다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기
Sustainability, v.13 no.20, 2021년, pp.11417 -
Waykole, Swapnil , Shiwakoti, Nirajan , Stasinopoulos, Peter
AI-Helper
Autonomous vehicles and advanced driver assistance systems are predicted to provide higher safety and reduce fuel and energy consumption and road traffic emissions. Lane detection and tracking are the advanced key features of the advanced driver assistance system. Lane detection is the process of de...
Nilsson Shakey the Robot 1984
Thorpe, C., Herbert, M., Kanade, T., Shafter, S.. Toward autonomous driving: the CMU Navlab. II. Architecture and systems. IEEE expert, vol.6, no.4, 44-52.
Horowitz, R., Varaiya, P.. Control design of an automated highway system. Proceedings of the IEEE, vol.88, no.7, 913-925.
Pomerleau, D., Jochem, T.. Rapidly adapting machine vision for automated vehicle steering. IEEE expert, vol.11, no.2, 19-27.
Parent, Michel. Advanced Urban Transport: Automation Is on the Way. IEEE intelligent systems, vol.22, no.2, 9-11.
Lari Self-Driving Vehicles and Policy Implications: Current Status of Autonomous Vehicle Development and Minnesota Policy Implications Minn. J. Law Sci. Technol. 2015 16 735
Green Lights for Our Self-Driving Vehicle Prototypes https://blog.google/alphabet/self-driving-vehicle-prototypes-on-road/
Campisi, Tiziana, Severino, Alessandro, Al-Rashid, Muhammad Ahmad, Pau, Giovanni. The Development of the Smart Cities in the Connected and Autonomous Vehicles (CAVs) Era: From Mobility Patterns to Scaling in Cities. Infrastructures, vol.6, no.7, 100-.
Severino, Alessandro, Curto, Salvatore, Barberi, Salvatore, Arena, Fabio, Pau, Giovanni. Autonomous Vehicles: An Analysis Both on Their Distinctiveness and the Potential Impact on Urban Transport Systems. Applied sciences, vol.11, no.8, 3604-.
10.1109/IVS.2008.4621152
Bar Hillel, Aharon, Lerner, Ronen, Levi, Dan, Raz, Guy. Recent progress in road and lane detection: a survey. Machine vision and applications, vol.25, no.3, 727-745.
Machine Learning Based Approach https://www.sciencedirect.com/topics/computer-science/machine-learning-based-approach
10.12720/joace.1.1.31-35
Kukkala, Vipin Kumar, Tunnell, Jordan, Pasricha, Sudeep, Bradley, Thomas. Advanced Driver-Assistance Systems: A Path Toward Autonomous Vehicles. IEEE consumer electronics magazine, vol.7, no.5, 18-25.
Yenkanchi Multi Sensor Data Fusion for Autonomous Vehicles 2016
Synopsys.com. What Is ADAS (Advanced Driver Assistance Systems)?-Overview of ADAS Applications|Synopsys https://www.synopsys.com/automotive/what-is-adas.html
McCall, J.C., Trivedi, M.M.. Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation. IEEE transactions on intelligent transportation systems : a publication of the IEEE Intelligent Transportation Systems Council, vol.7, no.1, 20-37.
Kuo, C.Y., Lu, Y.R., Yang, S.M.. On the Image Sensor Processing for Lane Detection and Control in Vehicle Lane Keeping Systems. Sensors, vol.19, no.7, 1665-.
Kang, Chang Mook, Lee, Seung-Hi, Kee, Seok-Cheol, Chung, Chung Choo. Kinematics-based Fault-tolerant Techniques: Lane Prediction for an Autonomous Lane Keeping System. International Journal of Control, Automation and Systems, vol.16, no.3, 1293-1302.
10.1109/ICIP.2009.5413980
Sun, Yi, Li, Jian, Sun, Zhenping. Multi-Stage Hough Space Calculation for Lane Markings Detection via IMU and Vision Fusion. Sensors, vol.19, no.10, 2305-.
10.1117/12.458518
10.1109/IVS.2009.5164335
10.1109/CIVVS.2009.4938723
10.1109/ICACCS.2019.8728305
Park Implementation of Lane Detection Algorithm for Self-driving Vehicles Using Tensor Flow International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing 2018 438
El Hajjouji, Ismaïl, Mars, Salah, Asrih, Zakariae, El Mourabit, Aimad. A novel FPGA implementation of Hough Transform for straight lane detection. JESTECH : Engineering science and technology, an international journal, vol.23, no.2, 274-280.
Samadzadegan Automatic Lane Detection in Image Sequences for Vision-based Navigation Purposes ISPRS Image Eng. Vis. Metrol. 2006
Cheng, H.-Y., Yu, C.-C., Tseng, C.-C., Fan, K.-C., Hwang, J.-N., Jeng, B.-S.. Environment classification and hierarchical lane detection for structured and unstructured roads. IET computer vision, vol.4, no.1, 37-49.
Han, J., Kim, D., Lee, M., Sunwoo, M.. Road boundary detection and tracking for structured and unstructured roads using a 2D lidar sensor. International journal of automotive technology, vol.15, no.4, 611-623.
10.1007/978-3-319-16865-4_27
10.1109/WorldS4.2019.8904020
Lee, Chanho, Moon, Ji-Hyun. Robust Lane Detection and Tracking for Real-Time Applications. IEEE transactions on intelligent transportation systems : a publication of the IEEE Intelligent Transportation Systems Council, vol.19, no.12, 4043-4048.
Son, Yeongho, Lee, Elijah S., Kum, Dongsuk. Robust multi-lane detection and tracking using adaptive threshold and lane classification. Machine vision and applications, vol.30, no.1, 111-124.
Li, Qingquan, Zhou, Jian, Li, Bijun, Guo, Yuan, Xiao, Jinsheng. Robust Lane-Detection Method for Low-Speed Environments. Sensors, vol.18, no.12, 4274-.
Son, J., Yoo, H., Kim, S., Sohn, K.. Real-time illumination invariant lane detection for lane departure warning system. Expert systems with applications, vol.42, no.4, 1816-1824.
10.1109/ITSC.2018.8569778
Lu, Zefeng, Xu, Ying, Shan, Xin, Liu, Licai, Wang, Xingzheng, Shen, Jianhao. A Lane Detection Method Based on a Ridge Detector and Regional G-RANSAC. Sensors, vol.19, no.18, 4028-.
Bian, Yougang, Ding, Jieyun, Hu, Manjiang, Xu, Qing, Wang, Jianqiang, Li, Keqiang. An Advanced Lane-Keeping Assistance System With Switchable Assistance Modes. IEEE transactions on intelligent transportation systems : a publication of the IEEE Intelligent Transportation Systems Council, vol.21, no.1, 385-396.
Wang Cooperative Lane Changing via Deep Reinforcement Learning arXiv 2019
Suh, Jongsang, Chae, Heungseok, Yi, Kyongsu. Stochastic Model-Predictive Control for Lane Change Decision of Automated Driving Vehicles. IEEE transactions on vehicular technology, vol.67, no.6, 4771-4782.
Gopalan, Raghuraman, Tsai Hong, Shneier, Michael, Chellappa, Rama. A Learning Approach Towards Detection and Tracking of Lane Markings. IEEE transactions on intelligent transportation systems : a publication of the IEEE Intelligent Transportation Systems Council, vol.13, no.3, 1088-1098.
Method for Lane Detection. US20170068862A1 https://patents.google.com/patent/US20170068862A1/en
Method for Generating Accurate Lane Level Maps. US9384394B2 https://patents.google.com/patent/US9384394B2/en
Lane Tracking Control System for Vehicle. US20020095246A1 https://patents.google.com/patent/US20020095246
Lane Detection Sensor and Navigation System Employing the Same. EP1143398A3 https://patents.google.com/patent/EP1143398A3/en
Vehicle Detecting Method Based on Multi-Target Tracking and Cascade Classifier Combination. CN105205500A https://patents.google.com/patent/CN105205500A/en
Steering Support Device. JP6589941B2, 2019. Patentimages.storage.googleapis.com https://patentimages.storage.googleapis.com/0b/d0/ff/978af5acfb7b35/JP6589941B2.pdf
Lane Detection Systems and Methods. US10336326B2 https://patents.google.com/patent/US10336326B2/en
Enhanced Perspective View Generation in a Front Curb Viewing System Abstract. US9834143B2 https://patents.google.com/patent/US9834143B2/en
Object Detection for an Autonomous Vehicle. US20170323179A1 https://patents.google.com/patent/US20170323179A1/en
Cu Lane Dataset https://xingangpan.github.io/projects/CULane.html
Caltech Pedestrian Detection Benchmark http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/
Digital Image Media Lab. Diml.yonsei.ac.kr http://diml.yonsei.ac.kr/dataset/
Cvlibs.net. The KITTI Vision Benchmark Suite http://www.cvlibs.net/datasets/kitti/
Tusimple/Tusimple-Benchmark https://github.com/TuSimple/tusimple-benchmark/tree/master/doc/velocity_estimation
BDD100K Dataset https://mc.ai/bdd100k-dataset/
10.5121/ijcsit.2015.7406
Hamed Computer and Information Security Handbook 2017 114
Precision and Recall https://en.wikipedia.org/wiki/Precision_and_recall
Fiorentini, Nicholas, Losa, Massimo. Long-Term-Based Road Blackspot Screening Procedures by Machine Learning Algorithms. Sustainability, vol.12, no.15, 5972-.
Shinq-Jen Wu, Hsin-Han Chiang, Jau-Woei Perng, Chao-Jung Chen, Bing-Fei Wu, Tsu-Tian Lee. The Heterogeneous Systems Integration Design and Implementation for Lane Keeping on a Vehicle. IEEE transactions on intelligent transportation systems : a publication of the IEEE Intelligent Transportation Systems Council, vol.9, no.2, 246-263.
Liu, Hongzhe, Li, Xuewei. Notice of Retraction: Sharp Curve Lane Detection for Autonomous Driving. Computing in science & engineering, vol.21, no.2, 80-95.
Han, Jiaming, Yang, Zhong, Hu, Guoxiong, Zhang, Tianyi, Song, Jiarong. Accurate and Robust Vanishing Point Detection Method in Unstructured Road Scenes. Journal of intelligent & robotic systems, vol.94, no.1, 143-158.
10.4271/2019-01-0685 GNSS Based Lane Keeping Assist System via Model Predictive Control https://doi.org/10.4271/2019-01-0685
10.1109/IVS.2019.8813778
10.1109/CCCM.2008.362
Nieuwenhuijsen, Jurgen, Correia, Gonçalo Homem de Almeida, Milakis, Dimitris, van Arem, Bart, van Daalen, Els. Towards a quantitative method to analyze the long-term innovation diffusion of automated vehicles technology using system dynamics. Transportation research. Part C, Emerging technologies, vol.86, 300-327.
Stasinopoulos, Peter, Shiwakoti, Nirajan, Beining, Marvin. Use-stage life cycle greenhouse gas emissions of the transition to an autonomous vehicle fleet: A System Dynamics approach. Journal of cleaner production, vol.278, 123447-.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
저자가 공개 리포지터리에 출판본, post-print, 또는 pre-print를 셀프 아카이빙 하여 자유로운 이용이 가능한 논문
Copyright KISTI. All Rights Reserved.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.