Deep Learning &amp; Computer Vision for IoT based Intelligent Driver Assistant System

Thevendran, Hirushiharan; Nagendran, Akshaya; Hydher, Hassaan; Bandara, A.W.M.H.K.; Oruthota, Udesh

doi:10.1109/iciafs52090.2021.9605823

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…Another work [ 20 ] considered the safety issue based on driver data and behavior has been introduced under the umbrella of Autonomous Vehicles (AV) through the Driver Assistance System (DAS). Their system can introduce different alerts to the driver based on machine learning algorithm outputs.…”

Section: Related Workmentioning

confidence: 99%

Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller

Hussein

Mohammed

Galal

et al. 2022

Sensors

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The Internet of Things (IoT) era is mainly dependent on the word “Smart”, such as smart cities, smart homes, and smart cars. This aspect can be achieved through the merging of machine learning algorithms with IoT computing models. By adding the Artificial Intelligence (AI) algorithms to IoT, the result is the Cognitive IoT (CIoT). In the automotive industry, many researchers worked on self-diagnosis systems using deep learning, but most of them performed this process on the cloud due to the hardware limitations of the end-devices, and the devices obtain the decision via the cloud servers. Others worked with simple traditional algorithms of machine learning to solve these limitations of the processing capabilities of the end-devices. In this paper, a self-diagnosis smart device is introduced with fast responses and little overhead using the Multi-Layer Perceptron Neural Network (MLP-NN) as a deep learning technique. The MLP-NN learning stage is performed using a Tensorflow framework to generate an MLP model’s parameters. Then, the MLP-NN model is implemented using these model’s parameters on a low cost end-device such as ARM Cortex-M Series architecture. After implementing the MLP-NN model, the IoT implementation is built to publish the decision results. With the proposed implemented method for the smart device, the output decision based on sensors values can be taken by the IoT node itself without returning to the cloud. For comparison, another solution is proposed for the cloud-based architecture, where the MLP-NN model is implemented on Cloud. The results clarify a successful implemented MLP-NN model for little capabilities end-devices, where the smart device solution has a lower traffic and latency than the cloud-based solution.

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Section: Related Workmentioning

confidence: 99%

Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller

Hussein

Mohammed

Galal

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Digital transformation trends such as Artificial Intelligence (AI), Digital Twins and internet of things plays an increasing and integral role in driving innovation and becoming ubiquitous in various domains and applications such as intelligent driving [16,23]. These trends enable smart systems with novel capabilities that were never possible without AI.…”

Section: Introductionmentioning

confidence: 99%

Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation

Wehner¹,

Powlesland²,

Altakrouri³

et al. 2022

Preprint

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Artificial Intelligence and Digital Twins play an integral role in driving innovation in the domain of intelligent driving. Long shortterm memory (LSTM) is a leading driver in the field of lane change prediction for manoeuvre anticipation. However, the decision-making process of such models is complex and non-transparent, hence reducing the trustworthiness of the smart solution. This work presents an innovative approach and a technical implementation for explaining lane change predictions of layer normalized LSTMs using Layer-wise Relevance Propagation (LRP). The core implementation includes consuming live data from a digital twin on a German highway, live predictions and explanations of lane changes by extending LRP to layer normalized LSTMs, and an interface for communicating and explaining the predictions to a human user. We aim to demonstrate faithful, understandable, and adaptable explanations of lane change prediction to increase the adoption and trustworthiness of AI systems that involve humans. Our research also emphases that explainability and state-of-the-art performance of ML models for manoeuvre anticipation go hand in hand without negatively affecting predictive effectiveness.

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Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation

Wehner

Powlesland

Altakrouri

et al. 2022

Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence

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Deep Learning & Computer Vision for IoT based Intelligent Driver Assistant System

Cited by 6 publications

References 11 publications

Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller

Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller

Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation

Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation

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