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Type :thesis
Main Author :Jumaah, Fawaz Mohammed
Title :The design and implementation of advanced driver assistance system (ADAS) data acquisition engine based on heterogeneous computation platform
Place of Production :Tanjong Malim
Year of Publication :2021
Corporate Name :Universiti Pendidikan Sultan Idris
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Abstract : Universiti Pendidikan Sultan Idris
ADAS supports drivers with the required tools and augments to properly decide while driving the car. It can safely control the car either by providing relevant information around the car to the driver, or by taking control of the vehicle movement, partially or completely. The purpose of this study is to develop an ADAS Data Acquisition Engine based on heterogenous platform that utilises SoC and FPGA platforms. Furthermore, the design methodology of heterogenous SoC-FPGA platform is developed to reduce design complexity of heterogenous design flow. Furthermore, it unifies the hardware and software design flows to reduce design cycle time required for development. The proposed system was verified indoor to confirm system functionality. After that, the proposed system was implemented on car for real-time system validation and testing. The system was able to interact with LiDAR sensor, four ultrasonic sensors, and Inertial Movement Unit (IMU) sensors. The LiDAR and ultrasonic were used for long distance, and short distance measurements, respectively. The proposed system implemented on FPGA consumed 15% of logic resources, and 76% of internal memory. The proposed test plan has been derived based on case study reliability tests, system functionality tests, data validation tests, and ADAS application functionality tests. Each test was executed four times to ensure system reliability. The proposed system was able to detect objects in short-range perspective through the ultrasonic sensor from 20 centimetres to 450 centimetres. Furthermore, the system was able to detect long-range distance through the LiDAR from 4 meters, up to 70 meters. The car steering wheel orientation was measured through the IMU sensor ranged from 58.8 angle (clockwise steering movement) to -61.4 angle (anti-clockwise steering movement). The collected data was postprocessed through Rapidminer studio software tool and was presented for further Artificial Intelligence (AI) future applications.

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