Self-driving Research Articles

Applying Simultaneous Localization and Mapping (SLAM), Simulation, and Surveys to Planning Wheelchair-Accessible Homes

This paper explores the use of technology in assessing wheelchair accessibility in existing and newly built homes. Our investigation was divided into two parts. The first part applied simultaneous localization and mapping (SLAM) and computer simulation technologies to existing homes to evaluate them for wheelchair accessibility. The second part considered the possibility of converting new houses in New Taipei City into accessible houses for wheelchair users, specifically focusing on kitchen design as a test case. Tasks in the first part of this study include the autonomous robot car, computer-aided simulation, SLAM, and field testing. The goal was to evaluate the possibility of employing robotic technologies for helping modify existing homes in order to make them more accessible by a wheelchair. The investigation started with LiDAR integration into an inexpensive autonomous robot car by designing and assembling hardware as well as integrating software. Then SLAM was employed to map the floor plan of a home, while the software for simulating a robot was used for modeling a wheelchair movement inside the house. Finally, field tests were conducted in the actual home environment and the methodology was analyzed. In the second part of the investigation, 137 floor plans of new homes in New Taipei City were analyzed and classified into eleven types. The study used the chi-square test to assess which home types are most suited for accessible-kitchen modification, and no specific type was found to be particularly suited for this purpose. The study indicated that only 30.7% of the kitchens could be adapted to become wheelchair-friendly. However, 69.3% of kitchens in new homes were discovered to be inappropriate for adapting to accommodate wheelchair accessibility. The identified problems included a stretched kitchen layout, a narrow space, undersized kitchen, poor traffic, and kitchen located near entrance. The study showed that these methods can help wheelchair users who are planning to make their homes wheelchair-accessible.

Eye Controlled Electric Wheelchair: Proof of Concept Using an Arduino Robotic Car

This research aims to develop an electric wheelchair that can be controlledby using eye gestures, providing an intuitive and accessible interface for individuals with mobility impairments. The eye gesture is captured with the help of a camera and the live image is then preprocessed and fed to a CNN-based model for classification. The gesture is classified into 4 classes, namely, forward, left, right, and stop. Based on the classification, a signal is sent to the wheelchair through Bluetooth and the control system of the wheelchair operates as per the instruction. For the model training, the datasethas been collectedwith image frames of eye movement representing different control commands. The dataset is then trained using a ResNet-18-based CNN model. The model is then deployed on a mobile device which takes the image of the user’s eyes from the camera and inferences the image to find the eye movement. The movement is then recognized, and appropriate control signals are generated and transmitted to the wheelchair through Bluetooth. The receiver in the wheelchair maps the transmitted signal to the specific movement and turns the actuators in the appropriate directions.Hence, the wheelchair can interpret eye movement captured by the camera, accurately recognize pupil movement, and translate them into control signals mapped from the predicted values given by the model, ultimately empowering users with improved mobility and independence. On testing the system under various lighting conditions and with different users, the control system showed 90.25% accuracy and the overall movement of the system showed an excellent result in following the user's eye movement in real-time. But when tested on very low light conditions, especially during night time, the system cannot perform as expected and often predicts random and false values.

Does AI based techniques in self driving cars help improve safety?

This brief investigation cognition self-driving cars are considered the future of transportation where the safety of the vehicles is of great significance as they become more prominent in society. This article goes deep in discussing the approaches to safety assessment of autonomous systems and the focus is made on the concept of black-box safety validation algorithms. These algorithms are based on probabilistic models of system operation. Therefore, they estimate a broad spectrum of possible failure situations for analyzing the system’s reliability, as opposed to the white-box approaches which are usually more time-consuming. However, the usage of such black-box approaches has its drawbacks concerning the guarantee of safe operations. To meet these challenges, the article discusses compositional validation techniques which involve testing of the components of the system to help come up with a comprehensive safety validation. In future development, black-box simulations combined with compositional validation may serve as potential approaches for improving the rigorous safety requirements that are vital to the viability of AVs. These ongoing pieces of study and developments in these fields are expected to increase the robustness and credibility of self-driving technologies in transportation.

Five user types of autonomous driving in Hungary

Abstract One of the most socially impactful innovations of the near future will be the proliferation of self-driving vehicles, which will have a major impact not only on the passengers in the vehicle but on all road users and even on society as a whole, transforming cityscapes. This study aims to contribute to the social acceptance of self-driving vehicles. As society is not unified in its attitude towards self-driving vehicles, the authors believe that successful social acceptance requires different messages to be delivered to different types of consumers. This research segmented consumers based on their acceptance of self-driving technology, thereby providing a basis for targeted communication in the future. Cluster analyses were used on a sample of 517 Hungarian consumers to identify five segments based on attitudes towards self-driving vehicles. The analysis identified five distinct segments of consumers: (1) tradition-loving dismissers, (2) open-minded adventurers, (3) uncertain optimists, (4) distrustful sceptics, and (5) abstentious observers. These segments can be targeted with differentiated communication strategies. This paper contributes to the literature on self-driving technology acceptance by providing a detailed segmentation of the consumer market, highlighting the importance of targeted communication to enhance technology adoption. It offers a novel approach by focusing on specific consumer segments rather than society in general. By identifying the needs and characteristics of different consumer segments, marketers can develop more effective communication strategies to promote the acceptance of self-driving technology. Using a more targeted marketing approach instead of mass-marketing may result in a smoother spread of innovation and maximise social welfare benefits from technological advancements.

Review of PID Control in Industrial Areas for Tobacco Production, Self-driving Car Technology and Robotic Arm Robots

Abstract. PID control is widely employed across diverse industries such as tobacco production, autonomous car technology, and mechanical arm robotics. This shows that PID control has a substantial developmental potential, so I choose the PID control as the topic. Since few people summarize its different applications in different industries, this study has the motivation to comprehensively summarize the applications of PID. This research provides an in-depth overview and analysis of PID control's varied applications in tobacco production, autonomous car technology, and mechanical arm robotics. It focuses on addressing practical challenges and exploring optimization strategies encountered in these fields. This article is summarized using the methods of the review. PID plays a crucial role in enhancing stability, precision, and response speed in these applications, thereby improving overall control system dynamic response performance. This study consolidated existing literature to offer insights into PID's effectiveness across these industrial sectors, shedding light on its impact and potential for further advancements in control technology.

Advanced obstacle recognition and trajectory prediction for autonomous vehicles using 3D CNN and GRU

Abstract. Recent years have seen a rapid advancement in new energy vehicle technology, which has increased consumer demand for autonomous driving systems. However, current self-driving cars still face many technical barriers. Obstacle detection and obstacle trajectory prediction in dynamic environments is one of the main difficulties of the technique. To improve self-driving cars' capacity for route planning and decision-making in dynamic contexts, this thesis proposes an obstacle identification and trajectory prediction model that combines a gated recurrent unit (GRU) network with a three-dimensional convolutional neural network (3D CNN). The integrated framework utilises the feature recognition capability of 3D CNN and the prediction capability of GRU to accurately predict dynamic obstacles, thereby improving the performance of the model in various types of environments (e.g., visual SLAM). Experimental results based on publicly available datasets (e.g., KITTI and Argoverse) show that the proposed method has significant advantages in dynamic obstacle identification and prediction as well as overall model operation efficiency. The findings show that the integrated framework is useful and successful in advancing the creation of self-driving automobiles.

Neuro Controlled Object Retrieval System

In this time of digitization and computerization, the life of individuals is getting more straightforward as nearly everything is programmed. This interconnection of the things can be used to help people with physical disabilities including paralysis. The brain signals of such people can be harnessed to create an object retrieval system. In this work, the concentration levels of such individuals are extracted using the EEG signals. These signals are then used to control the electronic devices. The designed system has been tested to control robotic car and arm using brain signals. These systems enable users to control external devices through neural signals, presenting significant potential in healthcare, robotics, and human-computer interaction. This paper reviews the development and implementation of a Neuro Controlled Object Retrieval System, which allows a user to control a robotic arm using brainwave patterns to pick up and retrieve objects. The system leverages Electroencephalography (EEG) signals to interpret neural activity, processed using machine learning algorithms to translate brain commands into physical actions. The objective of this review is to explore the existing methodologies, technologies, and challenges associated with such systems. The paper also discusses the potential applications, future advancements, and the significance of this technology in enhancing assistive robotics and rehabilitation tools.

승용차 반자율주행 기능의 사용자 경험에 대한 조사 연구

승용차 반자율주행 기능의 사용자 경험에 대한 조사 연구

Autonomous and Electric Vehicles in Urban Living Labs: Smart Mobility Strategies for the Future

The deployment of electric, connected, and autonomous vehicles on public roads presents a significant challenge that can be addressed through previously established frameworks developed globally for implementing these technologies as part of an urban living lab (ULL). This systematic review, based on records from four distinct databases, focuses on projects that have conducted deployments of self-driving technologies in streets within urban environments. The review describes relevant information about various initiatives, including a classification of the stages of development reached according to the urban area covered, safety considerations, and lessons learned for optimal deployment. On-board sensing technology, digital infrastructure, and energy and communication systems emerge as the essential components of a ULL with autonomous vehicles (AVs). A crucial goal for smart cities is ensuring the scalability of large-scale deployments of such ULLs for safe, clean, and future mobility experimentation. This can only be achieved through effective coordination among academia, government, industry, and society to guarantee the successful integration of multiple projects in a unique environment.

Investigating Electromagnetic Interference Induced Risks for Autonomous Driving

Abstract. The results of immunity tests on components of self-driving automobiles to radiated high power electromagnetic (HPEM) pulses are presented in this work. It is of particular interest to investigate such automobiles' resilience towards deliberate attacks with electromagnetic interference (EMI). Different types of HPEM capabilities at the Bundeswehr Research Institute for Protective Technologies and CBRN Protection (WIS) in Munster are applied for this investigation. Two types of automotive components have been tested, a two-axis acceleration sensor and an electronic power steering unit. Statistics of errors from the devices under test (DUTs), as well as a characterization of the applied pulses are presented. A correlation can be drawn between the pulse repetition rate or the amplitude of the applied pulses and the severity of the effect on the DUTs.

Optimizing Business Operations through Edge Computing: Advancements in Real-Time Data Processing for the Big Data Era

The question of big data brought new opportunities and threats to business environment primarily in terms of processing increasingly large amounts of data in realtime. This paper examines the way in which edge computing can /should benefit business processes by offering improvements to real-time data analysis beyond what is provided by cloud-based systems while overcoming the challenge of latency, bandwidth and scalability. The study uses both primary and secondary data collected from 50 companies that use edge computing for processing of big data in industries including manufacturing, health, and retail. It involves cases where the factors are manifested, practices involving changes in numerical values of operations and other models involving the management of real-time data. Studies show that edge computing cuts down data processing delay by 40-60% on average, cuts data utilization and improves decision-making. This work also examines the use-cases of edge computing in order to demonstrate its potential across industries that must analyze data in near realtime, including self-driving automobiles, IoT gadgets, and logistics. Toward this end, this research provides an analysis of the ethical issues of decentralized data handling especially focusing on data security and data privacy. In response to the research questions of the paper, this study provides practical recommendations to help the companies in pursuing edge computing in the big data environment for the sake of sustaining market competitiveness. The research enriches the existing knowledge in business technology by presenting the possibility of change that edge computing brings.

Analyzing the Impact of Technological Innovation: A Case of Tesla's Self-driving Technology

Against the backdrop of rapid global economic development, autonomous driving technology, as a revolutionary change, has attracted a large number of investors and corporate enterprises to invest and explore. This paper studies Tesla's R&D investment in Autopilot technology and its impact on market competitiveness between 2017 and 2019. By analyzing data from Tesla's official website and other reliable sources, the study finds that Tesla's high R&D investment and data-driven approach have significantly improved the safety and reliability of its Autopilot technology. For example, Tesla's Autopilot system enables a highly automated driving experience through multiple sensors and powerful computing capabilities. The results show that Tesla's leadership in Autopilot technology has greatly enhanced its market competitiveness and driven the industry as a whole. The study's conclusion suggests that Tesla's technological innovation not only improves the company's competitiveness but also drives changes in the mobility ecosystem, which has a positive impact on Tesla's development.

Green Giants: Competition and Climate Monopoly

This article provides a glimpse of how climate monopolists might emerge, consolidate economic power, and call forth the need for nimble procompetitive tweaks in dynamic (but not dynamic enough) climate markets. It tracks an under-recognized source of U.S. EV-sector sluggishness: the purportedly market-galvanizing performance of Tesla. Tesla’s dual dominance in both EV sales and high-speed EV charging means that prospective EV purchasers have had little alternative to Tesla’s luxury models. Moreover, polling data suggest that Tesla has foisted self-driving technologies on an unreceptive yet captive EV consumer base, generating unrivaled data streams which reinforce the firm’s market power.

Wall Follower Based Fire Extinguisher Wheeled Robot Prototype using 5 Channel Fire Sensor

Fires can occur anywhere, be it in schools, offices, and housing or other open spaces. Fire cannot be predicted when it will happen. In the modern era robots are one of the very special technologies, the benefits of these robots can facilitate human work. Robot cars in this era are attracting quite a lot of attention from experts to develop. Therefore, the author designed a "Wall Follower-based Fire Extinguishing Robot using a 5 Channel fire sensor" to reduce the risk of fire. This robot uses the HC-SR04 sensor to detect the barrier wall so that the extinguishing robot reaches the fire point. For the microcontroller tool used in this design system in the form of an Arduino Nano microcontroller which functions as part of the 5 Channel Flame Sensor controller to detect hotspots and a DC water pump to extinguish the fire. In this study using experimental research. This robot has successfully passed the track and put out the fire as the author has designed. The average flame sensor on this robot can read the fire point at a distance of 20 cm with angles of 90°, 60°, 120°, 150°, then the output voltage generated on the DC motor with a PWM value of 191 or 75% duty cycle produces an output of 8.77V measured using a multimeter, and this robot can extinguish the fire on the track with different distances with an average fire extinguishing time of 7.1 seconds.

Integrated evaluation of the functioning quality of robotic agricultural vehicle system

The article proposes to use a method for evaluating the effectiveness (Data Envelope Analysis) of a system of robotic agricultural vehicles, which is based on solving the problem of fractional linear programming when analyzing a set of homogeneous production facilities. This method is used for an integrated assessment of the quality of service of robotic vehicles according to the specified key parameters of functioning. The main parameters of the effectiveness of the system of robotic agricultural vehicles are selected: the coefficient of technical readiness, operational speed, downtime in maintenance and repair, the average daily duration of the car, the average monthly mileage of the vehicle, the remaining life of the car, the unit cost of maintenance and specific cargo displacement. Solving systems of linear equations makes it possible to determine the position of each analyzed object relative to the efficiency boundary in a multidimensional parameter space. The solution of the Bunker-Charnes-Cooper problem has been completed. Sets of target changes in the parameters of robotic cars have been obtained, which transfer objects to the efficiency boundary. The analysis of the parameters is carried out by constructing diagrams of production capabilities in a multidimensional space. For two periods of operation of the robotic vehicle system for an agro-industrial enterprise, the Malmquist index was calculated to assess the dynamics of operational efficiency.

Automatic Optimal Robotic Base Placement for Collaborative Industrial Robotic Car Painting

This paper investigates the problem of optimal base placement in collaborative robotic car painting. The objective of this problem is to find the optimal fixed base positions of a collection of given articulated robotic arms on the factory floor/ceiling such that the possibility of vehicle paint coverage is maximized while the possibility of robot collision avoidance is minimized. Leveraging the inherent two-dimensional geometric features of robotic car painting, we construct two types of cost functions that formally capture the notions of paint coverage maximization and collision avoidance minimization. Using these cost functions, we formulate a multi-objective optimization problem, which can be readily solved using any standard multi-objective optimizer. Our resulting optimal base placement algorithm decouples base placement from motion/trajectory planning. In particular, our computationally efficient algorithm does not require any information from motion/trajectory planners a priori or during base placement computations. Rather, it offers a hierarchical solution in the sense that its generated results can be utilized within already available robotic painting motion/trajectory planners. Our proposed solution’s effectiveness is demonstrated through simulation results of multiple industrial robotic arms collaboratively painting a Ford F-150 truck.

Design and Implementation of a Robotic Car with a ULV Atomizer for Mosquito Control in Urban Parks

Mosquito infestations in tropical regions, particularly during months of moderate to high rainfall, pose significant health risks due to diseases like Malaria, Dengue, and Chikungunya. Traditional control methods such as fumigation and chemical sprays face limitations of environmental concerns and potential health risks to humans. To address this issue in community parks, I developed a robotic car equipped with an atomizer to disperse an organic mosquito repellent. Powered by a Raspberry Pi 4 and brushless DC motors, the robotic vehicle is designed for stable and efficient movement across uneven park terrains. It is controlled via an integrated PlayStation 4 controller for user-friendly operation. The atomizer disperses a repellent from essential oils such as Neem (Azadirachta indica), Lemongrass, and Eucalyptus, offering an environmentally friendly and human-safe alternative to chemical repellents like DEET. Testing indicated that the robotic car equipped with an atomizer effectively reduces mosquito presence, enhancing the safety and enjoyment of outdoor recreational spaces.

A Survey of Image Enhancement and Dehazing Algorithms to Improving Autonomous Navigation in Fog and Dust.

Video captured during sand-dust weather conditions indicates significant degradation, diminished contrast, and pronounced color distortion. The reason behind this phenomenon is the impact of sand-dust particles, which result in the dispersion and assimilating of light. Consequently, resultant image seems blurred and lacks contrast. Additionally, the alteration in color is attributed to the swift reduction of blue light. Adverse weather circumstances, such as fog, sand, and dust storms, cause problems with vision for deploying self-driving automobiles. To guarantee secure and seamless operations during frequent adverse weather conditions, video enhancement plays a vital role in facilitating vehicular mobility and navigational in both road and air transportation. To improve the safety and efficiency of operation, it is imperative to employ video dehazing techniques, particularly in adverse weather conditions. The purpose of our overview is to offer interested researchers a thorough and methodical examination of image enhancement approaches, serving as an important point of reference for their work. The text explored different image enhancement methods, along with the challenges, constraints, advantages, and drawbacks associated with using these techniques throughout the past five years. Specifically, it focused on three aspects: supervised algorithms, unsupervised algorithms, and quality evaluation.

Distributed Learning in Intelligent Transportation Systems: A Survey

The development of artificial intelligence (AI) and self-driving technology is expected to enhance intelligent transportation systems (ITSs) by improving road safety and mobility, increasing traffic flow, and reducing vehicle emissions in the near future. In an ITS, each autonomous vehicle acts as a node with its own local machine learning models, which can be updated using locally collected data. However, for autonomous vehicles to learn effective models, they must be able to learn from data sources provided by other vehicles and infrastructure, utilizing innovative learning methods to adapt to various autonomous driving scenarios. Distributed learning plays a crucial role in implementing these learning tasks in an ITS. This review provides a systematic overview of distributed learning in the field of ITSs. Within an ITS, vehicles can engage in distributed learning by interacting with peers through opportunistic encounters and clustering. This study examines the challenges associated with distributed learning, focusing on issues related to privacy and security in data intelligence sharing, communication quality and speed, and trust. Through a thorough analysis of these challenges, this study presents potential research avenues to address these issues, including the utilization of incentive mechanisms that rely on reputation, the adoption of rapid convergence techniques, and the integration of opportunistic federated learning with blockchain technology.

Arduino-Controlled Multi-Function Robot with Bluetooth and nRF24L01+ Communication

This paper outlines the design and development of an advanced robotic system that integrates hardware implementation with theoretical simulation to address the need for versatile and user-friendly robotic solutions in various environments. Addressing the issue of limited adaptability in existing robotic systems, we propose a wireless, voice and gesture-controlled robot car with an integrated robotic arm capable of performing complex tasks such as line following, obstacle avoidance, object manipulation, and autonomous navigation over one-kilometer range. To improve operational efficiency and user involvement, this paper designs a multifunctional robotic platform that integrates user-friendly control interfaces with inexpensive, state-of-the-art sensor technologies. To achieve this, we integrate a variety of sensors, including ultrasonic sensors for precise distance measurement, infrared sensors for object detection and line following, an L298 motor driver for controlling geared motors, servo motors for controlling robotic arms, a flex sensor for claw control, and an mpu6050 accelerometer for gesture recognition. The system also uses a custom-made Bluetooth app for remote control, nRF24L01+ for long-range wireless control, and Arduino Mega and Nano for processing and control functions. The results demonstrate the robot functions well in dynamic conditions, and it can be used in hospitals to assist healthcare professionals, in restaurants for food delivery, and in industrial settings for object manipulation. The system’s design proves robust in real-world scenarios, offering significant improvements in accessibility and operational efficiency. This study aligns with Sustainable Development Goals (SDGs) 3 (Good Health and Well-being), 9 (Industry, Innovation, and Infrastructure), and 17 (Partnerships for the Goals). The robotic arm's potential application in healthcare settings advances SDG 3, its contribution to industrial productivity advances SDG 9, and collaborations with tech companies to expand and improve the robot's capabilities promote SDG 17.