An End-to-End solution to Autonomous Driving based on Xilinx FPGAd

被引:6
|
作者
Wu, Tianze [1 ]
Liu, Weiyi [2 ]
Jin, Yongwei [3 ]
机构
[1] Chinese Acad Sci, Inst Comp Technol, Univ Chinese Acad Sci, Beijing, Peoples R China
[2] Shanghai Jiao Tong Univ, Shanghai, Peoples R China
[3] Xidian Univ, Xian, Peoples R China
关键词
Autonomous Driving; Machine Learning; Pynq-Z2; Field programmable gate arrays; Deep Learning Processing Unit;
D O I
10.1109/ICFPT47387.2019.00084
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Nowadays, the autonomous driving topic is very hot, many people are trying to provide a solution to this problem. This time we build our own auto-driving car based on Xilinx Pynq-Z2, it provides an end-to-end solution which inputs images from camera and outputs control instructions directly. The platform also uses the power of Deep learning Processing Unit(DPU) to accelerate the inference process and provides a simulator for training and testing in virtual environment. If the car meets some situations which cannot be handled by AI model, it's easy to add extra traditional computer vision functions to our control system. So our platform can help people who want to try autonomous driving build their own model and test it efficiently. We hope that our platform can be easy to use and extend.
引用
收藏
页码:427 / 430
页数:4
相关论文
共 50 条
  • [31] SuperDriverAI: Towards Design and Implementation for End-to-End Learning-based Autonomous Driving
    Aoki, Shunsuke
    Yamamoto, Issei
    Shiotsuka, Daiki
    Inoue, Yuichi
    Tokuhiro, Kento
    Miwa, Keita
    [J]. 2023 IEEE VEHICULAR NETWORKING CONFERENCE, VNC, 2023, : 195 - 198
  • [32] Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving
    Jia, Xiaosong
    Wu, Penghao
    Chen, Li
    Xie, Jiangwei
    He, Conghui
    Yan, Junchi
    Li, Hongyang
    [J]. 2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2023, : 21983 - 21994
  • [33] Performance optimization of autonomous driving control under end-to-end deadlines
    Bai, Yunhao
    Li, Li
    Wang, Zejiang
    Wang, Xiaorui
    Wang, Junmin
    [J]. REAL-TIME SYSTEMS, 2022, 58 (04) : 509 - 547
  • [34] Real-to-Virtual Domain Unification for End-to-End Autonomous Driving
    Yang, Luona
    Liang, Xiaodan
    Wang, Tairui
    Xing, Eric
    [J]. COMPUTER VISION - ECCV 2018, PT IV, 2018, 11208 : 553 - 570
  • [35] End-to-End Learning of Behavioural Inputs for Autonomous Driving in Dense Traffic
    Shrestha, Jatan
    Idoko, Simon
    Sharma, Basant
    Singh, Arun Kumar
    [J]. 2023 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2023, : 10020 - 10027
  • [36] Multi-Modal Fusion Transformer for End-to-End Autonomous Driving
    Prakash, Aditya
    Chitta, Kashyap
    Geiger, Andreas
    [J]. 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021, 2021, : 7073 - 7083
  • [37] Evaluation of End-To-End Learning for Autonomous Driving: The Good, the Bad and the Ugly
    Varisteas, Georgios
    Frank, Raphael
    Alamdari, Seyed Amin Sajadi
    Voos, Holger
    State, Radu
    [J]. 2019 2ND INTERNATIONAL CONFERENCE ON INTELLIGENT AUTONOMOUS SYSTEMS (ICOIAS 2019), 2019, : 110 - 117
  • [38] Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision
    Mehta, Ashish
    Subramanian, Adithya
    Subramanian, Anbumani
    [J]. ELEVENTH INDIAN CONFERENCE ON COMPUTER VISION, GRAPHICS AND IMAGE PROCESSING (ICVGIP 2018), 2018,
  • [39] Agile Autonomous Driving using End-to-End Deep Imitation Learning
    Pan, Yunpeng
    Cheng, Ching-An
    Saigol, Kamil
    Lee, Keuntaek
    Yan, Xinyan
    Theodorou, Evangelos A.
    Boots, Byron
    [J]. ROBOTICS: SCIENCE AND SYSTEMS XIV, 2018,
  • [40] End-to-end deep learning for reverse driving trajectory of autonomous bulldozer
    You, Ke
    Ding, Lieyun
    Jiang, Yutian
    Wu, Zhangang
    Zhou, Cheng
    [J]. KNOWLEDGE-BASED SYSTEMS, 2022, 252