Car Test Research Articles

Parallel Parking System Design with Fuzzy Logic Control

The difficulty with parallel parking, especially when going backwards is when adjusting the steering wheel so that the car can park in the right direction. Based on these problems, this study aims to design an automatic parking system. To improve parking success, a trajectory tracking system with fuzzy logic control will be implemented on a 1/7.5 scale test car. The coordinates obtained from the bicycle kinematic model are compared with the design trajectory. The difference between the facing direction read by the gyroscope sensor and the trajectory design is processed using a fuzzy logic controller to direct the servo motor as the steering wheel. The test results obtained that the error value for the position and direction facing in the automatic parking test using fuzzy logic control is 1.25cm and 1.59°. Keywords: Trajectory tracking, gyroscope, fuzzy logic control, parallel parking

BUILDING OPENDRIVE MODEL FROM MOBILE MAPPING DATA

Abstract. The development of self-driving vehicles has been efficiently accelerated by applying computer simulations. These environments can achieve the best result only if reality has been digitized: highly accurate and detailed maps are required. The high-definition (HD) maps aim to fulfill these requests. Companies and academia are seeking adequate technology, where OpenDRIVE standard seems to win this race.A software was developed to create high-definition road models for self-driving car testing purposes, which can also be used in driving simulators, for example, for testing vehicle prototypes.The data of a sample area was received located in northern Budapest, which included inconsistent information about the lanes of the road network. The top-priority task was to build a highly structured, consistent model satisfying this open standard, an increasingly popular modeling format to describe the environment for various simulating software. In order to do that, the relevant information had to be extracted from an AutoCAD file. The result was a collection of points that was added to a software module written in C++. The code has built a structure from those points, including determining connections between lanes, building roads from the lanes, determining connections between roads, adding road marks, and building the final OpenDRIVE model.Two different sets of data have been used thus far in the software: a circa 8 km long road section and a separate junction – meaning about 20,000 points to process. Two independent models were created; both were tested in various automotive simulators – including IPG CarMaker, probably the most well-known simulating tool used in car manufacturing – which showed that both are indeed correct. The overall time required to build those models from simple text files to well-structured OpenDRIVE models is no more than two minutes, while before the software development – when all of those aforementioned processes were done by humans – it required several weeks, if not months.The software is already capable of building basic road models but is nowhere near finished. Numerous other features can be added to it as well, for instance, traffic signs and signals and environmental elements such as roadside objects, trees, and buildings.

China's self-driving car legislation study

The ongoing developments of artificial intelligence have made the self-driving car industry enter the phase of extensive road testing in recent years. However, in China, research on self-driving car legislation is basically nonexistent. This paper focuses on comprehensively analyzing the self-driving car legislation and testing process based on the latest provisions in both China and the United States and conducts a comparative study. This study gives a detailed analysis of legislation trends in the legal field and provides practical suggestions for promoting the self-driving car industry in China as well as for that in other countries.

INDUSTRI MEDIA BUDAYA POPULER ANALISIS SEMIOTIKA PEIRCE PADA DRAMA KOREA START UP

The focus of this studied lies in understanding the meaning behind the 'start up' Korean drama scene. The purpose of this study was to learned about the popular Korean culture. Available representations may help an interpretation of the performer starting the Korean drama. The approach used was a qualitative approach with a semiotic studied from peirce. There were 3 points of interest in peirce semiotics. Which were icon, index and symbol. The icon portrayed in drakor startup, the shooting site was at the sandbox and the hangang river. Both were known as an iconic sites in South Korea. Then the index seen in the startup film is when seo dal mi was having a religious service as she goes to her car test. Finally, the signed of drakor startup is the festival of chuseok. Further research is recommended for closer analysis so as to provide an even better representation of peirce semiotics analysis.

Individual Drive-Wheel Energy Management for Rear-Traction Electric Vehicles with In-Wheel Motors

In-wheel motor technology has reduced the number of components required in a vehicle’s power train system, but it has also led to several additional technological challenges. According to kinematic laws, during the turning maneuvers of a vehicle, the tires must turn at adequate rotational speeds to provide an instantaneous center of rotation. An Electronic Differential System (EDS) controlling these speeds is necessary to ensure speeds on the rear axle wheels, always guaranteeing a tractive effort to move the vehicle with the least possible energy. In this work, we present an EDS developed, implemented, and tested in a virtual environment using MATLAB™, with the proposed developments then implemented in a test car. Exhaustive experimental testing demonstrated that the proposed EDS design significantly improves the test vehicle’s longitudinal dynamics and energy consumption. This paper’s main contribution consists of designing an EDS for an in-wheel motor electric vehicle (IWMEV), with motors directly connected to the rear axle. The design demonstrated effective energy management, with savings of up to 21.4% over a vehicle without EDS, while at the same time improving longitudinal dynamic performance.

Anti-balance Load Control System Applied to an Overhead Crane Prototype Activated by Voice Commands

This paper presents a proposal of anti-balance control applied to a didactic overhead crane, with the possibility of its activation by voice commands through a Graphical User Interface. It was opted for the application of fuzzy intelligent control for the load anti-balance and Proportional-Derivative (PD) classic control with Ziegler-Nichols (ZN) tuning for the car sidelong movement. The whole control is embedded in an ATmega 2560 microcontroller, responsible for commuting between the two forms of control, as well as accomplish the communication with the interface. Moreover, it was used the Mel-Frequency Cepstral Coefficients for the characterization of the voice signals and a Multilayer Perceptron artificial neural network for the classification of the extracted coefficients. After the accomplishment of the car movement tests and load balance analysis, it was verified that the fuzzy controller for the sidelong movement allied to the ZN tuning for the anti-balance PD control and the speech commands recognition by artificial neural network were effective for the studied application.

Car Surveillance Video Summarization Based On Car Plate Detection

Today, video is a common medium for sharing information. Navigating the internet to download a certain form of video, it takes a long time, a lot of bandwidth, and a lot of disk space. Since sending video over the internet is too costly, therefore video summarization has become a critical technology. Monitoring vehicles of people from a security and traffic perspective is a major issue. This monitoring depends on the identification of the license plate of vehicles. The proposed system includes training and testing stages. Training stage comprises: video preprocessing, Viola-Jones training, and Support Vector Machine (SVM) optimization. Testing stage contains: test video preprocessing, car plate (detection, cropping, resizing, and grouping detecting test car plate, feature extraction using HOG feature. The total time of local recorded videos is (19.5 minutes), (15.5 minutes) for training, and (4 minutes) for testing. This means, (79.5%) for training and (20.5%) for testing. The proposed video summarization has got maximum accuracy of (86%) by using Viola-Jones and SVM by reducing the number of original video frames from (7077) frames to (1200) frames. The accuracy of the Viola-Jones object detection process for training 700 images is (97%). The accuracy of the SVM classifier is (99.6%).

Methods for Sweat Detection in Cars Based on Thermal Images of the Face

As part of automatization of driving, an increasing number of functions in the vehicle interior are being automated. Climate automation is a focus of the developers, since manual operation can lead to distraction from the driving task. This requires information about the vehicle’s surroundings, as well as information about the occupants. An example of such information is the measurement of the occupant’s perspiration. In this work we measure the local temperature variation distribution caused by sweating by thermal imaging and evaluate the resulting heat map by methods commonly applied to characterize surface roughness. We compare different characteristic values of the surface roughness according to EN ISO 25178, as well as the radially averaged power spectral density (RAPSD). Using signal detection theory, we evaluate the robustness of the different measures of roughness of the heat maps of selected areas on the forehead of persons sitting in the driver seat of a test car. The results show that the different methods provide different levels of distinction. The RAPSD value provides the best differentiation with 90% hits and 5% false alarms. If sweating is detected in the vehicle, appropriate countermeasures can then be initiated.

Performance Evaluation of a Single Sensor Control Scheme Using a Twin-Tube MR Damper Based Semi-active Suspension

Magneto-rheological (MR) dampers have a promising future for application in automotive semi-active suspensions. The damping force produced by MR dampers can be modulated by controlling the electric current supplied to it. The present work explores a twin-tube MR damper working in valve mode for application in a semi-active SUV suspension system. Further, the performance of a single sensor control scheme is evaluated. The MR damper is characterized in a damper testing machine to demonstrate the MR behaviour and also to show that it develops similar magnitude of force as a passive damper used in SUV suspension. To prove the superiority of semi-active suspension, a single degree of freedom quarter car test rig is built and ground excitation is given in the form of displacement input from a hydraulic actuator. Constant current control, Skyhook control and Rakheja-Sankar (RS) control method are employed as three different control strategies and compared with passive suspension to study the advantages. Peak acceleration response of the sprung mass is studied for better passenger ride comfort and peak ground force is studied for preventing damage to road surface as well as to vehicle suspension elements. RS control provides much lower sprung mass vertical acceleration than Skyhook control and constant current control. RS control method led to 36% reduction of peak ground force when compared to Skyhook control. For a semi-active suspension using twin-tube MR damper, RS control method provides better ride comfort to passengers due to lower peak vertical acceleration when compared to constant current control or Skyhook control method. Moreover, for preventing damage to road surface as well as to vehicle suspension elements, RS control method, requiring a single sensor, is a much better choice.

Practical Vitality of Green Bonds and Economic Benefits

Climate change is an overarching challenge for achieving sustainable development. “Green” or “Climate” Bonds are often seen as a financial instrument that may help overcome low-carbon investment defiance. This paper explores green bonds’ potential contribution to low-carbon transition and the corporate sector’s benefit following the stock market reaction. This paper focuses on a new and fascinating subject because the green bonds market is under constant scrutiny since the emergence of the first green bond in 2007. Anticipating the significance of action towards climate change is continuously increasing over time. This project can be seen as a supporting argument for investing in green bonds and fighting against climate change. This study investigates the recent developments and challenges in the green bond market. I used matching criteria and performed multivariate OLS regression to test whether the green bond is priced differently than conventional ones. The result finds that green bonds are cheaper than conventional bonds with a 1.93–2.24 per cent premium, consistent with prior studies in this topic. I used a sample of 200 corporate green bonds issued after the Paris Agreement, i. e., from December 2015 to December 2019. I further document that the stock market reacts positively to green bonds’ announcements. For this, I performed the CAR test on a company’s stock price, which gives a statistically significant abnormal return of 0.23 per cent and 1.14 per cent over time windows 10 and 20 days, respectively. Moreover, green bonds’ environmental performance on carbon emission reduction proved to be an insignificant player. For this, I tested a relationship between green bond labels and the firms’ carbon emission. The mixed results suggest that maybe green bonds are performing well economically, but it is still far from achieving its practical goal.

Design and implementation of comfort-quality HVAC sound inside a vehicle cabin

This study presents a practical heating, ventilation, and air-conditioning (HVAC) system that emits good-quality sound along with a pleasant and cool climate inside a vehicle cabin. This development included two steps. The first step was to design a new HVAC sound while the second step focused on the development of an active electric control system (AECS) inside cars; the AECS is used to emit a HVAC sound inside the vehicle cabin. To design the new HVAC sound with a pleasant and cool feel, a sound-quality index is necessary to evaluate which sound is pleasant. Therefore, two indices was developed based on subjective tests and the psychoacoustic parameters of HVAC sounds of 7 luxury passenger cars. A new HVAC sound was designed by adjusting the magnitude and phase of the HVAC sound recorded inside a test car. After designing the new HVAC sound, to deploy it at the driver seat of the test car, the AECS was employed. The source signal of the AECS was obtained using an inverse filter, which was designed based on the least-square method. The source signal was used as the input signal of the car audio part of the AECS. The output sound of the AECS was added to the existing HVAC sound to achieve a new HVAC sound at the driver seat. The sound quality of the new HVAC system was evaluated using the two developed indices.

Research on pedestrian detection using optimized mask R-CNN algorithm in low-light road environment

Aiming to the performance degradation of the object detection algorithms in low-light environment, the image fusion module (MSRCR-IF) proposed in this paper is introduced into the object detection network based on the object detection algorithm of Mask R-CNN. This proposed fusion algorithm adjusts Region Proposal Network (RPN) and delete instance mask branch to achieve better pedestrian detection performance of algorithm in low-light environment. The experimental results reveal that the algorithm proposed in this paper has better detection performance than other current mainstream algorithms in COCO2017 data set, and the average detection accuracy of 85.05% was achieved under the self-built low-light road environment data set, which was 4.66% higher than before improvement. In order to verify the effectiveness of the improved algorithm, a real car data test was conducted, and the test results showed that the improved method can effectively improve the detection effect of pedestrians in low light conditions.

Feasibility and acute alerting effects of a daylight-supplementing in-vehicle lighting system – Results from two randomised controlled field studies during dawn and dusk

The present studies examined the feasibility and acute alerting effects of additional in-vehicle lighting within a passenger car. These factors were examined during morning driving (Study 1) and evening driving (Study 2). In a balanced within-subjects design, 37 participants drove a test car two times in the morning or in the evening. The test vehicle was equipped with either a daylight-supplementing interior lighting system or a placebo system, which participants were told would refresh the air. Both studies used identical protocols, and participants participated either in Study 1 ( n = 18) or Study 2 ( n = 19). In both studies, corneal illuminance levels were recorded while driving. Feasibility of the systems was assessed using subjective ratings. Efficacy outcomes were spindle rates in the alpha bandwidth of electroencephalogram recordings, performance on a psychomotor vigilance task and subjective sleepiness ratings. In both studies, daylight-supplementing significantly increased corneal illuminances while driving and did not cause any negative visual side-effects. Study 1 revealed lower spindle rates while driving under daylight-supplementing lighting, indicating that drivers had higher physiological alertness when exposed to additional light in the morning. This alerting effect of daylight-supplementing lighting, however, was not observed in Study 2. In both studies, performance on the psychomotor vigilance task as well as subjective sleepiness ratings did not significantly differ between the experimental conditions. The present studies provide novel evidence for the feasibility and positive impact of daylight-supplementing in-vehicle lighting systems on the physiological alertness of drivers under naturalistic driving conditions. Further studies are warranted to evaluate carry-over effects of increased alertness on road safety measures.

Modelling and experimental evaluation of a variable stiffness MR suspension with self-powering capability

This paper presents the modelling and experimental evaluation of a semi-active vehicle suspension installed with a self-powered MR damper which is able to perform variable stiffness. Its variable stiffness feature as well as the self-powering capability was evaluated and verified using a hydraulic Instron test system. The testing results show that the stiffness of the damper is dependent on the current which can be generated by the self-powering component. A mathematic model was established to describe the dynamic properties of the MR damper and its power-generating capability. Finally, the self-powered MR suspension was installed on a quarter car test rig for its vibration isolation evaluation. A controller based on the short-time Fourier transform (STFT) was developed for the stiffness control. The evaluation result illustrates that the proposed MR damper can reduce the acceleration and displacement of the sprung mass by 16.8% and 21.4% respectively, compared with the passive system.

Modeling human-like longitudinal driver model for intelligent vehicles based on reinforcement learning

The driver model is the decision-making and control center of intelligent vehicle. In order to improve the adaptability of intelligent vehicles under complex driving conditions, and simulate the manipulation characteristics of the skilled driver under the driver-vehicle-road closed-loop system, a kind of human-like longitudinal driver model for intelligent vehicles based on reinforcement learning is proposed. This paper builds the lateral driver model for intelligent vehicles based on optimal preview control theory. Then, the control correction link of longitudinal driver model is established to calculate the throttle opening or brake pedal travel for the desired longitudinal acceleration. Moreover, the reinforcement learning agents for longitudinal driver model is parallel trained by comprehensive evaluation index and skilled driver data. Lastly, training performance and scenarios verification between the simulation experiment and the real car test are performed to verify the effectiveness of the reinforcement learning based longitudinal driver model. The results show that the proposed human-like longitudinal driver model based on reinforcement learning can help intelligent vehicles effectively imitate the speed control behavior of the skilled driver in various path-following scenarios.

Health Monitoring of Automotive Suspensions: A LSTM Network Approach

In the automotive industry, one of the critical issues is to develop a health monitoring system for condition assessment and remaining fatigue life estimation of key load‐bearing components including automotive suspension. However, considering the difficulty to obtain expert knowledge and nonlinear dynamics in large‐scale sensory data, health monitoring of automotive suspension is a challenging work. With the development of deep learning based sequence models in recent years, a long short‐term memory (LSTM) network has been proved to capture long‐term dependencies in time‐series prediction without additional expert knowledge. In this paper, a novel health monitoring system based on a LSTM network is proposed to estimate the remaining fatigue life of automotive suspension. Specifically, first durability tests under various driving cycles are implemented to obtain sequential sensory data provided by common sensors on a test car. Then, a LSTM‐based load identification method is designed to predict dynamic stress histories based on the available sensory data. Finally, the damages and remaining fatigue life of the suspensions are estimated by each time step. The experimental results prove that our model can achieve a better performance compared with other representative models.

Analytical determination of application point and normal reaction arm when rolling a wheel on a drum

Evaluation of the rolling resistance of car tires is now often performed on drum stands like car tests. This necessitates the study of the mechanics of interaction between the wheel and the drum in order to determine its force and kinematic characteristics, including the values and points of application of tangential and normal forces in contact with the drum. These problems can be solved taking into account that the mechanics of elastic wheel rolling on a drum is the same as when rolling on a flat rigid support surface. In this paper, from consideration of the mechanics of interaction between an elastic wheel and a drum, using the equations of power balance and force equilibrium of the wheel, the equations for determining the point of normal reaction in contact and its arm relative to the wheel axis during its rolling along one and two drums have been derived.. These dependencies have a simple form and can be applied when considering the rolling of both a single wheel and the car as a whole on a drum stand.

Synthesis and biological evaluation of a new class of multi-target heterocycle piperazine derivatives as potential antipsychotics.

In this study, we designed and synthesized a novel series of multi-receptor ligands as polypharmacological antipsychotic agents by using a multi-receptor affinity strategy. Among them, 3w combines a multi-receptor mechanism with high mixed affinities for D2, 5-HT1A, 5-HT2A and H3 receptors, and low efficacy at the off-target receptors (5-HT2C, H1 and α1 receptor) and human ether-à-go-go-related gene (hERG) channel. In addition, compound 3w exhibits favorable antipsychotic drug-like activities in in vivo assessment. An animal behavioral study revealed that compound 3w significantly reverses apomorphine-induced climbing and MK-801-induced hyperactivity, and avoidance behavior in the CAR test, with a high threshold for catalepsy. Moreover, compound 3w demonstrates memory enhancement in a novel object recognition task and low liabilities for weight gain and hyperprolactinemia in a long-term metabolic adverse effects model. Thus, 3w was selected as an antipsychotic candidate for further development.

Dynamic load test and lateral load distribution of a modified rigid frame bridge

In order to evaluate the stress state and working performance of a rigid frame bridge after reconstruction and extension, load test and transverse load distribution were carried out. Static load test is the test of stress at each section of main beam under the action of partial load and medium load. Dynamic load test is used to test the inherent fundamental frequency, damping ratio and impact coefficient of the bridge through pulsation test and sports car test. The results show that the first order vibration of the bridge is mainly transverse vibration from the measured modal parameters, which is consistent with the characteristics of higher pier and greater flexibility. The vibration characteristics of the bridge structure are low frequency and small damping vibration, the value of which belongs to the normal range among similar bridge structures, the overall stiffness of the bridge structure is normal, and the measured impact coefficient during the test of sports car is less than the design impact coefficient, indicating that the dynamic stiffness of the bridge meets the design and specification requirements.

Car Parking Distance Controller Using Ultrasonic Sensors Based On Arduino Uno

The design of a car parking sensor is done because there are some problems such as the absence of parking attendants from 18:00 to 06:00 and the lack of parking attendants so the researchers took the title of arduino uno-based car parking distance controller by using ultrasonic sensors in this study using 3 main components, namely Arduino UNO, Arduino MP3 Shield and Ultrasonic HC-SR04. Arduino Shield MP3 is a module that is used to play sound. MP3 which can give voice commands that have been programmed before using Arduino IDE and combined with Ultrasonic HC-SR04 components in the calculation of the distance to the object. The distance (cm) obtained as input is then processed and then combined with the Arduino Shield MP3, then at a predetermined distance will output a predetermined voice command. Then the method used is the prototype method which starts from designing, writing the system, implementing it with the aim of making a prototype of a car parking sensor and testing it with a prototype. Conclusion testing the car sensor sensor car makes it easy for car drivers to park the car.