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

Totakura, Varun; Vuribindi, Bhargava Reddy; Reddy, E. Madhusudhana

doi:10.1088/1757-899x/1022/1/012079

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…Experimental results show that the proposed method improves the performance and safety of users from attackers. Totakura et al [55] focused on developing self-driving cars using convolutional neural networks and identifying and addressing potential drawbacks. The developed model for self-driving cars was trained using data from the Asphalt-8 game, while a separate convolutional neural network model for voice-command prediction was trained with the voices of a child, man, and woman.…”

Section: Related Workmentioning

confidence: 99%

Improving Autonomous Vehicle Controls and Quality Using Natural Language Processing-Based Input Recognition Model

Anjum

Shahab

2023

Sustainability

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In contemporary development, autonomous vehicles (AVs) have emerged as a potential solution for sustainable and smart transportation to fulfill the increasing mobility demands whilst alleviating the negative impacts on society, the economy, and the environment. AVs completely depend on a machine to perform driving tasks. Therefore, their quality and safety are critical concerns for driving users. AVs use advanced driver assistance systems (ADASs) that heavily rely on sensors and camera data. These data are processed to execute vehicle control functions for autonomous driving. Furthermore, AVs have a voice communication system (VCS) to interact with driving users to accomplish different hand-free functions. Some functions such as navigation, climate control, media and entertainment, communication, vehicle settings, vehicle status, and emergency assistance have been successfully incorporated into AVs using VCSs. Several researchers have also implemented vehicle control functions using voice commands through VCSs. If a situation occurs when AV has lost control due to malfunctioning or fault in the installed computer, sensors and other associated modules, driving users can control the AV using voice notes to perform some driving tasks such as changing speeds, lanes, breaking, and directing the car to reach a safe condition. Furthermore, driving users need manual control over AV to perform these tasks in some situations, like lane changing or taking an exit due to divergence. These tasks can also be performed with the help of voice commands using VCSs. Therefore, finding the exact voice note used to instruct different actuators in risk situations is crucial. As a result, VCSs can greatly improve safety in critical situations where manual intervention is necessary. AVs’ functions and quality can be significantly increased by integrating a VCS with an ADAS and developing an interactive ADAS. Now, the driver functions are controlled by voice features. Therefore, natural language processing is utilized to extract the features to determine the user’s requirements. The extracted features control the vehicle functions and support driving activities. The existing techniques consume high computation while predicting the user command and causing a reduction in the AVs’ functions. This research issue is overcome by applying the variation continuous input recognition model. The proposed approach utilizes the linear training process that resolves the listening and time-constrained problems and uncertain response issues. The proposed model categorizes the inputs into non-trainable and trainable data, according to the data readiness and listening span. Then, the non-distinguishable data were validated by dividing it into the linear inputs used to improve the response in the AVs. Thus, effectively utilizing training parameters and the data decomposition process minimizes the uncertainty and increases the response rate. The proposed model has significantly improved the exact prediction of users’ voice notes and computation efficiency. This improvement enhances the VCS quality and reliability used to perform hand-free and vehicle control functions. The reliability of these functions ultimately improves the safety of AVs’ driving users and other road users.

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Section: Related Workmentioning

confidence: 99%

Improving Autonomous Vehicle Controls and Quality Using Natural Language Processing-Based Input Recognition Model

Anjum

Shahab

2023

Sustainability

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“…Moreover, speaker identification facilitates exclusive access for authorized individuals. The relevance of robust speaker identification systems in autonomous vehicles has become even more apparent, especially with the alarming number of accidents and incidents involving self-driving cars [15]. However, they also emphasize how critical it is for speaker identification systems to be robust enough to enable seamless and reliable interactions between users and the autonomous vehicle.…”

Section: Human Monitors the Driving Environmentmentioning

confidence: 99%

Enhancing Security and Accountability in Autonomous Vehicles through Robust Speaker Identification and Blockchain-Based Event Recording

Njoku,

Nwakanma,

Lee

et al. 2023

Electronics

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As the deployment of Autonomous Vehicles (AVs) gains momentum, ensuring both security and accountability becomes paramount. This paper proposes a comprehensive approach to address these concerns. With the increasing importance of speaker identification, our first contribution lies in implementing a robust mechanism for identifying authorized users within AVs, enhancing security. To counter the threat of voice spoofing, an ensemble-based approach leveraging speaker verification techniques is presented, ensuring the authenticity of user commands. Furthermore, in scenarios of accidents involving AVs, the need for accurate accountability and liability allocation arises. To address this, we introduce a novel application of blockchain technology, enabling an event recording system that ensures transparent and tamper-proof records. The proposed system enhances AV security and establishes a framework for reliable accident investigation using speakers’ records. In addition, this paper presents an innovative concept where vehicles act as impartial judges during accidents, utilizing location-based identification. Results show the viability of the proposed solution for accident investigation and analysis.

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“…In the current research of the parallel robot, the dynamic driving force calculation that is a complicated and lengthy process. The algorithm based on deep learning has the characteristics of high accuracy and fast speed, and has become the key to solving problems in many fields, such as voice recognition [4,5], facial recognition [6,7], and autonomous driving [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Driving Force Estimation of 3-PRS Parallel Robot Based on Deep Learning

2024

IJOMAM

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Aiming at the problem of the difficulty in solving the dynamics driving force of the parallel robot, a deep learning model is proposed to estimate the slider driving force of the 3-PRS parallel robot. The 3-PRS parallel robot is presented, and its dynamics model is established using the Newton-Euler method. The dynamics of the 3-PRS parallel robot is analysed by using the theory of the "black box model". The parameters of the designed 3-PRS parallel robot are used as input, and the driving force can be output directly through the deep learning algorithm. The regression convolutional neural network (CNN) algorithm based on deep learning is established to estimate the slider driving force. An overall framework is proposed according to the characteristics of the three sliders. The parameters of the parallel robot are collected, and the collected data are converted into a grayscale image to establish the data sets for driving force estimation. Two cases with friction and without friction are considered respectively in the experiment. The experimental results show that measuring the driving force using the regression CNN can improve calculation accuracy and efficiency, which lays a foundation for studying the dynamics of the parallel robot.

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

Cited by 8 publications

References 5 publications

Improving Autonomous Vehicle Controls and Quality Using Natural Language Processing-Based Input Recognition Model

Improving Autonomous Vehicle Controls and Quality Using Natural Language Processing-Based Input Recognition Model

Enhancing Security and Accountability in Autonomous Vehicles through Robust Speaker Identification and Blockchain-Based Event Recording

Driving Force Estimation of 3-PRS Parallel Robot Based on Deep Learning

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