Development Of Self-driving Cars Research Articles

Automatic Identification of Emergency Sounds for Self-Driving Cars

As the development of Self-Driving Cars (SDCs) advances, one important feature that requires attention is their ability to sense and respond to emergencies on the road. Drivers of emergency vehicles prioritize speed over safety during emergencies to save lives, potentially leading to accidents. However, manual cars sometimes may not understand the urgency of the situation, further increasing the risk of collisions. In such cases, self-driving cars offer a better solution to replace manual cars and minimize road accidents. These cars with emergency sound detections offer a level of responsiveness, accuracy, and consistency that surpasses manual cars, contributing to improved road safety and efficiency in emergencies. Therefore, the key objective of the research is to improve the listening capability of self-driving cars to detect emergency vehicles with the help of a hybrid feature extraction technique. The suggested technique leverages a combination of Complex Morlet Wavelet and Co-occurrence Matrix to obtain statistical features from the emergency sounds. The proposed technique can work with the input length of 1.2 seconds of raw waveforms. This research work investigates that self-driving cars can accurately identify emergency vehicles by examining the distinctive emergency sound patterns emitted by the emergency vehicle with the highest accuracy of 94%. At the same time, the proposed technique reduces the computational cost by 20 – 40 milliseconds when compared with other techniques. The result of this work not only provides better accuracy but also reduces detection time, which is a crucial requirement for real-time applications such as self-driving cars.

THE RESPONSIBILITY ASSESSMENT IN THE SCENARIO OF DRIVING AI-BASED VEHICLES

Artificial intelligence (AI) machines can process data independently, but they need human assistance to make decisions and control their behaviour. As AI technology progresses, it becomes difficult for humans to predict the outcome of calculations and inferences, which increases the potential risks associated with its operation. The introduction of AI has revolutionised the automotive industry, especially with the development of self-driving cars. While they offer convenient transportation, accidents may occur, and it's necessary to distinguish whether the AI system has autonomy or not, and in the scenario of an accident, it may be controversial who should be held accountable. Consequently, this article aims to explore the direction of future legislative policy in the event of an accident involving AI with a focus on self-driving cars in the automotive industry. In this regard, the authors propose a solution proportionally to the behaviour assessment of the user and developers respectively, and risk control measures during the development stage underscoring careful planning and management in real-world testing of unmanned vehicles for the integration of AI into daily life. Furthermore, it highlights the uncertainties about appropriate laws to regulate the phenomenon of self-driving vehicles and suggests the importance of complying not only with laws and regulations but also with ethical development and use.

Predicting Physical World Destinations for Commands Given to Self-Driving Cars

In recent years, we have seen significant steps taken in the development of self-driving cars. Multiple companies are starting to roll out impressive systems that work in a variety of settings. These systems can sometimes give the impression that full self-driving is just around the corner and that we would soon build cars without even a steering wheel. The increase in the level of autonomy and control given to an AI provides an opportunity for new modes of human-vehicle interaction. However, surveys have shown that giving more control to an AI in self-driving cars is accompanied by a degree of uneasiness by passengers. In an attempt to alleviate this issue, recent works have taken a natural language-oriented approach by allowing the passenger to give commands that refer to specific objects in the visual scene. Nevertheless, this is only half the task as the car should also understand the physical destination of the command, which is what we focus on in this paper. We propose an extension in which we annotate the 3D destination that the car needs to reach after executing the given command and evaluate multiple different baselines on predicting this destination location. Additionally, we introduce a model that outperforms the prior works adapted for this particular setting.

A Hierarchical Approach for Traffic Sign Recognition Based on Shape Detection and Image Classification.

In recent years, the development of self-driving cars and their inclusion in our daily life has rapidly transformed from an idea into a reality. One of the main issues that autonomous vehicles must face is the problem of traffic sign detection and recognition. Most works focusing on this problem utilize a two-phase approach. However, a fast-moving car has to quickly detect the sign as seen by humans and recognize the image it contains. In this paper, we chose to utilize two different solutions to solve tasks of detection and classification separately and compare the results of our method with a novel state-of-the-art detector, YOLOv5. Our approach utilizes the Mask R-CNN deep learning model in the first phase, which aims to detect traffic signs based on their shapes. The second phase uses the Xception model for the task of traffic sign classification. The dataset used in this work is a manually collected dataset of 11,074 Taiwanese traffic signs collected using mobile phone cameras and a GoPro camera mounted inside a car. It consists of 23 classes divided into 3 subclasses based on their shape. The conducted experiments utilized both versions of the dataset, class-based and shape-based. The experimental result shows that the precision, recall and mAP can be significantly improved for our proposed approach.

Automotive Vulnerability Analysis for Deep Learning Blockchain Consensus Algorithm

In this study, future cars are attempting self-driving around the world. However, hacking, such as ECUs in automobiles, creates problems that are directly connected to human life. Therefore, this study wrote a paper that detects anomalies in such cars by field. As a related study, the study investigated the vulnerabilities of the automobile security committee and automobile security standards and investigated the detection of abnormalities in the hacking of geo-train cars using artificial intelligence’s LSTM and blockchain consensus algorithm. In addition, in automobile security, an algorithm was studied to predict normal and abnormal values using LSTM-based anomaly detection techniques on the premise that automobile communication networks are largely divided into internal and external networks. In the methodology, LSTM’s pure propagation malicious code detection technique was used, and it worked with an artificial intelligence consensus algorithm to increase security. In addition, Unity ML conducted an experiment by constructing a virtual environment using the Beta version. The LSTM blockchain consensus node network was composed of 50,000 processes to compare performance. For the first time, 100 Grouped Tx, 500 Channels were tested for performance. For the first time, the malicious code detection rate of the existing system was verified. Accelerator, Multichannel, Sharding, Raiden, Plasma, and Trubit values were verified, and values of approximately 15,000 to 50,000 were obtained. In this paper, we studied to become a paper of great significance on hacking that threatens human life with the development of self-driving cars in the future.

Neural networks based on the 1С:enterprise 8.3 platform in data analysis systems for self-driving cars

The development of self-driving cars in the modern world is accelerating every year, and it is obvious that the safe movement of such cars is impossible without special fault-tolerant software tools. With the improvement of technology, such software tools increasingly include elements of artificial intelligence. Currently, on the basis of flexible mechanisms of the 1C:Enterprise 8.3 platform, an application solution is being developed that allows to analyze the behavior of an self-driving car on a public road. Such software can be used in the educational activities of students in areas related to mathematical statistics, as well as to study mathematical methods that optimize the operation of the on-board computer of a driving self-driving car. Considering the growth of educational programs, which include the study of applied solutions and the development of such solutions on the 1C:Enterprise 8.3 platform, the use of the software in question in the educational process is available to students, useful and interesting for them. The presence of a large number of reports using the data layout system will allow to analyze the movement of self-driving cars in precisely those sections that are necessary for a student or researcher to conduct research activities.

Improved Safety of Self-Driving Car using Voice Recognition through CNN

Deployment of Artificial Intelligence (AI) in daily life will show a new way to the future, the complex task will become easy. One of such evolution of AI technology is the creation of the Self-Driving cars. But according to the statistics, there are some cases where the Self-Driving cars killed people due to the failure in the prediction or hardware problem. So, to overcome those kinds of situations the AI models should be accurate and also humans should take part in making them perfect. For example, when there are some passengers in a self-driving car and the car has lost its control due to the fault in the hardware of the computer installed in the car then an authorized person in the car should be able to instruct it using voice commands to control the car at that moment. The main focus of this paper relies on the development of Self-Driving Cars using the Convolutional Neural Networks (CNN), drawbacks and solutions for the drawbacks. The Self-Driving Car CNN model was trained using the Asphalt-8 game data and the Voice command prediction CNN model was trained with 3 different persons (1-Kid, 1-Man, 1-Woman) voices. The accuracy of both the CNN models were 99% and was tested on the same game where they have produced their best results.

Towards a stage model of regional industrial path transformation

ABSTRACT The recent debate on innovation-based structural change in Evolutionary Economic Geography is characterised by a strong focus on the rise of new industrial paths. This paper seeks to shift attention and cast light on radical innovation activities occurring within existing paths without necessarily leading to their dissolution. Departing from a systemic perspective of path development we propose a stage model of path transformation. We outline how radical change becomes initiated, reinforced and finally consolidated in established industrial paths. Particular attention is devoted to the ways in which actors – influenced by ‘the past’ and driven by visions and expectations (that is, ‘the future’) – exert agency to stimulate asset modification processes that are assumed to underpin path transformation and the reconfiguration of the wider support structures. The framework is applied to the analysis of the automotive industry in West Sweden, which is currently transforming towards the development of self-driving cars.

The road towards autonomous driving – A differentiated view of institutional agency in path transformation

ABSTRACT The purpose of the article is to contribute to conceptual and empirical understandings of institutional agency in path transformation. Previous studies of links between institutional change and industrial transformation have focused mainly on the institutionalization of new practices and influence of territorially defined institutional preconditions, leaving a need to disentangle different types of institutional agency and the rationales behind actors’ activities. The author elaborates on different dimensions of the institutional environment in which path transformation occurs and proposes a new analytical framework for investigating the role of institutional agency in path development. The framework identifies different types of institutional agency, respectively targeting the ‘legitimation’, ‘anchoring’ and ‘enabling’ of new paths. It is applied to a case study of the automotive industry in the NUTS2 region West Sweden, based on the development of self-driving cars. The analysis reveals that actors utilized the relationship between existing institutions when formulating strategies, rather than primarily targeting institutional change. The author concludes that actors deploy a combination of different types of institutional agency that exhibit varying spatial patterns, and discusses the implications for how the relationship between the past, present and future is understood in path development research.

It’s Time to Rethink Levels of Automation for Self-Driving Vehicles

One of the major ways in which the development of self-driving cars has been discussed - levels of automation - is misleading. A typology originally developed to provide some engineering clarity now benefits technology developers far more than it serves the public interest. As social researchers who have spent time working with and speaking to the developers of this technology, we believe the levels of automation need a rethink.

Fundamentals and development of self-driving cars

This paper represents detailed information on the constitutional and development of self-driving cars. For a decade, in the automotive industry, there are lots of malfunctions are performed. It makes many issues like accidents, driver’s liability and much more. These kinds of problems arise when human interfaces with the car. In real-time Self-driving cars that are driven by digital technologies without using a human interruption. Based on the fundamentals of developing self-driving cars are totally by sensing their environment and automating the tasks. In our proposed system the Localization, Perception, Prediction, Planning, and Control are to make define and governing the car, certain algorithms are used to control the autonomous system and are used for steering functions. The autonomous car can predict and cruise its path and traffic signs as like as pedestrians. It can minimize accidents, fuel rates, and parking space.

From the testing to the deployment of self-driving cars: Legal challenges to policymakers on the road ahead

Self-driving cars and self-driving technology are tested on public roads in several countries on a large scale. With this development not only technical, but also legal questions arise. This article will give a brief overview of the legal developments in multiple jurisdictions – California (USA), United Kingdom, and the Netherlands – and will highlight several legal questions regarding the testing and deployment of self-driving cars.Policymakers are confronted with the question how the testing of self-driving cars can be regulated. The discussed jurisdictions all choose a different approach. Different legal instruments – binding regulation, non-binding regulation, granting exemptions – are used to regulate the testing of self-driving cars. Are these instruments suitable for the objectives the jurisdictions want to achieve?As technology matures, self-driving cars will at some point become available to the general public. Regarding this post-testing phase, two pressing problems arise: how to deal with the absence of a human driver and how does this affect liability and insurance? The Vienna Convention on Road Traffic 1968 and the Geneva Convention on Road Traffic 1949, as well as national traffic laws, are based on the notion that only a human can drive a car. To what extent a different interpretation of the term ‘driver’ in traffic laws and international Conventions can accommodate the deployment of self-driving cars without a human driver present will be discussed in this article.When the self-driving car becomes reality, current liability regimes can fall short. Liability for car accidents might shift from the driver or owner to the manufacturer of the car. This could have a negative effect on the development of self-driving cars. In this context, it will also be discussed to what extent insurance can affect this development.

In-vehicle technology for self-driving cars: Advantages and challenges for aging drivers

The development of self-driving cars or autonomous vehicles has progressed at an unanticipated pace. Ironically, the driver or the driver-vehicle interaction is a largely neglected factor in the development of enabling technologies for autonomous vehicles. Therefore, this paper discusses the advantages and challenges faced by aging drivers with reference to in-vehicle technology for self-driving cars, on the basis of findings of recent studies. We summarize age-related characteristics of sensory, motor, and cognitive functions on the basis of extensive age-related research, which can provide a familiar to better aging drivers. Furthermore, we discuss some key aspects that need to be considered, such as familar to learnability, acceptance, and net effectiveness of new in-vehicle technology, as addressed in relevant studies. In addition, we present research-based examples on aging drivers and advanced technology, including a holistic approach that is being developed by MIT AgeLab, advanced navigation systems, and health monitoring systems. This paper anticipates many questions that may arise owing to the interaction of autonomous technologies with an older driver population. We expect the results of our study to be a foundation for further developments toward the consideration of needs of aging drivers while designing self-driving vehicles.

Lakatos, laudan and the hermeneutic circle

Self-driving cars and self-driving technology are tested on public roads in several countries on a large scale. With this development not only technical, but also legal questions arise. This article will give a brief overview of the legal developments in multiple jurisdictions – California (USA), United Kingdom, and the Netherlands – and will highlight several legal questions regarding the testing and deployment of self-driving cars.Policymakers are confronted with the question how the testing of self-driving cars can be regulated. The discussed jurisdictions all choose a different approach. Different legal instruments – binding regulation, non-binding regulation, granting exemptions – are used to regulate the testing of self-driving cars. Are these instruments suitable for the objectives the jurisdictions want to achieve?As technology matures, self-driving cars will at some point become available to the general public. Regarding this post-testing phase, two pressing problems arise: how to deal with the absence of a human driver and how does this affect liability and insurance? The Vienna Convention on Road Traffic 1968 and the Geneva Convention on Road Traffic 1949, as well as national traffic laws, are based on the notion that only a human can drive a car. To what extent a different interpretation of the term ‘driver’ in traffic laws and international Conventions can accommodate the deployment of self-driving cars without a human driver present will be discussed in this article.When the self-driving car becomes reality, current liability regimes can fall short. Liability for car accidents might shift from the driver or owner to the manufacturer of the car. This could have a negative effect on the development of self-driving cars. In this context, it will also be discussed to what extent insurance can affect this development.