network, were established. Based on that, the methods used to calculate the optimal dynamic and economical shift schedules with dynamic 3-parameters were put forward. The shift schedule with dynamic 3-parameters based on neural network model is proven to be superior to the shift schedule with only 2-parameters in both dynamic performance and fuel economy by the test.
Technical Paper

Research on the Classification and Identification for Personalized Driving Styles

2018-04-03
2018-01-1096
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.
Technical Paper

Sound Absorption Optimization of Porous Materials Using BP Neural Network and Genetic Algorithm

2016-04-05
2016-01-0472
In recent years, the interior noise of automobile has been becoming a significant problem. In order to reduce the noise, porous materials have been widely applied in automobile manufacturing. In this study, the simulation method and optimal analysis are used to determine the optimum sound absorption of polyurethane foam. The experimental simulation is processed based on the Johnson-Allard model. In the model, the foam adheres to a hard wall. The incident wave is plane wave. The function of the model is to calculate the noise reduction coefficient of polyurethane foam with different thickness, density and porosity. The back propagation neural network coupled with genetic optimization technique is utilized to predict the optimum sound absorption. A developed back propagation neural network model is trained and tested by the simulation data.
Technical Paper

Study on Dynamic Characteristics and Control Methods for Drive-by-Wire Electric Vehicle

2014-09-30
2014-01-2291
A full drive-by-wire electric vehicle, named Urban Future Electric Vehicle (UFEV) is developed, where the four wheels' traction and braking torques, four wheels' steering angles, and four active suspensions (in the future) are controlled independently. It is an ideal platform to realize the optimal vehicle dynamics, the marginal-stability and the energy-efficient control, it is also a platform for studying the advanced chassis control methods and their applications. A centralized control system of hierarchical structure for UFEV is proposed, which consist of Sensor Layer, Identification and Estimation Layer, Objective Control Layer, Forces and Motion Distribution Layer, Executive Layer. In the Identification and Estimation Layer, identification model is established by utilizing neural network algorithms to identify the driver characteristics. Vehicle state estimation and road identification of UFEV based on EKF and Fuzzy Logic Control methods is also conducted in this layer.
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