Self-driving Research Articles

Design of Predictive Control System for Lane Change in Autonomous Vehicle

The automobile business is introducing a lot of autonomous vehicles in the modern day. Lane changes are one of the most complicated urban scenarios in which autonomous vehicles are used. Self-driving automobiles must thus interact with human-driven vehicles in a certain way. In this work, we concentrate on the autonomous vehicle's lane-changing control system for obstacle avoidance. This study employs a predictive control system as its methodology. The vehicle's next movements can be predicted by this control system. The vehicle's position, which is adjusted by the steering angle, is the controllable variable. The vehicle's position, which is adjusted by the steering angle, is the controllable variable. It is clear from the numerical simulation results that the predictive control system executes control actions on lane changes correctly, avoiding collisions with the running vehicle obstacles. RMSE (Root-Mean Square Error) is a performance metric that is derived from the difference between the vehicle's lateral position and the reference trajectory value. The RMSE of the planned predictive control is 0.9681.

Fine-Grained Multi-class Road Segmentation using MultiScale Probability Learning

Abstract: Driven road segmentation, a crucial part of advanced driver assistance systems, looks at the surroundings to keep vehicles within safe driving limits (ADASs). It begins by outlining the automatic lane detection shortfalls of conventional computer vision systems, pointing out problems such subpar segmentation, insufficient mask edge contours, sluggish processing, and restricted flexibility in intricate urban environments. Next, a multi-step procedure using deep learning networks is offered as a solution. This involves extracting vector skeletons, computing neighbouring pixels, assigning proportional weights depending on endpoints, and getting binary prediction masks. The conversation also touches on how self-driving technology will affect society, emphasizing how it could provide safe and intelligent transportation choices in the face of an increase in traffic accidents caused by careless drivers. Self-driving cars could be the first practical example of socially conscious robots interacting with humans, the story implies, even if it acknowledges possible public opposition. Furthermore, the story highlights the latest developments in autonomous driving technology, highlighting the vital requirement for strong sensing, perception, and cognitive technologies to enable completely autonomous vehicles that can adjust to changing road conditions.

Systematic development of an autonomous robotic car for fire-fighting based on the interactive design approach

Systematic development of an autonomous robotic car for fire-fighting based on the interactive design approach

A Holistic Approach to Cybersecurity Integration in Autonomous Vehicles

The breakthrough of the self-driving car technology is a double-edged sword that gives us incredible opportunities and at the same time, it is the source of huge cybersecurity challenges. This paper champions the creation of a complete legal system and worldwide standards, like the ISO 26262, to regulate different parts of the autonomous vehicle (AV) operations, such as safety testing, liability, data privacy, and cybersecurity. By highlighting the significance of cybersecurity from the very start, the stakeholders and the automakers have to incorporate the practices like secure programming, routine updates, intrusion detection systems and encryption methods which will be the effective shield against the unauthorized access and data breaches. People should be informed and educated about the cybersecurity risks of AVs so that they can take the steps to protect themselves. Nevertheless, the integration of AVs with the Internet of Things (IoT) and other devices makes the cybersecurity landscape even more complicated, which means that there is a constant need for research and development to deal with problems like adversarial attacks. Presently, the deep learning-driven AV systems do not have the explainability which complicates the safety-critical applications, hence the need to increase the resilience training and deepen the deep learning models against attacks. The paper goes through all the literature on AV cybersecurity which stresses the need for functional safety standards and cybersecurity engineering guidelines. Collaboration between the governments, the industry, and the researchers is a must to set the legal foundations and the technical details for the AV cybersecurity across all levels of automation. Finally, solid cybersecurity is the key to the success of the autonomous vehicle revolution, hence the road to transformative safety, efficiency, and sustainability is paved. INDEX TERMS: Autonomous vehicles, Cybersecurity, ISO 26262, Encryption methods, ISO 21434, EU regulations, Security and Privacy.

From Perception to Prediction: Leveraging Explainable AI in Self-Driving Cars for Enhanced Passenger Trust

Self-driving cars hold immense potential for revolutionizing transportation. However, public acceptance hinges on trust in the car's ability to navigate safely and make critical decisions. This trust deficit stems from the "black box" nature of traditional machine learning models used in self-driving cars. Passengers are left in the dark about the car's perception of the environment and the reasoning behind its actions. This research proposes leveraging Explainable Artificial Intelligence (XAI) techniques to enhance passenger trust in self-driving cars. By incorporating explainability into the perception and prediction modules of the car's decision- making system, we aim to provide passengers with real-time insights into how the car perceives its surroundings and translates those perceptions into driving decisions. This paper explores various XAI methods suitable for self-driving car applications. We discuss the integration of these techniques into the perception and prediction pipelines, enabling the car to explain its reasoning behind lane changes, obstacle avoidance maneuvers, and other critical actions. We evaluate the effectiveness of the proposed approach through user studies, assessing how explainability can improve passenger trust and comfort in self-driving vehicles. The ultimate goal of this research is to foster greater transparency and trust in self-driving car technology, paving the way for wider public adoption and a future of safe and reliable autonomous transportation.

Enhancing Agricultural Efficiency through Robotic Systems and AI Integration

Robotic systems have progressed and grown more available in recent years. They have been gradually but steadily integrated and used in several procedures and industries, particularly farming, as a consequence. Nowadays, agriculture machines are being developed to take the place of humans in labor-intensive, thing or hazardous activities. Depending on the kind of vehicle, its detectors, motors, and telecommunication networks, agro mobile robots provide a number of advantages. The most significant profession on the globe is gardening. The fundamental main objective of the task is to construct an automated multifunctional agricultural robot car. It is used to boost farming efficiency while decreasing the number of individuals who labor in fields and improving the accuracy of the task.

Is a robot car still a car? Consumer perceptions of fully automated vehicles and automobility in Canada

Fully automated vehicles (FAVs) could transform private car-based mobility or “automobility”, but the direction of FAV impacts is uncertain and contingent on consumers. We investigate consumer response to FAVs and its relationship to automobility by conducting semi-structured interviews with 34 new car buyers in British Columbia, Canada. First, we assess consumer response through exercises where participants design their “ideal next vehicle”, choosing between FAV versus conventional vehicle (CV) options. We find that two-thirds of participants prefer FAVs over CVs in a scenario where FAVs are available for sale and include a steering wheel. Second, we conduct qualitative content analysis of transcript data to investigate consumer engagement with automobility upon access to privately-owned and shared FAVs. We apply a conceptual framework of consumer “automobility engagement”, considering preferences for car ownership and residential location, car use emotions, symbolic and societal perceptions, and social norms. We find that minorities of participants expect to own fewer cars or change residential preferences following access to FAVs. Results also indicate that FAVs largely reproduce the symbolic and social significance of car ownership. Contrasting with these results, several participants expect FAVs to reduce automobility impacts on society at large. We conclude that FAV adoption may reproduce existing consumer engagement with automobility and discuss implications for transport emissions and policy.

ARUDINO BASED OBSTACLE AVOIDING ROBOT CAR P VAYUNANDA SAI KUMAR

This project involves the design and implementation of an intelligent obstacle-avoiding robot car. The objective of this project is to implement a robot car, which while moving should have the ability to detect obstacles in its path and change direction where obstacles are present without any form of external influence. The new direction to be taken to avoid collision is the direction that has the most distance between the obstacle and the sensor and this is determined by the robot based on sensor inputs. This implementation was done using an ultrasonic wave sensor, which measures distance by sending pulses. Also, the movement of the servo motor (for sensor movement) and the DC motors (for wheel movement) are controlled by the motor driver shield in order to enable the obstacle avoidance function. The commands are sent to the Arduino microcontroller chip which serves as the main control of the robot car, as it controls the sensor and car movement. The implemented robot car was able to successfully detect and avoid obstacles within the line of sight of the Ultrasonic sensor used. Keywords: Arduino UNO, ultrasonic sensor, DC motor, Servo Motor

Impact of Artificial Intelligence in Education Sector

Abstract: Building intelligent computers that can carry out tasks that traditionally require human intelligence is the goal of artificial intelligence (AI), a broad field of computer science. While there are many different approaches to AI, it is an interdisciplinary discipline, and recent developments in machine learning and deep learning in particular are causing a paradigm change in almost every area of the tech industry. Machines equipped with artificial intelligence are able to mimic or even outperform human brain functions. And as generative AI tools like ChatGPT and Google's Bard proliferate and selfdriving car technology advances, AI is quickly becoming a part of daily life and a field that businesses in every sector are investing in.

Высокотехнологичное автосборочное производство

In modern automotive production, high-tech robotic complexes and assembly lines are widely used for all four main production stages: pressing, welding, coating and assembly. The paper views industrial robot-assisted high-tech incomplete vehicle production using robotic manipulators. Studies have been conducted on the efficiency and productivity of automotive production through the use of a robotic assembly. Studies have been conducted on the efficiency and productivity of automotive production using a robotic assembly. A brief overview of scientific works on automation of production using robotic systems is presented. The discussed robotic complexes include various robots: assemblers, manipulators, auxiliary robots. The payback period of the robotic complex ranges from three to five years. Robotic complexes are successfully used in various factories, e.g. 115 robots are used at Volkswagen, 112 robots are used at Renault, 106 robots are used at Nissan. The average time period for assembling one basic vehicle product at the factory is 25 hours. The main characteristics of a robotic automobile assembly such as cycle of the assembly line; work pace; assembly line load factor are viewed. The influence of the main characteristics of the assembly line on productivity in robotic automotive production is estimated. The results show an increase in product output of up to 10% with an increase in the load factor by one tenth. The robotic assembly line efficiency calculation for automotive production shows that within five years the increase in net profit when using just one robotic assembly line will amount to about five million rubles. In addition, the output of products will significantly increase with RPA in automotive production. The effectiveness of using a robotized assembly line in automotive production is also shown.

Pose estimation in robotic electric vehicle plug-in charging tasks using auto-annotation and deep learning-based keypoint detector

The rapid growth of electric vehicles and advancements in self-driving technologies necessitate the development of specialized infrastructure to support autonomy. Accurate pose estimation of electric vehicles sockets is crucial for efficient and reliable plug-in charging operations. This process is inherently complex due to several factors, including the textureless black color of the socket, environment-dependent lighting conditions, and the presence of small geometrical features. To address these challenges, we propose a comprehensive method that combines Deep Neural Network-based pose estimation and auto-annotation methodology. Auto-annotation facilitates the generation of diverse training data, enhancing the accuracy, robustness, and generalization capabilities of the deep learning model. The advantages of the proposed method were evaluated through a comprehensive series of experiments conducted in both simulation and controlled laboratory settings. In our evaluation, we implemented a robotic charging system consisting of a manipulator with a charging plug and a hand-eye monocular camera and conducted plug-in testing in three scenarios: (A) uniform lighting conditions, (B) dark, and (C) highly uneven illumination of the socket surface. The experimental results show that our method can achieve precise and reliable pose estimation with mean absolute errors less than 0.73 mm and 0.82 deg, and an average Insertion Success Rate of over 97.5%.

Challenges as catalysts: how Waymo’s Open Dataset Challenges shape AI development

AbstractArtificial intelligence (AI) and machine learning (ML) are becoming increasingly significant areas of research for scholars in science and technology studies (STS) and media studies. In March 2020, Waymo, Google/Alphabet’s autonomous vehicle project, introduced the ‘Open Dataset Virtual Challenge’, an annual competition leveraging their Waymo Open Dataset. This freely accessible dataset comprises annotated autonomous vehicle data from their own Waymo vehicles. Yearly, Waymo has continued to host iterations of this challenge, inviting teams of computer scientists to tackle evolving machine learning and vision problems using Google's data and tools. This article analyses these challenges, situating them within the context of the ‘Grand Challenges’ of artificial intelligence (AI), which aimed to foster accountable and commercially viable advancements in the late 1980s. Through two exploratory workshops, we adopted a ‘technographic’ approach to examine the pivotal role of challenges in the development and political economy of AI. Serving as an organising principle for the AI innovation ecosystem, the challenge connects companies and external collaborators, driving advancements in specific machine vision domains. By exploring six key themes—interface methods, incrementalism, metrics, AI vernacular, applied domains, and competitive advantages—the article illustrates the role of these challenges in shaping AI research and development. By unpacking the dynamic interaction between data, computation, and labour, these challenges serve as catalysts propelling advancements towards self-driving technologies. The study reveals how challenges have historically and presently shaped the evolving landscape of self-driving and AI technologies.

Artificial Intelligence Based on Self Driving Cars with Safety Algorithm

The auto industry is going through a radical transformation with the introduction of self-driving cars, which offer safer and more effective transportation systems. An extensive analysis of the most recent developments in autonomous vehicle technology is given in this research report. It examines the different parts and mechanisms such as perception, decision-making, and control that are essential for autonomous functioning. The study also explores the benefits and problems that come with self-driving automobiles, including ethical issues, legal barriers, and public acceptance. This study contributes to the continuing discussion on the future of transportation by providing insightful information about the existing and potential states of autonomous cars through a synthesis of recent academic findings and industry advances. Keywords-- Self-driving Cars, Road Safety, Autonomous Vehicles, Decision Making, Safety Algorithm.

Voice Controlled Car

A system is being proposed, which focuses on the concept of how a car can be controlled by the human voice. Voice control car is just a practical example of controlling motions of a simple car by giving voice commands. In this system, an android app is used for the transmission of human commands to Arduino. An Arduino can be interfaced with the Bluetooth module through the UART protocol. The speech is received by the android app and processed by the voice module. Voice is then converted to text. The Arduino will further process this text, which will take suitable action to regulate the robot. The objective is to design a robotic car whose basic movements such as moving forward, turning to left or right can be controlled by the human voice

Foundations of Autonomous Vehicles: A Curriculum Model for Developing Competencies in Artificial Intelligence and the Internet of Things for Grades 7–10

A few states (e.g., Maryland, Georgia, and Florida) have initiated efforts to incorporate artificial intelligence outcomes in K-12 education but others are still relying on informal spaces for learning and literacy in this area. In this manuscript, we share the curriculum and content of an informal effort focused on students in grades 7-10. We combined artificial intelligence competencies with Internet of Things skills to enable meaningful learning covering all Five Big Ideas in AI. In our one-week summer camp, students experimented with perceptions by working with vision, infrared, and ultrasonic sensors. They learned about representation through work with neural network playgrounds. Students engaged in supervised learning of an image processing model and used the model to control the actions of a robot car. Natural interactions and societal impacts were assessed as students observed the robot car's behavior. Results demonstrate that our curriculum was successful in achieving its objectives. Excluding the robot car kit, the curriculum was created using free platforms and tools. This program could be replicated in informal settings by any educator or collaborator with a computer science background. This paper describes our summer camp curriculum, its components and their implementation, the lessons learned, and potential future enhancements.

LLM-Powered Synthetic Environments for Self-Driving Scenarios

This paper outlines a proposal exploring the potential use of Large Language Models (LLMs), particularly GPT-4, in crafting realistic synthetic environments for self-driving scenarios. The envisioned approach involves dynamic scene generation within game engines, leveraging LLMs to introduce challenging elements for autonomous vehicles. The proposed evaluation process outlines assessments such as realistic testing, safety metrics, and user interaction, aiming to set the stage for potential improvements in self-driving system performance. The paper aims to contribute to the AI field by discussing how LLMs could be utilized to create valuable testing grounds for autonomous vehicles, potentially fostering the development of more robust self-driving technology. The envisioned impact is the eventual enhancement of road safety and the possible acceleration of the adoption of autonomous vehicles, paving the way for a future with safer and more efficient transportation.

Fog Cloud Computing and IoT Integration for AI enabled Autonomous Systems in Robotics

Fog Cloud Computing and the Internet of Things are transforming robotics by empowering AI-enabled autonomous systems. This study analyzes the benefits, drawbacks, and uses of this integration. AI-enabled autonomous robots can use edge computing and cloud resources for real-time data processing and decision-making, improving their performance and adaptability. Communication protocols, data management, security, and scalability are examined in the ecosystem. Case studies reveal how this confluence affects robotics applications. This research shows how FCC, IoT, and AI may improve robotic systems' efficiency, intelligence, and autonomy. The article covers AI-enabled autonomous systems in transportation, manufacturing, healthcare, agriculture, and smart cities. These technologies can improve productivity and safety in many fields, from self-driving automobiles to surgical robots. Integrating these technologies raises safety, ethical decision-making, data privacy, and security concerns. The report emphasizes transparent and ethical AI algorithms, unbiased decision-making, and regulatory frameworks to enable responsible integration and mitigate dangers. In the future, AI-enabled autonomous systems will be shaped by improved AI algorithms, multi-modal sensing, human-robot collaboration, and edge intelligence. It emphasizes the necessity of interdisciplinary collaboration and ethical considerations in responsible technology development. This study concludes with a detailed analysis of fog/cloud computing, IoT, and AI in robotics, revealing the immense promise and problems of AI-enabled autonomous systems. Responsible development and collaboration can help us negotiate this transformational frontier and create a safer, more efficient, and innovative society with AI-driven autonomous systems.

A Study on Autonomous Vehicles - Advancements, Benefits & Challenges

Autonomous vehicles, often known as self-driving cars, are automobiles that can function without the need for a driver and rely on a variety of cutting-edge technologies to detect their environment and make judgments.Road traffic is projected to change because of autonomous driving, with accidents and congestion being the two main externalities that will be reduced. Autonomous cars are starting to become a reality as a result of recent, major advancements in processing and communication technologies. Companies that produce autonomous vehicles have invested a lot of money in developing technology to make it completely commercially viable. Adoption of autonomous vehicle technology has several advantages and drawbacks. These problems come in the form of technical, non-technical, and legal difficulties. This study paper offers a thorough examination of the developments, advantages, and difficulties related to autonomous cars. The study looks at how quickly self-driving technology is developing, what benefits it might have for society and the economy, and what complicated challenges need to be solved before it can be successfully incorporated into our transportation systems. This study presents a comprehensive picture of the present situation and potential futures for autonomous cars through a thorough analysis of the existing literature and case studies.

IOT Based All-in-One Robot (Agent-Eye)

Agent-Eye is a versatile project that involves creating a robotic system for surveillance or reconnaissance purposes. The key components typically include a mobile robot platform, camera(s), sensors for detecting mines in military, and a communication module It uses commands to move car in front, back and left right directions. Whatever is recorded by the camera can be viewed in Mobile/PC for reference. There is a metal detector sensor is used for detecting bomb/mines in war. The servo motor is used to rotate wheels of robot car. Using Arduino we implement the logic of move car in directions like forward, backward, right and left

Autonomous Systems Revolution: Exploring the Future of Self-Driving Technology

The rise of autonomous systems, particularly in the realm of self-driving technology, heralds a transformative era in transportation and beyond. This paper delves into the multifaceted landscape of autonomous systems, examining their evolution, current state, and future potential. By exploring the intricate workings of self-driving technology, we unravel the complexities and implications of this burgeoning field. From the underlying algorithms to the societal impacts, we navigate through the promises and challenges of autonomous systems. Through a comprehensive analysis, we shed light on the trajectory of self-driving technology and its role in shaping the future of transportation and beyond.