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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.

References

Abid, F., Izeboudjen, N., Sahli, L., Lazib, D., Titri, S., Louiz, F., & Bakiri, M. (2018).

Towards an open embedded system on chip for network applications. Latest

Trends on Circuits, Systems and Signals.

 

Adafruit. (2020). MPU6050 6-DoF Accelerometer and Gyro. https://cdnlearn.

adafruit.com/downloads/pdf/mpu6050-6-dof-accelerometer-andgyro.

pdf?timestamp=1610817659

 

Alarcón, J., Salvador, R., Moreno, F., Cobos, P., & López, I. (2006). A new real-time

hardware architecture for road line tracking using a particle filter. IEEE

Industrial Electronics, IECON 2006-32nd Annual Conference On, 736–741.

 

Alsheikhy, A., & Said, F. (2019). Design of Embedded Vision System based on FPGASoC.

 

Anaya, J. J., Ponz, A., García, F., & Talavera, E. (2017). Motorcycle detection for

ADAS through camera and V2V Communication, a comparative analysis of

two modern technologies. Expert Systems with Applications, 77, 148–159.

 

Andrade, H., Lwakatare, L. E., Crnkovic, I., & Bosch, J. (2019). Software Challenges

in Heterogeneous Computing: A Multiple Case Study in Industry. 2019 45th

Euromicro Conference on Software Engineering and Advanced Applications

(SEAA), 148–155.

 

Andrade, H., Schroeder, J., & Crnkovic, I. (2019). Software deployment on

heterogeneous platforms: A systematic mapping study. IEEE Transactions on

Software Engineering.

 

Arndt, O. J., Träger, F. D., Mo\s s, T., & Blume, H. (2017). Portable Implementation

of Advanced Driver-Assistance Algorithms on Heterogeneous Architectures.

Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2017

IEEE International, 6–17.

 

B. Wu, H. Chiang, T. Lee, & J. Perng. (2008). The embedded driving-assistance system

on Taiwan iTS-1. 2008 IEEE International Conference on Systems, Man and

Cybernetics, 3382–3387. https://doi.org/10.1109/ICSMC.2008.4811820

 

Ball, J. E., & Tang, B. (2019). Machine Learning and Embedded Computing in

Advanced Driver Assistance Systems (ADAS). Multidisciplinary Digital

Publishing Institute.

 

Batavia, P. H. (1999). Driver-adaptive lane departure warning systems. Carnegie

Mellon University Pittsburgh,, USA.

 

Bauer, W., Holzinger, P., Rachuj, S., Haeublein, K., Reichenbach, M., & Fey, D.

(2019). Evaluating HSA-Compatible Heterogeneous Systems for ADAS

Applications. ARCS Workshop 2019; 32nd International Conference on

Architecture of Computing Systems, 1–8.

 

Bécsi, T., Aradi, S., Fehér, Á., & Gáldi, G. (2017). Autonomous Vehicle Function

Experiments with Low-Cost Environment Sensors. Transportation Research

Procedia, 27, 333–340.

 

Bell, S., Pu, J., Hegarty, J., & Horowitz, M. (2018). Compiling algorithms for

heterogeneous systems. Synthesis Lectures on Computer Architecture, 13(1), 1–

105.

 

Besta, M., Stanojevic, D., Licht, J. D. F., Ben-Nun, T., & Hoefler, T. (2019). Graph

Processing on FPGAs: Taxonomy, Survey, Challenges. ArXiv Preprint

ArXiv:1903.06697.

 

Bougharriou, S., Hamdaoui, F., & Mtibaa, A. (2016). Hardware architecture:

Correlation-based approach for road sign detection. Advanced Technologies for

Signal and Image Processing (ATSIP), 2016 2nd International Conference On,

247–251.

 

Brackenbury, L. E., Plana, L. A., & Pepper, J. (2010). System-on-chip design and

implementation. IEEE Transactions on Education, 53(2), 272–281.

 

Brenot, F., Fillatreau, P., & Piat, J. (2015). FPGA based accelerator for visual features

detection. Electronics, Control, Measurement, Signals and Their Application to

Mechatronics (ECMSM), 2015 IEEE International Workshop Of, 1–6.

 

Burgio, P., Bertogna, M., Capodieci, N., Cavicchioli, R., Sojka, M., Houdek, P.,

Marongiu, A., Gai, P., Scordino, C., & Morelli, B. (2017). A software stack for

next-generation automotive systems on many-core heterogeneous platforms.

Microprocessors and Microsystems, 52, 299–311.

 

Calefato, C., Ferrarini, C., Landini, E., Kutila, M., & Quinteiro, E. G. (2016). The

modularisation design approach applied to the ADAS domain: The DESERVE

project experience. Transportation Research Procedia, 14, 2265–2273.

 

Campmany, V., Silva, S., Espinosa, A., Moure, J. C., Vázquez, D., & López, A. M.

(2016). GPU-based pedestrian detection for autonomous driving. ArXiv

Preprint ArXiv:1611.01642.

 

CHAABAN, K., SHAWKY, M., & CRUBILLE, P. (2009). A distributed embedded

architecture for the evaluation of ADAS systems. IFAC Proceedings Volumes,

42(15), 237–244.

 

Chishiro, H., Suito, K., Ito, T., Maeda, S., Azumi, T., Funaoka, K., & Kato, S. (2019).

Towards Heterogeneous Computing Platforms for Autonomous Driving. 2019

IEEE International Conference on Embedded Software and Systems (ICESS),

1–8.

 

D. Poddar, P. Swami, S. Nagori, P. Viswanath, M. Mathew, D. Kumar, A. Jain, & S.

Jagannathan. (2017). Real time Structure from Motion for Driver Assistance

System. 2017 IEEE International Conference on Consumer Electronics (ICCE),

231–232. https://doi.org/10.1109/ICCE.2017.7889295

 

Danowitz, A., Kelley, K., Mao, J., Stevenson, J. P., & Horowitz, M. (2012). CPU DB:

Recording microprocessor history. Queue, 10(4), 10–27.

 

Dell. (2019). Dell EMC Isilon: Deep Learning Infrastructure for Autonomous Driving.

Dell EMC Isilon. https://www.delltechnologies.com/enca/

collaterals/unauth/whitepapers/

solutions/h17918_deep_learning_infrastructure_for_autonomous_drivi

ng.pdf

 

Diniz, W. F., Frémont, V., Fantoni, I., & Nóbrega, E. G. (2017). An FPGA-based

architecture for embedded systems performance acceleration applied to

Optimum-Path Forest classifier. Microprocessors and Microsystems, 52, 261–

271.

 

Du, S., Huang, T., Hou, J., Song, S., & Song, Y. (2019). FPGA based acceleration of

game theory algorithm in edge computing for autonomous driving. Journal of

Systems Architecture, 93, 33–39.

 

Faisal, I. A., Purboyo, T. W., & Ansori, A. S. R. (2020). A Review of Accelerometer

Sensor and Gyroscope Sensor in IMU Sensors on Motion Capture. Journal of

Engineering and Applied Sciences, 15(3), 826–829.

 

Fan, R., Prokhorov, V., & Dahnoun, N. (2016). Faster-than-real-time linear lane

detection implementation using SoC DSP TMS320C6678. Imaging Systems

and Techniques (IST), 2016 IEEE International Conference On, 306–311.

 

Fernandes, L. C., Souza, J. R., Pessin, G., Shinzato, P. Y., Sales, D., Mendes, C., Prado,

M., Klaser, R., Magalhaes, A. C., & Hata, A. (2014). CaRINA intelligent

robotic car: Architectural design and applications. Journal of Systems

Architecture, 60(4), 372–392.

 

Forsberg, B., Palossi, D., Marongiu, A., & Benini, L. (2017). GPU-Accelerated Real-

Time Path Planning and the Predictable Execution Model. Procedia Computer

Science, 108, 2428–2432.

 

Galko, C., Rossi, R., & Savatier, X. (2014). Vehicle-hardware-in-the-loop system for

adas prototyping and validation. Embedded Computer Systems: Architectures,

Modeling, and Simulation (SAMOS XIV), 2014 International Conference On,

329–334.

 

Garmin. (2016). V3 Operation Manual and Technical Specifications. Garmin: Olathe,

KS, USA, 1–14.

 

Gehrig, S., Schneider, N., Stalder, R., & Franke, U. (2017). Stereo vision during adverse

weather—Using priors to increase robustness in real-time stereo vision. Image

and Vision Computing, 68, 28–39.

 

Gerstlauer, A., Haubelt, C., Pimentel, A. D., Stefanov, T. P., Gajski, D. D., & Teich, J.

(2009). Electronic system-level synthesis methodologies. IEEE Transactions on

Computer-Aided Design of Integrated Circuits and Systems, 28(10), 1517–

1530.

 

Giesemann, F., Payá-Vayá, G., Blume, H., Limmer, M., & Ritter, W. (2014). A

comprehensive ASIC/FPGA prototyping environment for exploring embedded

processing systems for advanced driver assistance applications. Embedded

Computer Systems: Architectures, Modeling, and Simulation (SAMOS XIV),

2014 International Conference On, 314–321.

 

Goswami, P., Chitnis, K., Jadav, B., Kapania, A., & Sivasankaran, S. (2017). Software

framework for runtime application monitoring of fail-safe multi-processor

ADAS SoCs. 2017 IEEE International Conference on Consumer Electronics

(ICCE), 39–42.

 

Greaves, D. J. (2011). System on Chip Design and Modelling. University of Cambridge

Computer Laboratory Lecture Notes, 130.

 

Gruyer, D., Belaroussi, R., Li, X., Lusetti, B., Revilloud, M., & Glaser, S. (2015).

PerSEE: A central sensors fusion electronic control unit for the development of

perception-based ADAS. Machine Vision Applications (MVA), 2015 14th IAPR

International Conference On, 250–254.

 

Gruyer, D., Magnier, V., Hamdi, K., Claussmann, L., Orfila, O., & Rakotonirainy, A.

(2017). Perception, information processing and modeling: Critical stages for

autonomous driving applications. Annual Reviews in Control, 44, 323–341.

 

Hammond, M., Qu, G., & Rawashdeh, O. A. (2015). Deploying and scheduling vision

based advanced driver assistance systems (ADAS) on heterogeneous multicore

embedded platform. Frontier of Computer Science and Technology (FCST),

2015 Ninth International Conference On, 172–177.

 

Hamza, B., Abdelhakim, K., & Brahim, C. (2012). FPGA design of a real-time obstacle

detection system using stereovision. Microelectronics (ICM), 2012 24th

International Conference On, 1–4.

 

Harada, K., Kanazawa, K., & Yasunaga, M. (2019). FPGA-Based Object Detection for

Autonomous Driving System. 2019 International Conference on Field-

Programmable Technology (ICFPT), 465–468.

 

Heimberger, M., Horgan, J., Hughes, C., McDonald, J., & Yogamani, S. (2017).

Computer vision in automated parking systems: Design, implementation and

challenges. Image and Vision Computing, 68, 88–101.

 

Hernandez-Juarez, D., Chacón, A., Espinosa, A., Vázquez, D., Moure, J. C., & López,

A. M. (2016). Embedded real-time stereo estimation via semi-global matching

on the GPU. Procedia Computer Science, 80, 143–153.

 

Hiltschera, J., Akulaa, S. P., Streiterb, R., & Wanielika, G. (2018). A flexible automotive

systems architecture for next generation ADAS.

 

Huang, B. K., Vann, R. G. L., Freethy, S., Myers, R. M., Naylor, G., Sharples, R. M.,

& Shevchenko, V. F. (2012). FPGA-based embedded Linux technology in

fusion: The MAST microwave imaging system. Fusion Engineering and

Design, 87(12), 2106–2111.

 

Hwang, S., & Lee, Y. (2016). FPGA-based real-time lane detection for advanced driver

assistance systems. Circuits and Systems (APCCAS), 2016 IEEE Asia Pacific

Conference On, 218–219.

 

Hwu, W.-M. W. (2016). Chapter 1—Introduction. In W. W. Hwu (Ed.), Heterogeneous

System Architecture (pp. 1–5). Morgan Kaufmann.

https://doi.org/10.1016/B978-0-12-800386-2.00009-2

 

Iagnemma, K., & Buehler, M. (2006). Editorial for Journal of Field Robotics—Special

issue on the DARPA grand challenge. Journal of Field Robotics, 23(9), 655–

656.

 

Intel. (2009). Standard Cell ASIC to FPGA Design Methodology and Guidelines.

https://www.intel.com/content/dam/www/programmable/us/en/pdfs/literature/

an/an311.pdf

 

Intel. (2017). Quartus Prime standard edition handbook volume 1: Design and

synthesis. USA.

 

Intel. (2020). AN 307: Intel® FPGA Design Flow for Xilinx Users.

https://www.intel.com/content/dam/www/programmable/us/en/pdfs/literature/

an/an307.pdf

 

Intel. (2021a). Intel FPGA and Programmable Devices.

https://www.intel.com/content/www/us/en/products/programmable.html

 

Intel. (2021b). Skylake (microarchitecture). In Wikipedia.

https://en.wikipedia.org/w/index.php?title=Skylake_(microarchitecture)&oldid

=998421085

 

Intel Corporation. (2014). Booting and Configuration Introduction.

https://www.intel.cn/content/dam/www/programmable/us/en/pdfs/literature/hb

/arria-10/a10_5400a.pdf

 

Intel Corporation. (2015). Instantiating the Nios II Processor. Instantiating the Nios II

Processor

 

Intel PSG website. (2020).

https://www.intel.com/content/www/us/en/programmable/products/boards_an

d_kits/dev-kits/altera/kit-cyclone-v-soc.html

 

International Organization for Standardization. (2011). ISO/IEC/IEEE 42010:2011

Systems and Software Engineering—Architecture Description.

 

J. Rettkowski, A. Boutros, & D. Göhringer. (2015). Real-time pedestrian detection on

a xilinx zynq using the HOG algorithm. 2015 International Conference on

ReConFigurable Computing and FPGAs (ReConFig), 1–8.

https://doi.org/10.1109/ReConFig.2015.7393339

 

Jian-feng, L., Chun-Yi, W., & Jie, H. (2012). A High Performance Data Storage

Method for Embedded Linux Real-time Database in Power Systems. Energy

Procedia, 16, 883–888.

 

Józwiak, L. (2017). Advanced mobile and wearable systems. Microprocessors and

Microsystems, 50, 202–221.

 

K. Huang, B. Hu, L. Chen, A. Knoll, & Z. Wang. (2018). ADAS on COTS with OpenCL:

A Case Study with Lane Detection. IEEE Transactions on Computers, 67(4),

559–565. https://doi.org/10.1109/TC.2017.2759203

 

Karan, S., Sudarshan, S., Aditya, S., Rameez, S., & Apurva, P. (2018). Component

Measurement Using Ultrasonic Sensor. International Research Journal of

Engineering and Technology (IRJET), 5(5).

https://www.irjet.net/archives/V5/i5/IRJET-V5I5193.pdf

 

Khan, J., Tatkeu, C., Deloof, P., & Niar, S. (2011). Data association techniques for

advanced driver assistance systems using embedded soft-core processors. ITS

Telecommunications (ITST), 2011 11th International Conference On, 51–55.

 

Kocic, O., Simic, A., Bjelica, M. Z., & Maruna, T. (2016). Optimization of driver

monitoring ADAS algorithm for heterogeneous platform. 2016 24th

Telecommunications Forum (TELFOR), 1–4.

 

Komorkiewicz, M., Turek, K., Skruch, P., Kryjak, T., & Gorgon, M. (2016). FPGAbased

Hardware-in-the-Loop environment using video injection concept for

camera-based systems in automotive applications. Design and Architectures for

Signal and Image Processing (DASIP), 2016 Conference On, 183–190.

 

Kudo, Y., Takada, A., Ishida, Y., & Izumi, T. (2019). An SoC-FPGA-Based Micro

UGV with Localization and Motion Planning. 2019 International Conference

on Field-Programmable Technology (ICFPT), 469–472.

 

Kukkala, V. K., Tunnell, J., Pasricha, S., & Bradley, T. (2018). Advanced driverassistance

systems: A path toward autonomous vehicles. IEEE Consumer

Electronics Magazine, 7(5), 18–25.

 

Kwon, S., & Lee, H.-J. (2016). Dense stereo-based real-time ROI generation for onroad

obstacle detection. SoC Design Conference (ISOCC), 2016 International,

179–180.

 

Kyriazis, G. (2012). Heterogeneous system architecture: A technical review. AMD

Fusion Developer Summit, 21.

 

Lee, K. J., Bong, K., Kim, C., Jang, J., Lee, K.-R., Lee, J., Kim, G., & Yoo, H.-J. (2017).

A 502-GOPS and 0.984-mW dual-mode intelligent ADAS SoC with real-time

semiglobal matching and intention prediction for smart automotive black box

system. IEEE Journal of Solid-State Circuits, 52(1), 139–150.

 

Lee, S., Son, H., Choi, J. C., & Min, K. (2012). HOG feature extractor circuit for realtime

human and vehicle detection. TENCON 2012-2012 IEEE Region 10

Conference, 1–5.

 

Lee, S.-S., Lee, E., Hwang, Y., & Jang, S.-J. (2016). Low-complexity hardware

architecture of traffic sign recognition with IHSL color space for advanced

driver assistance systems. Consumer Electronics-Asia (ICCE-Asia), IEEE

International Conference On, 1–2.

 

Li, L., Fajar, E., Kurimoto, K., & Goto, S. (2005). A mixed design flow for FPGA

prototyping of design with scan circuits. 2005 6th International Conference on

ASIC, 2, 1127–1130.

 

Lin, I.-A., Lee, T.-Y., Chen, C.-M., & Liu, S.-Y. (2017). FPGA-based fast rain removal

system using orientation-adaptive non-local mean filter. Consumer Electronics-

Taiwan (ICCE-TW), 2017 IEEE International Conference On, 39–40.

 

Lohani, B., & Ghosh, S. (2017). Airborne LiDAR technology: A review of data

collection and processing systems. Proceedings of the National Academy of

Sciences, India Section A: Physical Sciences, 87(4), 567–579.

 

Lopes, I. C., Benevenuti, F., Kastensmidt, F. L., Susin, A. A., & Rech, P. (2018).

Reliability analysis on case-study traffic sign convolutional neural network on

APSoC. Test Symposium (LATS), 2018 IEEE 19th Latin-American, 1–6.

 

Lopez, D., & Clairet, M. (2016). Fail silent and robust power management architectures

to enable autonomous driving embedded systems. 2016 International

Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and

Road Vehicles & International Transportation Electrification Conference

(ESARS-ITEC), 1–6.

 

Louis, L. (2016). WORKING PRINCIPLE OF ARDUINO AND U SING IT.

International Journal of Control, Automation, Communication and Systems

(IJCACS), 1(2), 21–29.

 

Lyytinen, H., Haataja, K., & Toivanen, P. (2009). Designing and implementing an

embedded linux for limited resource devices. 2009 Eighth International

Conference on Networks, 18–23.

 

Mandal, D. K., Sankaran, J., Gupta, A., Castille, K., Gondkar, S., Kamath, S., Sundar,

P., & Phipps, A. (2014). An Embedded Vision Engine (EVE) for automotive

vision processing. Circuits and Systems (ISCAS), 2014 IEEE International

Symposium On, 49–52.

 

Martinez, L. A., & Marques, E. (2016). A hardware/software codesign framework for

vision-based ADAS. Field Programmable Logic and Applications (FPL), 2016

26th International Conference On, 1–2.

 

Martínez-Barberá, H., & Herrero-Pérez, D. (2014). Multilayer distributed intelligent

control of an autonomous car. Transportation Research Part C: Emerging

Technologies, 39, 94–112.

 

Mensch, W. D., & Silage, D. A. (2000). System-on-chip design methodology in

engineering education. International Conference on Engineering Education.

 

Milõs, J., Milan, V., Boris, D., & Milija, S. (2013). Using RapidMiner for Research:

Experimental Evaluation of Learners. RapidMiner: Data Mining Use Cases and

Business Analytics Applications, 439.

 

Mirnig, A. G., Gärtner, M., Laminger, A., Meschtscherjakov, A., Trösterer, S.,

Tscheligi, M., McCall, R., & McGee, F. (2017). Control transition interfaces in

semiautonomous vehicles: A categorization framework and literature analysis.

Proceedings of the 9th International Conference on Automotive User Interfaces

and Interactive Vehicular Applications, 209–220.

 

Mody, M., Sanghvi, H., Nandan, N., Dabral, S., Allu, R., Sagar, R., Chitnis, K., Jones,

J., Jadhav, B., & Shivalingappa, S. (2017). A 216 gops flexible wdr image

processor for adas soc. 2017 IEEE Symposium in Low-Power and High-Speed

Chips (COOL CHIPS), 1–2.

 

Nvidia. (2018). UNMATCHED POWER. UNMATCHED CREATIVE FREEDOM.

NVIDIA QUADRO P6000. https://www.nvidia.com/content/dam/enzz/

Solutions/design-visualization/productspage/quadro/quadrodesktop/

quadro-pascal-p6000-data-sheet-us-nv-704590-r1.pdf

 

Nvidia. (2020). NVIDIA DRIVE AGX. https://www.nvidia.com/en-us/self-drivingcars/

drive-platform/hardware/

 

O. Abid, Q. Cabannes, & B. Senouci. (2018). Supervisor and control investigation in

smart/autonomous vehicles: Environment recognition and objects detection

ADAS application case study. 2018 11th International Symposium on

Mechatronics and Its Applications (ISMA), 1–7.

https://doi.org/10.1109/ISMA.2018.8330135

 

Okamoto, K., & Tsiotras, P. (2019). Data-driven human driver lateral control models

for developing haptic-shared control advanced driver assist systems. Robotics

and Autonomous Systems, 114, 155–171.

 

Okuda, R., Kajiwara, Y., & Terashima, K. (2014). A survey of technical trend of ADAS

and autonomous driving. Technical Papers of 2014 International Symposium

on VLSI Design, Automation and Test, 1–4.

 

P. Swami, A. Jain, P. Goswami, K. Chitnis, A. Dubey, & P. Chaudhari. (2017). High

performance automotive radar signal processing on TI’s TDA3X platform. 2017

IEEE Radar Conference (RadarConf), 1317–1320.

https://doi.org/10.1109/RADAR.2017.7944409

 

P. Yadav & J. Guddeti. (2017). A methodology for validation of system level

synchronization in different interface standards for automotive microcontroller.

2017 2nd IEEE International Conference on Intelligent Transportation

Engineering (ICITE), 67–71. https://doi.org/10.1109/ICITE.2017.8056883

 

Patel, J. J., Reddy, N., Kumari, P., Rajpal, R., Pujara, H., Jha, R., & Kalappurakkal, P.

(2014). Embedded Linux platform for data acquisition systems. Fusion

Engineering and Design, 89(5), 684–688.

 

Peng, J., Tian, L., Jia, X., Guo, H., Xu, Y., Xie, D., Luo, H., Shan, Y., & Wang, Y.

(2019). Multi-task ADAS system on FPGA. 2019 IEEE International

Conference on Artificial Intelligence Circuits and Systems (AICAS), 171–174.

 

Purkayastha, A. A., Shiddhibhavi, S. A., & Tabkhi, H. (2018). Taxonomy of spatial

parallelism on fpgas for massively parallel applications. 2018 31st IEEE

International System-on-Chip Conference (SOCC), 55–60.

 

R. Bushey, H. Tabkhi, & G. Schirner. (2013). Flexible function-level acceleration of

embedded vision applications using the Pipelined Vision Processor. 2013

Asilomar Conference on Signals, Systems and Computers, 1447–1452.

https://doi.org/10.1109/ACSSC.2013.6810535

 

R. Saussard, B. Bouzid, M. Vasiliu, & R. Reynaud. (2015). Towards an Automatic

Prediction of Image Processing Algorithms Performances on Embedded

Heterogeneous Architectures. 2015 44th International Conference on Parallel

Processing Workshops, 27–36. https://doi.org/10.1109/ICPPW.2015.14

 

Rahul, K. (2016). On-Road Intelligent Vehicles. Elsevier.

 

Ranft, B., Schoenwald, T., & Kitt, B. (2011). Parallel matching-based estimation-a case

study on three different hardware architectures. Intelligent Vehicles Symposium

(IV), 2011 IEEE, 1060–1067.

 

Rifenbark, S. (2014). Yocto Project Development Manual.

https://www.yoctoproject.org/docs/1.6.1/dev-manual/dev-manual.pdf

 

Rupp, A., Tranninger, M., Wallner, R., Zubaca, J., Steinberger, M., & Horn, M. (2019).

Fast and low-cost testing of advanced driver assistance systems using smallscale

vehicles. IFAC-PapersOnLine, 52(5), 34–39.

 

Sahlbach, H., Whitty, S., Bende, O., & Ernst, R. (2010). A Scalable, High-Performance

Motion Estimation Application for a Weakly-Programmable FPGA

Architecture. Field Programmable Logic and Applications (FPL), 2010

International Conference On, 15–18.

 

Sally, G. (2010). Pro Linux embedded systems. Apress.

 

Salvador, O., & Angolini, D. (2014). Embedded Linux Development with Yocto Project.

Packt Publishing Ltd.

 

Saussard, R., Bouzid, B., Vasiliu, M., & Reynaud, R. (2015a). Optimal performance

prediction of ADAS algorithms on embedded parallel architectures. High

Performance Computing and Communications (HPCC), 2015 IEEE 7th

International Symposium on Cyberspace Safety and Security (CSS), 2015 IEEE

12th International Conferen on Embedded Software and Systems (ICESS), 2015

IEEE 17th International Conference On, 213–218.

 

Saussard, R., Bouzid, B., Vasiliu, M., & Reynaud, R. (2015b). The embeddability of

lane detection algorithms on heterogeneous architectures. Image Processing

(ICIP), 2015 IEEE International Conference On, 4694–4697.

 

Schaub, A., de la Cruz, J. C. R., & Burschka, D. (2014). Autonomous parking using a

highly maneuverable robotic vehicle. IFAC Proceedings Volumes, 47(3), 2640–

2645.

 

Schumacher, F., & Greiner, T. (2014). Matching cost computation algorithm and high

speed fpga architecture for high quality real-time semi global matching stereo

vision for road scenes. Intelligent Transportation Systems (ITSC), 2014 IEEE

17th International Conference On, 3064–3069.

 

Schwiegelshohn, F., Gierke, L., & Hübner, M. (2015). FPGA based traffic sign

detection for automotive camera systems. ReCoSoC, 1–6.

 

Schwiegelshohn, F., & Hübner, M. (2014). Design of an attention detection system on

the zynq-7000 soc. ReConFigurable Computing and FPGAs (ReConFig), 2014

International Conference On, 1–6.

 

Senouci, B., Rouis, H., Han, D.-S., & Bourennanea, E. (2017). A hardware skinsegmentation

IP for vision based smart ADAS through an FPGA prototyping.

2017 Ninth International Conference on Ubiquitous and Future Networks

(ICUFN), 197–199.

 

Shi, W., Alawieh, M. B., Li, X., & Yu, H. (2017). Algorithm and hardware

implementation for visual perception system in autonomous vehicle: A survey.

Integration, 59, 148–156. https://doi.org/10.1016/j.vlsi.2017.07.007

 

Shibahara, S. (2018). Functional safety SoC for autonomous driving. 2018 IEEE

Custom Integrated Circuits Conference (CICC), 1–8.

 

Soltani, A., & Assadian, F. (2016). A Hardware-in-the-Loop Facility for Integrated

Vehicle Dynamics Control System Design and Validation. IFACPapersOnLine,

49(21), 32–38.

 

Spagnolo, F., Perri, S., Frustaci, F., & Corsonello, P. (2018). Connected component

analysis for traffic sign recognition embedded processing systems. 2018 25th

IEEE International Conference on Electronics, Circuits and Systems (ICECS),

749–752.

 

Tan, C. Y., Ismail, N., Ooi, C. Y., & Hon, J. Y. (2019). Accelerating Extreme Learning

Machine on FPGA by Hardware Implementation of Given Rotation-QRD.

International Journal of Integrated Engineering, 11(7), 31–39.

 

Tanaka, T., Ikeno, I., Tsuruoka, R., Kuchiba, T., Liao, W., & Mitsuyama, Y. (2019).

Development of Autonomous Driving System Using Programmable SoCs. 2019

International Conference on Field-Programmable Technology (ICFPT), 453–

456.

 

Terasic. (2017a). DE10-Nano SoC User Manual. https://www.terasic.com.tw/cgibin/

page/archive_download.pl?Language=English&No=1046&FID=f1f656bb

5f040121c36f2f93f6b107ff

 

Terasic. (2017b). Terasic—SoC Platform—Cyclone—DE10-Nano Kit.

https://www.terasic.com.tw/cgibin/

page/archive.pl?Language=English&CategoryNo=205&No=1046&PartNo

=2

 

TI. (2013). Advanced Driver Assistance (ADAS) Solutions Guide.

https://uk.farnell.com/wcsstore/ExtendedSitesCatalogAssetStore/cms/asset/im

ages/europe/common/applications/automotive/pdf/ti-adas-solution-guide.pdf

 

TI. (2019). Ultrasonic Sensing Basics.

https://www.ti.com/lit/an/slaa907c/slaa907c.pdf?ts=1610556385347&ref_url=

https%253A%252F%252Fwww.google.com%252F

 

Vahidi, A., & Eskandarian, A. (2003). Research advances in intelligent collision

avoidance and adaptive cruise control. IEEE Transactions on Intelligent

Transportation Systems, 4(3), 143–153.

 

Vanholme, B., Gruyer, D., Lusetti, B., Glaser, S., & Mammar, S. (2012). Highly

automated driving on highways based on legal safety. IEEE Transactions on

Intelligent Transportation Systems, 14(1), 333–347.

 

Velez, G., Cortés, A., Nieto, M., Vélez, I., & Otaegui, O. (2015). A reconfigurable

embedded vision system for advanced driver assistance. Journal of Real-Time

Image Processing, 10(4), 725–739. https://doi.org/10.1007/s11554-014-0412-3

 

Verma, R. K., Sukumar, N., & Sumathi, P. (2019). Vision-Based Estimation of Range

and Direction of Preceding Vehicle for Advanced Driver Assistance Systems.

2019 IEEE 16th India Council International Conference (INDICON), 1–4.

https://doi.org/10.1109/INDICON47234.2019.9030272

 

Viswanath, P., Chitnis, K., Swami, P., Mody, M., Shivalingappa, S., Nagori, S.,

Mathew, M., Desappan, K., Jagannathan, S., Poddar, D., Jain, A., Garud, H.,

Appia, V., Mangla, M., & Dabral, S. (2016, June). A Diverse Low Cost High

Performance Platform for Advanced Driver Assistance System (ADAS)

Applications. The IEEE Conference on Computer Vision and Pattern

Recognition (CVPR) Workshops.

 

Wang, X., Huang, K., & Knoll, A. (2019). Performance Optimisation of Parallelized

ADAS Applications in FPGA-GPU Heterogeneous Systems: A Case Study

With Lane Detection. IEEE Transactions on Intelligent Vehicles, 4(4), 519–

531.

 

Wang, Y., & Nouta, R. (2004). System design methodologies–High level synthesis and

a VHDL implementation of a practical scheme for UWB communication [PhD

Thesis]. Citeseer.

 

What is FPGA. (2020).

https://www.intel.com/content/www/us/en/products/programmable/fpga/newto-

fpgas/resource-center/overview.html

 

World Health Organization Report. (2020). https://www.who.int/news-room/factsheets/

detail/road-traffic-injuries

 

Wu, T., Liu, W., & Jin, Y. (2019). An End-to-End solution to Autonomous Driving

based on Xilinx FPGA. 2019 International Conference on Field-Programmable

Technology (ICFPT), 427–430.

 

Yadav, P., & Guddeti, J. (2016). FPGA based validation technique for Advanced Driver

Assistance System. Embedded Computing and System Design (ISED), 2016

Sixth International Symposium On, 159–165.

 

Yao, Y., Zhang, Z., Yang, Z., Wang, J., & Lai, J. (2017). FPGA-based convolution

neural network for traffic sign recognition. ASIC (ASICON), 2017 IEEE 12th

International Conference On, 891–894.

 

Yi-Yuan Chen, Yuan-Yao Tu, Cheng-Hsiang Chiu, & Y. Chen. (2009). An embedded

system for vehicle surrounding monitoring. 2009 2nd International Conference

on Power Electronics and Intelligent Transportation System (PEITS), 2, 92–95.

https://doi.org/10.1109/PEITS.2009.5406797

 

Yoshizawa, A., & Iwasaki, H. (2018). Influence of a Driver’s Mindset on

Understanding Driver-Assist Systems. 2018 IEEE 17th International

Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC),

393–400.

 

Zaimovic, S., Šiljak, H., & Jokic, D. (2018). Artificial Colloquist: Treating Social

Anxiety Disorder Using Altera FPGA. IFAC-PapersOnLine, 51(6), 336–341.

 

Zhang, J., Wang, F.-Y., Wang, K., Lin, W.-H., Xu, X., & Chen, C. (2011). Data-driven

intelligent transportation systems: A survey. IEEE Transactions on Intelligent

Transportation Systems, 12(4), 1624–1639.

 

Zhang, X., Wei, X., Sang, Q., Chen, H., & Xie, Y. (2020). An Efficient FPGA-Based

Implementation for Quantized Remote Sensing Image Scene Classification

Network. Electronics, 9(9), 1344.

 

Zhong, G., Niar, S., Prakash, A., & Mitra, T. (2016). Design of multiple-target tracking

system on heterogeneous system-on-chip devices. IEEE Transactions on

Vehicular Technology, 65(6), 4802–4812.

 

Zhou, Y., Chen, Z., & Huang, X. (2016). A system-on-chip FPGA design for real-time

traffic signal recognition system. Circuits and Systems (ISCAS), 2016 IEEE

International Symposium On, 1778–1781.

 

Ziener, D. (2018). Improving Reliability, Security, and Efficiency of Reconfigurable

Hardware Systems. ArXiv Preprint ArXiv:1809.11156.

 


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