Automated Car Research Articles

Development of an Automatic Scissors Screw Car Jack

In this paper, an existing manual scissors screw car jack was converted to an automatic scissors screw car jack by introducing a sleeve coupling between the manual screw jack and a 12V DC electric motor, which is powered through a direct current energy flow directly from the car battery. The DC electric motor provides a turning effect to automatically power the screw jack for the purpose of lifting the desired load. A screw jack (manual or automatic) is a simple machine used in lifting heavy loads. It becomes very useful in vehicles when changing a punctured tire. It is mandatory for each vehicle plying an approved road to carry a jack for fixing a punctured tire. However, the operation of most car jacks is done manually and also requires prolonged bending or squatting positions when using the jack, making it difficult for most women, the disabled, the elderly, etc., and above all, not ergonomically suitable for the human body due to crouching and squatting positions when in use, which may result in health complications. The automatic scissors car jack developed was successfully tested on five different types of vehicles, with different weights. Five different tests measurements were carried out on each vehicle. The time taken to lift the vehicles 0.03 m off the ground was recorded. They ranged from 74 s to 90 s depending on the weight of the vehicle involved. The heavier the vehicle, the longer the time taken to lift the vehicle off the ground to the predetermined height of 0.03 m.

Artificial Intelligence Techniques in Automated Vehicle Systems: A Comprehensive Review

Artificial intelligence (AI) has changed the way automatic car systems work, making them safer, more efficient, and able to drive themselves. This in-depth review looks at the many different AI methods that are used to create and improve automatic car systems. The main focus of this study is on machine learning methods, such as supervised, unsupervised, and reinforcement learning, and how they can be used to help vehicles understand, make decisions, and be controlled. Supervised learning, especially deep learning, is a key part of finding and classifying objects, which is needed to tell the difference between people, cars, and road signs. Many people use Convolutional Neural Networks (CNNs) because they are very good at handling images and videos accurately, which makes real-time responses easier in changing driving situations. Unsupervised learning methods, like grouping and anomaly detection, make systems more reliable by finding strange trends and behaviors. This makes it easier to know what's going on and plan for future maintenance. Vehicles can learn from interacting with their surroundings thanks to reinforcement learning, which is a key part of improving decision-making. This method is very important for planning routes, adaptive speed control, and avoiding collisions, which makes sure that self-driving cars can handle complicated situations safely and quickly. Also talked about are sensor fusion methods that combine data from LiDAR, radar, and video to give a more complete picture of the surroundings and provide extra information. The review also talks about the moral issues and legal problems that come up with AI-driven self-driving cars. To get a full picture of the field, things like computer openness, data protection, and the moral effects of making choices in tough scenarios are looked at. This review highlights the changing potential of AI in automated car systems by putting together the latest research and developments. It also gives us a look into the directions and improvements that will come next. The goal of this study is to be a useful resource for students, practitioners, and lawmakers who are interested in how autonomous driving technologies are changing over time.

Aggregation operators based on Einstein averaging under q-spherical fuzzy rough sets and their applications in navigation systems for automatic cars

This study introduces innovative operational laws, Einstein operations, and novel aggregation algorithms tailored for handling q-spherical fuzzy rough data. The research article presents three newly designed arithmetic averaging operators: q-spherical fuzzy rough Einstein weighted averaging, q-spherical fuzzy rough Einstein ordered weighted averaging, and q-spherical fuzzy rough Einstein hybrid weighted averaging. These operators are meticulously crafted to enhance precision and accuracy in arithmetic averaging. By thoroughly examining their characteristics and interrelations with existing aggregate operators, the article uncovers their distinct advantages and innovative contributions to the field. Furthermore, the study illustrates the actual implementation of these newly constructed operators in a variety of attribute decision-making scenarios employing q-SFR data, yielding useful insights. Our suite of decision-making tools, including these operators, is specifically designed to address complex and uncertain data. To validate our approach, this study offers a numerical example showcasing the real-world applicability of the proposed operators. The results not only corroborate the efficacy of the proposed method but also underscore its potential significance in practical decision-making processes dealing with intricate and ambiguous data. Additionally, comparative and sensitivity analyses are presented to assess the effectiveness and robustness of our proposed work relative to other approaches. The acquired knowledge enriches the current understanding and opens new avenues for future research.

Towards Enhanced Autonomous Driving Takeovers: Fuzzy Logic Perspective for Predicting Situational Awareness

This paper investigates the application of fuzzy logic to enhance situational awareness in Advanced Driver Assistance Systems (ADAS). Situational awareness is critical for drivers to respond appropriately to dynamic driving scenarios. As car automation increases, monitoring situational awareness ensures that drivers can effectively take control of the vehicle when needed. Our study explores whether fuzzy logic can accurately assess situational awareness using a set of 14 critical predictors categorized into time decision, criticality, eye-related metrics, and driver experience. We based our work on prior research that used machine learning (ML) models to achieve high accuracy. Our proposed fuzzy logic system aims to match the predictive accuracy of ML models while providing additional benefits in terms of interpretability and robustness. This approach emphasizes a fresh perspective on situational awareness within ADAS, potentially improving safety and efficiency in real-world driving scenarios.

Evaluation of the use of activated carbon derived from coconut shells to treat car wash wastewaters

Car wash services generate wastewater that consists of many contaminants. The discharge of raw car wash wastewater would bring adverse impacts to the environment. Therefore, this study aims to evaluate the use of activated carbon derived from coconut shells to treat car wash wastewater. In this study, activated carbon (AC) prepared from coconut shells shows a good microporosity structure with functional groups on the surface. The performance of activated carbon prepared is evaluated by determining the removal efficiency of the parameter including COD, BOD, and heavy metals concentration (zinc and iron) from car wash wastewater. The operating parameters are dosage (0.2 - 1.0 g/100 mL), contact time of 30 min-150 min and pH adjustment (pH 3 - pH 9). Throughout the research, the highest removal efficiencies of COD, BOD, zinc, and iron from automatic car wash wastewater are 91.03 %, 88.51 %, 68.25 %, and 86.09 % respectively achieved by 0.8 g of carbon dosage under 90 rpm agitation speed for 60 min. Under the same operational condition, CSAC removed 84.43 % COD, 81.80 % BOD, 59.78 % zinc, and 78.58 % iron from manual car wash wastewater. Therefore, the removal of pollutants from car wash water by using activated carbon derived from coconut shells has been proven to be an effective method.

Car Washing System using raspberry Pi Pico as PLC

Abstract: Using the Raspberry Pi Pico microcontroller, we have designed and implemented an automatic vehicle wash system in this work. By automating steps including scrubbing, rinsing, drying, and applying soap, the system seeks to expedite the automobile washing process. We go over the Raspberry Pi Pico, actuators, and sensor hardware configuration in addition to the software architecture that controls the system. We also emphasize the advantages of utilizing Raspberry Pi Pico for this application, such as its affordability, portability, and versatility. The efficacy and dependability of the suggested automatic car wash system are shown by the experimental results.

Assessing public perception of car automation in Iran: Acceptance and willingness to pay for adaptive cruise control

Adaptive Cruise Control (ACC) is a technology that can reduce traffic. However, its availability in Iran is relatively limited compared to more developed countries. This research examines the acceptance and willingness to pay for ACC among Iranian drivers. Data from an online survey of 453 respondents were analyzed using an ordered logit model and a structural equation model. The results of modelings show that perceived ease of use and perceived usefulness affect attitudes towards using ACC, which in turn influence behavioural intentions. In addition, drivers who find ACC easy to use and user-friendly, own expensive vehicles, and female drivers who have experience with cruise control are more likely to pay for ACC. To enhance the adoption of ACC in Iran, it is recommended to target early adopters, especially women and capitalists, who can influence others with their positive feedback. The benefits of ACC for traffic safety and environmental sustainability should also be emphasized. Furthermore, future studies would better concentrate on public perceptions and assessing the necessary infrastructure for ACC in Iran.

Solving Parking Problems with Micro Controller Smart Car Parking Systems

The aim of this paper is to provide a simple automatic car parking allocation system with basic components like microcontroller that provides solution to the problems in car parking allocation. Parking allocations provided in shopping complexes, malls, multi-store buildings etc usually have persons allocated to supervise manually the traffic and to allocate spaces available for parking, directing vehicles to be parked without any disturbances making the process more complex. A simple parking automation system with IR sensors provided at the parking space to detect the presence of vehicle parked, LED notification board to show specific empty parking slots and a display to direct individuals can avoid trafficking at the gateways of parking slots and helps them to park their vehicles easily. Therefore, the proposed research work designs and implements a prototype system model, which will regulate trafficking in parking garages along with providing information to the drivers about the availability of spaces.

Anthropomorphic Design and Self-Reported Behavioral Trust: The Case of a Virtual Assistant in a Highly Automated Car

The latest advances in car automation present new challenges in vehicle–driver interactions. Indeed, acceptance and adoption of high levels of automation (when full control of the driving task is given to the automated system) are conditioned by human factors such as user trust. In this work, we study the impact of anthropomorphic design on user trust in the context of a highly automated car. A virtual assistant was designed using two levels of anthropomorphic design: “voice-only” and “voice with visual appearance”. The visual appearance was a three-dimensional model, integrated as a hologram in the cockpit of a driving simulator. In a driving simulator study, we compared the three interfaces: two versions of the virtual assistant interface and the baseline interface with no anthropomorphic attributes. We measured trust versus perceived anthropomorphism. We also studied the evolution of trust throughout a range of driving scenarios. We finally analyzed participants’ reaction time to takeover request events. We found a significant correlation between perceived anthropomorphism and trust. However, the three interfaces tested did not significantly differentiate in terms of perceived anthropomorphism while trust converged over time across all our measurements. Finally, we found that the anthropomorphic assistant positively impacts reaction time for one takeover request scenario. We discuss methodological issues and implication for design and further research.

Design of Sensor-Based Semi-Automatic Car Wash System Prototype and it’s Gear Box Conveyor Calculation

The rapid and widespread development of science and technology (IPTEK). Particularly in the automotive industry has encouraged the development of the automotive industry, especially in four-wheeled vehicles or also known as cars. Given the number of vehicles that are still quite a lot, maintenance, care and repair must be carried out for the smooth running of the car's work process so that the car's work process runs smoothly. One of them is the system of car washing. In the process of washing a car, the uses of human power (manual) still predominant. This automatic car wash system is the perfect solution to save time and effort. Based on this, a designed small-scale sensor based semi-automatic car wash miniature was made. Supporting components are needed so that the automatic car wash system can work according to the desired design, including Ultrasonic sensors, DC motors, dynamo motors, Water Pumps, 12V fans. The main objective of this simulation is the development of criteria to design and build a car wash of this type-high real way to solve the challenges of automation. The research results show that the proposed method is in process control not only increases productivity, speed, accuracy and safety, but also reduces the time and cost of washing dynamic vehicle models.

Assistive Self-Driving Car Networks to Provide Safe Road Ecosystems for Disabled Road Users

Disabled pedestrians are among the most vulnerable groups in road traffic. Using technology to assist this vulnerable group could be instrumental in reducing the mobility challenges they face daily. On the one hand, the automotive industry is focusing its efforts on car automation. On the other hand, in recent years, assistive technology has been promoted as a tool for consolidating the functional independence of people with disabilities. However, the success of these technologies depends on how well they help self-driving cars interact with disabled pedestrians. This paper proposes an architecture to facilitate interaction between disabled pedestrians and self-driving cars based on deep learning and 802.11p wireless technology. Through the application of assistive technology, we can locate the pedestrian with a disability within the road traffic ecosystem, and we define a set of functionalities for the identification of hand gestures of people with disabilities. These functions enable pedestrians with disabilities to express their intentions, improving their confidence and safety level in tasks within the road ecosystem, such as crossing the street.

The Effectiveness of Using Continuous Variable Transmission (CVT) in 2WD Buggy Vehicles

Continuous Variable Transmission (CVT) is a transmission system that does not have gears like in manual transmission cars or conventional automatic transmission cars. CVT has the advantage of being able to maintain the proper rotational speed at any time in terms of engine efficiency by changing the speed ratio flexibly and continuously. A continuously variable transmission (CVT) is a transmission that can change steplessly through an infinite effective gear ratio between maximum and minimum. This is in contrast to other mechanical transmissions which only allow a few different gear ratios to choose. The flexibility of the CVT allows the drive shaft to maintain a constant angular speed over the output speed range. Torque is transmitted from the drive to the pulley which is driven by friction acting between the belt and pulley surfaces. Tests were carried out using a dynotest to obtain wheel rotation data and the resulting torque. The loading is given from 3 kg, 5 kg, 7 kg and 8 kg, the engine rpm used is 5500 rpm, 6000 rpm, 6500 rpm, 7000 rpm and 8000 rpm. Rear wheel rotation is inversely proportional to the load, the greater the load the rotation will decrease. Load is directly proportional to torque, the greater the load given the torque will increase. The CVT system is still effective for use as a power successor. The power that can be transmitted is still greater than 50%, namely 68%.

Perbandingan Kinerja 4 Mesin ATF Charger untuk Proses Flashing pada Transmisi Toyota Yaris Matic

ABSTRACT
 This research is an experiment testing several Automatic Transmission Fluid (ATF) charger machines in the Flushing process during oil changes by comparing several ATF machines with the Toyota Yaris automatic transmission hydraulic pump.
 By knowing the performance of each of these tools, we can recommend a better and more efficient use for automatic car maintenance. The purpose of this study was to compare the ATF Charger Lounch Cat-501s, ATF Charger brand X1, ATF Charger brand X2 with a Toyota Yaris automatic transmission hydraulic pump. By knowing the performance of each of these tools, we can recommend a better and more efficient use for automatic car maintenance. besides that it also aims to find out the maintenance of the automatic transmission on the Toyota Yaris automatic transmission hydraulic pump. The method in this study uses experimental methods by conducting literature studies, sample preparation, group division, treatment implementation, measurement and observation and measurement of the speed of oil entering the transmission during flashing, data analysis, data interpretation, and report generation. The data collected includes efficiency, output power, generated pressure, costs incurred including maintenance costs. In addition, testing was also carried out to evaluate the performance of the Toyota Yaris automatic transmission hydraulic pump when using each type of ATF Charger machine during Flushing. The results showed that the ATF Brand X1 engine had the fastest performance in the process of changing the engine oil for the transmission of the Toyota Yaris automatic car, which was able to drain 1000 ml in 17.65 seconds, the speed was around 850 rpm (revolutions per minute), with an electric voltage of 12 Volts, as well as the cost of procuring the lowest equipment, which is IDR 1,807,530
 
 Keywords: ATF Charger, Toyota Yaris, Matic

Research on a New Method of Track Turnout Identification Based on Improved Yolov5s

The modern tram track automatic cleaning car is a crucial equipment in urban rail transportation systems, effectively removing trash, dust, and other debris from the slotted tracks of trams. However, due to the complex and variable structure of turnouts, the cleaning car often requires assistance in accurately detecting their positions. Consequently, the cleaning car needs help in adequately cleaning or bypassing turnouts, which adversely affects cleaning effectiveness and track maintenance quality. This paper presents a novel method for tracking turnout identification called PBE-YOLO based on the improved yolov5s framework. The algorithm enhances yolov5s by optimizing the lightweight backbone network, improving feature fusion methods, and optimizing the regression loss function. The proposed method is trained using a dataset of track turnouts collected through field shots on modern tram lines. Comparative experiments are conducted to analyze the performance of the improved lightweight backbone network, as well as performance comparisons and ablation experiments for the new turnout identification method. Experimental results demonstrate that the proposed PBE-YOLO method achieves a 52.71% reduction in model parameters, a 4.60% increase in mAP@0.5(%), and a 3.27% improvement in precision compared to traditional yolov5s. By improving the track turnout identification method, this paper enables the automatic cleaning car to identify turnouts’ positions accurately. This enhancement leads to several benefits, including increased automation levels, improved cleaning efficiency and quality, reduced reliance on manual intervention, and mitigation of collision risks between the cleaning car and turnouts.

CarDD: A New Dataset for Vision-Based Car Damage Detection

Automatic car damage detection has attracted significant attention in the car insurance business. However, due to the lack of high-quality and publicly available datasets, we can hardly learn a feasible model for car damage detection. To this end, we contribute with Car Damage Detection (CarDD), the first public large-scale dataset designed for vision-based car damage detection and segmentation. Our CarDD contains 4,000 high-resolution car damage images with over 9,000 well-annotated instances of six damage categories. We detail the image collection, selection, and annotation processes, and present a statistical dataset analysis. Furthermore, we conduct extensive experiments on CarDD with state-of-the-art deep methods for different tasks and provide comprehensive analyses to highlight the specialty of car damage detection. CarDD dataset and the source code are available at https://cardd-ustc.github.io.

Vehicle door frame positioning method for binocular vision robots based on improved YOLOv4

In the task of using robots to capture end-of-life cars, the position of the vehicle door frame needs to be grasped. A fast and accurate positioning of the vehicle door frame is the key to realizing the automatic car grasping process. Traditional methods for locating and grasping scrap cars rely heavily on manual operations and suffer from low grasping efficiency and poor accuracy. Therefore, this paper proposes a binocular vision robot vehicle door frame spatial localization method based on the improved YOLOv4. This method includes a lightweight and efficient feature fusion target detection network in complex environments, and the target detection results are combined with an enhanced SURF feature–matching method to locate the vehicle door frame position. To simplify the network structure, MobileNetv3 is used instead of the backbone network CSPDarknet53, and deep separable convolution is used in the network. To increase the sensitivity of the network to vehicle door frame targets in complex environments, an improved convolutional block attention module is added to the pyramid attention with simple network backbones. Moreover, adaptive spatial feature fusion is introduced into the network to fully use the features at different scales for more effective feature fusion. Compared with YOLOv4, the number of network parameters is reduced by 73.8%, the mAP is improved by 1.35%, and the detection speed is increased by 28.7%. The experimental results demonstrate that the positioning accuracy of the system is 0.745 mm, which meets the positioning measurement error of less than 1 cm required for the vehicle door frame. The paper also compares our findings with other network models. The results show that the method achieves a good balance between detection speed and detection accuracy, satisfying the task of identifying vehicle door frames in complex environments with good detection results.

Embedded Deep Learning System for Classification of Car Make and Model

Automatic car make, and model classification is essential to support activities of intelligent traffic systems in urban areas, such as surveillance, traffic information collection, statistics, etc. In order to classify this data, we need an embedded system approach for real-time car recognition. Many approaches could be made, from image processing to machine learning. Recently, the development of the Convolutional Neural Network has spurred various research in the Area. ResNet, Inception, DenseNet, and NasNet are some of the most commonly used Neural Network based method that is used to classify images. In this research, these Neural Network methods are going to be compared in classifying vehicle make and model in the Stanford dataset. The dataset contains 196 different labels. Several evaluation metrics are used to compare the performance of the methods. From the experiment, the InceptionV3 method achieved the best performance of the AUROC ratio for training the dataset under 50 epochs. Other methods that achieve a high AUROC value tends to have a higher computational time. Real-time simulations have shown that the embedded system is capable of classifying a 100 % success rate for six concurrent users.

Should my automated car drive as I do? Investigating speed preferences of drivengers in various driving conditions.

Studies investigating the question of how automated cars (ACs) should drive converge to show that a personalized automated driving-style, i.e., mimicking the driving-style of the human behind the wheel, has a positive influence on various aspects of his experience (e.g., comfort). However, few studies have investigated the fact that these benefits might vary with respect to driver-related variables, such as trust in ACs, and contextual variables of the driving activity, such as weather conditions. Additionally, the context of intermediate levels of automation, such as SAE level 3, remains largely unexplored. The objective of this study was to investigate these points. In a scenario-based experimental protocol, participants were exposed to written scenarios in which a character is driven by a SAE level 3 AC in different combinations of conditions (i.e., types of roads, weather conditions and traffic congestion levels). For each condition, participants were asked to indicate how fast they would prefer their AC to drive and how fast they would manually drive in the same situation. Through analyses of variance and equivalence tests, results showed a tendency for participants to overall prefer a slightly lower AC speed than their own. However, a linear regression analysis showed that while participants with the lowest levels of trust preferred an AC speed lower than theirs, those with the highest levels preferred an AC speed nearly identical to theirs. Overall, the results of this study suggest that it would be more beneficial to implement a personalization approach for the design of automated driving-styles rather than a one for all approach.

Detecting Driver Sleepiness using Convolutional Neural Networks

The development in computer vision has aided drivers in the form of automatic self-driving cars etc. The accidents are caused by driver's exhaustion and drowsiness about 20%. Its carriages a dangerous issue for which numerous methods were proposed. However, they are not appropriate for real-time implementation. The major encounters confronted by these approaches are forcefulness to handle dissimilarity in human face and lightning conditions. Our intention is to implement a smart operating system that can lower the rate of road accidents considerably. This method enables us to find driver's face features like eye closure percentage, eye-mouth aspect ratios, blink rate, yawning, head movement, etc. In this classification, the driver is uninterruptedly observed by using a webcam. The car driver’s facial features along with the eye movements are observed using a cascade classifier. Eye images are pull out and fed to Custom designed Convolutional Neural Network for categorizing whether both left and right eye are closed. Based on the sorting, the eye closure score is considered. Upon finding that the driver is being detected drowsy that a high alarm will be raised.

Design and implementation of automatic sorting car based on OpenMV

The system uses the OpenMV algorithm module combined with the k-means framework through the Euclidean distance formula to facilitate the automation of logistics sorting, which identifies the color of logistics parcels in the LAB color space. It sends the identified color information to the motion drive module, which sorts and transports the logistics parcels to the designated storage area. The fusion of algorithms allows the system to identify colors and transportation accurately.