{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T16:39:32Z","timestamp":1726850372067},"reference-count":54,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,9]],"date-time":"2020-09-09T00:00:00Z","timestamp":1599609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"One major concern in the development of intelligent vehicles is to improve the driving safety. It is also an essential issue for future autonomous driving and intelligent transportation. In this paper, we present a vision-based system for driving assistance. A front and a rear on-board camera are adopted for visual sensing and environment perception. The purpose is to avoid potential traffic accidents due to forward collision and vehicle overtaking, and assist the drivers or self-driving cars to perform safe lane change operations. The proposed techniques consist of lane change detection, forward collision warning, and overtaking vehicle identification. A new cumulative density function (CDF)-based symmetry verification method is proposed for the detection of front vehicles. The motion cue obtained from optical flow is used for overtaking detection. It is further combined with a convolutional neural network to remove repetitive patterns for more accurate overtaking vehicle identification. Our approach is able to adapt to a variety of highway and urban scenarios under different illumination conditions. The experiments and performance evaluation carried out on real scene images have demonstrated the effectiveness of the proposed techniques.<\/jats:p>","DOI":"10.3390\/s20185139","type":"journal-article","created":{"date-parts":[[2020,9,9]],"date-time":"2020-09-09T13:01:09Z","timestamp":1599656469000},"page":"5139","source":"Crossref","is-referenced-by-count":33,"title":["A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-6476-6625","authenticated-orcid":false,"given":"Huei-Yung","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Advanced Institute of Manufacturing with High-Tech Innovation, National Chung Cheng University, Chiayu 621, Taiwan"}]},{"given":"Jyun-Min","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Chung Cheng University, Chiayi 621, Taiwan"}]},{"given":"Lu-Ting","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Chung Cheng University, Chiayi 621, Taiwan"}]},{"given":"Li-Qi","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Chung Cheng University, Chiayi 621, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MCE.2018.2828440","article-title":"Advanced Driver-Assistance Systems: A Path Toward Autonomous Vehicles","volume":"7","author":"Kukkala","year":"2018","journal-title":"IEEE Consum. Electron. Mag."},{"key":"ref_2","unstructured":"MOTC (2020, July 13). Taiwan Area National Freeway Bureau, Available online: http:\/\/www.freeway.gov.tw\/."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Su, C., Deng, W., Sun, H., Wu, J., Sun, B., and Yang, S. (2017, January 11\u201314). Forward collision avoidance systems considering driver\u2019s driving behavior recognized by Gaussian Mixture Model. Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA.","DOI":"10.1109\/IVS.2017.7995773"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5151","DOI":"10.1109\/JSEN.2018.2832291","article-title":"Lane Detection and Classification for Forward Collision Warning System Based on Stereo Vision","volume":"18","author":"Song","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4422","DOI":"10.1109\/TVT.2014.2369522","article-title":"Personalized Driver\/Vehicle Lane Change Models for ADAS","volume":"64","author":"Butakov","year":"2015","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Liu, G., Wang, L., and Zou, S. (2017, January 25\u201326). A radar-based blind spot detection and warning system for driver assistance. Proceedings of the 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China.","DOI":"10.1109\/IAEAC.2017.8054409"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dai, J., Wu, L., Lin, H., and Tai, W. (2016, January 13\u201316). A driving assistance system with vision based vehicle detection techniques. Proceedings of the 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), Jeju, Korea.","DOI":"10.1109\/APSIPA.2016.7820739"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yeh, T., Lin, S., Lin, H., Chan, S., Lin, C., and Lin, Y. (2019, January 3\u20136). Traffic Light Detection using Convolutional Neural Networks and Lidar Data. Proceedings of the 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), Taipei, Taiwan.","DOI":"10.1109\/ISPACS48206.2019.8986310"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1080\/02533839.2019.1708801","article-title":"Improved traffic sign recognition for in-car cameras","volume":"43","author":"Lin","year":"2020","journal-title":"J. Chin. Inst. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.cviu.2019.03.001","article-title":"A survey of advances in vision-based vehicle re-identification","volume":"182","author":"Khan","year":"2019","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1016\/j.imavis.2007.04.004","article-title":"Vehicle speed detection from a single motion blurred image","volume":"26","author":"Lin","year":"2008","journal-title":"Image Vis. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.neucom.2018.01.092","article-title":"Computer vision and deep learning techniques for pedestrian detection and tracking: A survey","volume":"300","author":"Brunetti","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/MITS.2017.2743165","article-title":"Ready for Take-Over? A New Driver Assistance System for an Automated Classification of Driver Take-Over Readiness","volume":"9","author":"Braunagel","year":"2017","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.imavis.2017.07.002","article-title":"Computer vision in automated parking systems: Design, implementation and challenges","volume":"68","author":"Heimberger","year":"2017","journal-title":"Image Vis. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tang, Z., Wang, G., Xiao, H., Zheng, A., and Hwang, J.N. (2018, January 18\u201322). Single-Camera and Inter-Camera Vehicle Tracking and 3D Speed Estimation Based on Fusion of Visual and Semantic Features. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00022"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Nguyen, N.T., Iliadis, L., Manolopoulos, Y., and Trawi\u0144ski, B. (2016). A Survey of ADAS Technologies for the Future Perspective of Sensor Fusion. Computational Collective Intelligence, Springer International Publishing.","DOI":"10.1007\/978-3-319-45243-2"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.patcog.2015.12.010","article-title":"Robust Lane Detection using Two-stage Feature Extraction with Curve Fitting","volume":"59","author":"Niu","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lu, Y., Huang, J., Chen, Y., and Heisele, B. (2017, January 11\u201314). Monocular localization in urban environments using road markings. Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA.","DOI":"10.1109\/IVS.2017.7995762"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1049\/iet-its.2018.5256","article-title":"Dynamic integration and online evaluation of vision-based lane detection algorithms","volume":"13","author":"Xing","year":"2019","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.patcog.2017.08.014","article-title":"A review of recent advances in lane detection and departure warning system","volume":"73","author":"Narote","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bruls, T., Porav, H., Kunze, L., and Newman, P. (2019, January 9\u201312). The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.","DOI":"10.1109\/IVS.2019.8814056"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ying, Z., and Li, G. (2016, January 20\u201325). Robust lane marking detection using boundary-based inverse perspective mapping. Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China.","DOI":"10.1109\/ICASSP.2016.7472011"},{"key":"ref_23","unstructured":"Long, S., and Dhillon, B.S. (2020). Lane Detection Algorithm Based on Inverse Perspective Mapping. Man\u2014Machine\u2014Environment System Engineering, Springer."},{"key":"ref_24","unstructured":"Kluge, K., and Lakshmanan, S. (1995, January 25\u201326). A deformable-template approach to lane detection. Proceedings of the Intelligent Vehicles \u201995. Symposium, Detroit, MI, USA."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Deng, Y., Liang, H., Wang, Z., and Huang, J. (2014, January 5\u201310). An integrated forward collision warning system based on monocular vision. Proceedings of the 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014), Bali, Indonesia.","DOI":"10.1109\/ROBIO.2014.7090499"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Song, W., Fu, M., Yang, Y., Wang, M., Wang, X., and Kornhauser, A. (2017, January 11\u201314). Real-time lane detection and forward collision warning system based on stereo vision. Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA.","DOI":"10.1109\/IVS.2017.7995766"},{"key":"ref_27","unstructured":"Di, Z., and He, D. (2016, January 10\u201312). Forward Collision Warning system based on vehicle detection and tracking. Proceedings of the 2016 International Conference on Optoelectronics and Image Processing (ICOIP), Warsaw, Poland."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Baek, J.W., Han, B., Kang, H., Chung, Y., and Lee, S. (2016, January 5\u20138). Fast and reliable tracking algorithm for on-road vehicle detection systems. Proceedings of the 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN), Vienna, Austria.","DOI":"10.1109\/ICUFN.2016.7536983"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Moujahid, A., ElAraki Tantaoui, M., Hina, M.D., Soukane, A., Ortalda, A., ElKhadimi, A., and Ramdane-Cherif, A. (2018, January 22\u201323). Machine Learning Techniques in ADAS: A Review. Proceedings of the 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE), Paris, France.","DOI":"10.1109\/ICACCE.2018.8441758"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Fekri, P., Abedi, V., Dargahi, J., and Zadeh, M. (2020). A Forward Collision Warning System Using Deep Reinforcement Learning, SAE International. SAE Technical Paper.","DOI":"10.4271\/2020-01-0138"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Nur, S.A., Ibrahim, M.M., Ali, N.M., and Nur, F.I.Y. (2016, January 25\u201327). Vehicle detection based on underneath vehicle shadow using edge features. Proceedings of the 2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), Batu Ferringhi, Malaysia.","DOI":"10.1109\/ICCSCE.2016.7893608"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zebbara, K., El Ansari, M., Mazoul, A., and Oudani, H. (2019, January 3\u20134). A Fast Road Obstacle Detection Using Association and Symmetry recognition. Proceedings of the 2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), Fez, Morocco.","DOI":"10.1109\/WITS.2019.8723741"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ma, X., and Sun, X. (2017, January 11\u201313). Detection and segmentation of occluded vehicles based on symmetry analysis. Proceedings of the 2017 4th International Conference on Systems and Informatics (ICSAI), Hangzhou, China.","DOI":"10.1109\/ICSAI.2017.8248385"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lim, Y., and Kang, M. (2014, January 1\u20133). Stereo vision-based visual tracking using 3D feature clustering for robust vehicle tracking. Proceedings of the 2014 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO), Vienna, Austri.","DOI":"10.5220\/0005147807880793"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lai, Y., Huang, Y., and Hwang, C. (2016, January 7\u201311). Front moving object detection for car collision avoidance applications. Proceedings of the 2016 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE.2016.7430650"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, D., Wang, J., Chen, C., and Chen, Y. (2011, January 10\u201313). Video-based intelligent vehicle contextual information extraction for night conditions. Proceedings of the 2011 International Conference on Machine Learning and Cybernetics, Guilin, China.","DOI":"10.1109\/ICMLC.2011.6017010"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hultqvist, D., Roll, J., Svensson, F., Dahlin, J., and Sch\u00f6n, T.B. (2014, January 8\u201311). Detecting and positioning overtaking vehicles using 1D optical flow. Proceedings of the 2014 IEEE Intelligent Vehicles Symposium Proceedings, Dearborn, MI, USA.","DOI":"10.1109\/IVS.2014.6856447"},{"key":"ref_38","unstructured":"Chen, Y., and Wu, Q. (2015, January 15\u201317). Moving vehicle detection based on optical flow estimation of edge. Proceedings of the 2015 11th International Conference on Natural Computation (ICNC), Zhangjiajie, China."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2736","DOI":"10.1109\/TVT.2008.917220","article-title":"Lane-Change Decision Aid System Based on Motion-Driven Vehicle Tracking","volume":"57","author":"Rotter","year":"2008","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_40","first-page":"506235","article-title":"A real-time embedded blind spot safety assistance system","volume":"2012","author":"Wu","year":"2012","journal-title":"Int. J. Veh. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014). Extended Lucas-Kanade Tracking. Computer Vision\u2014ECCV 2014, Springer International Publishing.","DOI":"10.1007\/978-3-319-10578-9"},{"key":"ref_42","unstructured":"Pereira, F., Burges, C.J.C., Bottou, L., and Weinberger, K.Q. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25, Curran Associates, Inc."},{"key":"ref_43","unstructured":"Shi, J. (1994, January 21\u201323). Good features to track. Proceedings of the 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1109\/LRA.2017.2660543","article-title":"Lane-Change Detection Based on Vehicle-Trajectory Prediction","volume":"2","author":"Woo","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1177\/154193120504902217","article-title":"Using Support Vector Machines for Lane-Change Detection","volume":"49","author":"Mandalia","year":"2005","journal-title":"Proc. Hum. Factors Ergon. Soc. Annu. Meet."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Schlechtriemen, J., Wedel, A., Hillenbrand, J., Breuel, G., and Kuhnert, K. (2014, January 8\u201311). A lane change detection approach using feature ranking with maximized predictive power. Proceedings of the 2014 IEEE Intelligent Vehicles Symposium Proceedings, Dearborn, MI, USA.","DOI":"10.1109\/IVS.2014.6856491"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Aly, M. (2008, January 4\u20136). Real time detection of lane markers in urban streets. Proceedings of the 2008 IEEE Intelligent Vehicles Symposium, Eindhoven, The Netherlands.","DOI":"10.1109\/IVS.2008.4621152"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1049\/iet-its.2017.0143","article-title":"Lane detection method based on lane structural analysis and CNNs","volume":"12","author":"Ye","year":"2018","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Shiru, Q., and Xu, L. (2016, January 28\u201330). Research on multi-feature front vehicle detection algorithm based on video image. Proceedings of the 2016 Chinese Control and Decision Conference (CCDC), Yinchuan, China.","DOI":"10.1109\/CCDC.2016.7531653"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chen, C., Chen, T., Huang, D., and Feng, K. (2015, January 18\u201320). Front Vehicle Detection and Distance Estimation Using Single-Lens Video Camera. Proceedings of the 2015 Third International Conference on Robot, Vision and Signal Processing (RVSP), Kaohsiung, Taiwan.","DOI":"10.1109\/RVSP.2015.12"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.trc.2014.10.009","article-title":"Vehicle detection based on And\u2013Or Graph and Hybrid Image Templates for complex urban traffic conditions","volume":"51","author":"Li","year":"2015","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Yang, B., Zhang, S., Tian, Y., and Li, B. (2019). Front-Vehicle Detection in Video Images Based on Temporal and Spatial Characteristics. Sensors, 19.","DOI":"10.3390\/s19071728"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Chen, J., Chen, Y., and Chuang, C. (2019, January 20\u201322). Overtaking Vehicle Detection Based on Deep Learning and Headlight Recognition. Proceedings of the 2019 IEEE International Conference on Consumer Electronics\u2014Taiwan (ICCE-TW), YILAN, Taiwan.","DOI":"10.1109\/ICCE-TW46550.2019.8991755"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Tseng, C., Liao, C., Shen, P., and Guo, J. (2019, January 22\u201325). Using C3D to Detect Rear Overtaking Behavior. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8802963"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5139\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T02:20:04Z","timestamp":1719973204000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5139"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,9]]},"references-count":54,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20185139"],"URL":"https:\/\/doi.org\/10.3390\/s20185139","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,9]]}}}