Development and Key Technologies of the Cognition system of Autonomous Driving
DOI:
https://doi.org/10.54097/hset.v56i.10835Keywords:
Autonomous vehicles, cognition, perception, assessment and prediction.Abstract
With the rapid development of automotive electronics technology and artificial intelligence, autonomous driving has become an important solution for future transportation challenges. As an integral component of autonomous driving systems, the intelligent cognitive system has emerged as a prominent area of technological research. This paper focuses on the development and key technologies of autonomous driving cognitive systems, based on published literature. The architecture of cognitive systems for autonomous driving is typically divided into two subsystems: the environmental perception system and the assessment and prediction system. Our study provides a detailed description of the methods used in both subsystems. We then conduct a comparative analysis and discussion to evaluate their strengths and limitations. Overall, we hope that this study will provide timely and valuable information to a rapidly evolving field concerning cognitive technology for autonomous vehicles.
Downloads
References
E! 45: Programme For a European Traffic System With Highest Efficiency and Unprecedented Safety, May 2019, [online] Available: https://www.eurekanetwork.org/project/id/45.https://www.eurekanetwork.org/project/id/45.
Thorpe, Charles, et al. "Toward autonomous driving: the cmu navlab. ii. architecture and systems." IEEE expert 6.4 (1991): 44-52.
L. Liu et al., "Computing Systems for Autonomous Driving: State of the Art and Challenges," in IEEE Internet of Things Journal, vol. 8, no. 8, pp. 6469-6486, 15 April15, 2021, doi: 10.1109/JIOT.2020.3043716.
F. Liu, C. Shen, G. Lin and I. Reid, "Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 10, pp. 2024-2039, 1 Oct. 2016, doi: 10.1109/TPAMI.2015.2505283.
Binas, Jonathan, et al. "DDD17: End-to-end DAVIS driving dataset." arXiv preprint arXiv:1711.01458 (2017).
C. V. Poulton, D. B. Cole, A. Yaacobi and M. R. Watts, "Frequency-modulated continuous-wave LIDAR module in silicon photonics," 2016 Optical Fiber Communications Conference and Exhibition (OFC), Anaheim, CA, USA, 2016, pp. 1-3.
P. F. McManamon et al., "Optical phased array technology," in Proceedings of the IEEE, vol. 84, no. 2, pp. 268-298, Feb. 1996, doi: 10.1109/5.482231.
O. Schumann, M. Hahn, J. Dickmann and C. Wöhler, "Semantic Segmentation on Radar Point Clouds," 2018 21st International Conference on Information Fusion (FUSION), Cambridge, UK, 2018, pp. 2179-2186, doi: 10.23919/ICIF.2018.8455344.
Chang S, Zhang Y, Zhang F, Zhao X, Huang S, Feng Z, Wei Z. Spatial Attention Fusion for Obstacle Detection Using MmWave Radar and Vision Sensor. Sensors. 2020; 20(4):956. https://doi.org/10.3390/s20040956
Kim, Tae-Hyeong, and Tae-Hyoung Park. "Placement optimization of multiple lidar sensors for autonomous vehicles." IEEE Transactions on Intelligent Transportation Systems 21.5 (2019): 2139-2145.
Jaritz, Maximilian, et al. "Sparse and dense data with cnns: Depth completion and semantic segmentation." 2018 International Conference on 3D Vision (3DV). IEEE, 2018.
Cheng, Xuelian, et al. "Noise-aware unsupervised deep lidar-stereo fusion." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019.
M. Montemerlo, J. Levinson and S. Thrun, Map-Based Precision Vehicle Localization in Urban Environments, Collection of open conferences in research transport (2007). Vol. 2007, 141 URL https://www.scipedia.com/public/Montemerlo_et_al_2007a
G. Bresson, Z. Alsayed, L. Yu and S. Glaser, "Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving," in IEEE Transactions on Intelligent Vehicles, vol. 2, no. 3, pp. 194-220, Sept. 2017, doi: 10.1109/TIV.2017.2749181.
L. M. Paz, J. D. TardÓs and J. Neira, "Divide and Conquer: EKF SLAM in $O(n)$," in IEEE Transactions on Robotics, vol. 24, no. 5, pp. 1107-1120, Oct. 2008, doi: 10.1109/TRO.2008.2004639.
Z. Zhang and Y. Shan, "Incremental motion estimation through modified bundle adjustment," Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), Barcelona, Spain, 2003, pp. II-343, doi: 10.1109/ICIP.2003.1246687.
Michot, Julien, Adrien Bartoli, and François Gaspard. "Bi-objective bundle adjustment with application to multi-sensor slam." 3DPVT’10 3025 (2010).
R. W. Wolcott and R. M. Eustice, "Visual localization within LIDAR maps for automated urban driving," 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA, 2014, pp. 176-183, doi: 10.1109/IROS.2014.6942558.
McCall, Joel C., and Mohan M. Trivedi. "Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation." IEEE transactions on intelligent transportation systems 7.1 (2006): 20-37.
Zou, Zhengxia, et al. "Object detection in 20 years: A survey." Proceedings of the IEEE (2023).
Z. Zou, K. Chen, Z. Shi, Y. Guo and J. Ye, "Object Detection in 20 Years: A Survey," in Proceedings of the IEEE, vol. 111, no. 3, pp. 257-276, March 2023, doi: 10.1109/JPROC.2023.3238524.
S. -C. Hsu, C. -L. Huang and C. -H. Chuang, "Vehicle detection using simplified fast R-CNN," 2018 International Workshop on Advanced Image Technology (IWAIT), Chiang Mai, Thailand, 2018, pp. 1-3, doi: 10.1109/IWAIT.2018.8369767.
Redmon, Joseph, et al. "You only look once: Unified, real-time object detection." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
Lee, Seokju, et al. "Vpgnet: Vanishing point guided network for lane and road marking detection and recognition." Proceedings of the IEEE international conference on computer vision. 2017.
J. Li, X. Mei, D. Prokhorov and D. Tao, "Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene," in IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 3, pp. 690-703, March 2017, doi: 10.1109/TNNLS.2016.2522428.
A. Azim and O. Aycard, "Detection, classification and tracking of moving objects in a 3D environment," 2012 IEEE Intelligent Vehicles Symposium, Madrid, Spain, 2012, pp. 802-807, doi: 10.1109/IVS.2012.6232303.
S. Hwang, N. Kim, Y. Choi, S. Lee and I. S. Kweon, "Fast multiple objects detection and tracking fusing color camera and 3D LIDAR for intelligent vehicles," 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Xi'an, China, 2016, pp. 234-239, doi: 10.1109/URAI.2016.7625744.
Russell, Rebecca L., and Christopher Reale. "Multivariate uncertainty in deep learning." IEEE Transactions on Neural Networks and Learning Systems 33.12 (2021): 7937-7943.
Russell, Rebecca L., and Christopher Reale. "Multivariate uncertainty in deep learning." IEEE Transactions on Neural Networks and Learning Systems 33.12 (2021): 7937-7943.
Yurtsever, Ekim, et al. "Risky action recognition in lane change video clips using deep spatiotemporal networks with segmentation mask transfer." 2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE, 2019.
Kumar, Puneet, et al. "Learning-based approach for online lane change intention prediction." 2013 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2013.
Kumar, Puneet, et al. "Learning-based approach for online lane change intention prediction." 2013 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2013.
Dörr, Dominik, David Grabengiesser, and Frank Gauterin. "Online driving style recognition using fuzzy logic." 17th international IEEE conference on intelligent transportation systems (ITSC). IEEE, 2014.
Doshi, Anup, and Mohan M. Trivedi. "Examining the impact of driving style on the predictability and responsiveness of the driver: Real-world and simulator analysis." 2010 IEEE Intelligent Vehicles Symposium. IEEE, 2010.
Corti, Andrea, et al. "Quantitative driving style estimation for energy-oriented applications in road vehicles." 2013 IEEE International Conference on Systems, Man, and Cybernetics. IEEE, 2013.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
