Ultrasonic Multi-Sensor Detection Patterns On Autonomous Vehicles Using Data Stream Method

Budisusila, Eka Nuryanto; Khosyi’in, Muhammad; Prasetyowati, Sri Arttini Dwi; Suprapto, Bhakti Yudho; Nawawi, Zainuddin

doi:10.23919/eecsi53397.2021.9624313

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…As a response of previous research limitation, the long range sensors of Maxbotix MB7383HRXL and MB7380HRXL ultrasonic sensors are used in this research, which have a detection range of up to 10 meters and are designed to be deployed outside [16]- [18]. By these sensors the limitations range detections are upgraded to be implemented in real condition without light or weather problems.…”

Section: Introductionmentioning

confidence: 99%

Neural network training for serial multisensor of autonomous vehicle system

Budisusila

Prasetyowati²,

Suprapto³

et al. 2022

IJECE

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<span>This study aims to find the best artificial neural network weight values to be applied to the autonomous vehicle system with ultrasonic multisensor. The implementation of neural network in the system required long time process due to its training process. Therefore, this research is using offline training before implementing to online training by embedding the best network weight values to obtain the outputs faster according to desired targets. Simulink were used to train the system offline. Eight ultrasonic sensors are used on all sides of the vehicle and arranged in a serial multisensory configuration as inputs of neural network. With eight inputs, one sixteen-depth hidden layer, and five outputs, it was trained using the back-propagation algorithm of artificial neural network. By 100000 iterations, the output values and the target values are almost the same, indicating its convergency with minimum of errors. The result of this training is the best weights of the networks. These weight values can be implemented as fixed-weight in online training.</span>

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Section: Introductionmentioning

confidence: 99%

Neural network training for serial multisensor of autonomous vehicle system

Budisusila

Prasetyowati²,

Suprapto³

et al. 2022

IJECE

Self Cite

View full text Add to dashboard Cite

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Real Time System Handling Using Multi Fixed Weight Artificial Neural Network

Budisusila,

Dwi Prasetyowati,

Arifin

et al. 2023

2023 10th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)

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A Survey on Sensor Failures in Autonomous Vehicles: Challenges and Solutions

Matos,

Bernardino,

Durães

et al. 2024

Sensors

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Autonomous vehicles (AVs) rely heavily on sensors to perceive their surrounding environment and then make decisions and act on them. However, these sensors have weaknesses, and are prone to failure, resulting in decision errors by vehicle controllers that pose significant challenges to their safe operation. To mitigate sensor failures, it is necessary to understand how they occur and how they affect the vehicle’s behavior so that fault-tolerant and fault-masking strategies can be applied. This survey covers 108 publications and presents an overview of the sensors used in AVs today, categorizes the sensor’s failures that can occur, such as radar interferences, ambiguities detection, or camera image failures, and provides an overview of mitigation strategies such as sensor fusion, redundancy, and sensor calibration. It also provides insights into research areas critical to improving safety in the autonomous vehicle industry, so that new or more in-depth research may emerge.

show abstract

Ultrasonic Multi-Sensor Detection Patterns On Autonomous Vehicles Using Data Stream Method

Cited by 3 publications

References 17 publications

Neural network training for serial multisensor of autonomous vehicle system

Neural network training for serial multisensor of autonomous vehicle system

Real Time System Handling Using Multi Fixed Weight Artificial Neural Network

A Survey on Sensor Failures in Autonomous Vehicles: Challenges and Solutions

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