US 7409295B2
· Paradie
· 2008
[cited by applicant]
US 8204542B2
· Liao et al.
· 2012
[cited by applicant]
US 8204642B2
· Tanaka et al.
· 2012
[cited by applicant]
US 9349076B1
· Liu et al.
· 2016
[cited by applicant]
US 9373057B1
· Erhan et al.
· 2016
[cited by applicant]
US 9701307B1
· Newman et al.
· 2017
[cited by applicant]
US 9710714B2
· Chen et al.
· 2017
[cited by applicant]
US 9742869B2
· Bolotin et al.
· 2017
[cited by applicant]
US 10108867B1
· Vallespi-Gonzalez et al.
· 2018
[cited by applicant]
US 20180136332A1
· Barfield, Jr. et al.
· 2018
[cited by applicant]
US 20180158244A1
· Ybanez Zepeda et al.
· 2018
[cited by applicant]
US 20180203959A1
· Refsnaes et al.
· 2018
[cited by applicant]
US 20180349746A1
· Vellespi-Gonzalez
· 2018
[cited by applicant]
US 20180373980A1
· Huval
· 2018
[cited by applicant]
US 20190071101A1
· Emura et al.
· 2019
[cited by applicant]
US 20190129831A1
· Goldberg
· 2019
[cited by applicant]
US 20190179979A1
· Melick
· 2019
[cited by applicant]
US 20250172665A1
· Koivisto et al.
· 2025
[cited by applicant]
US 20250172666A1
· Koivisto et al.
· 2025
[cited by applicant]
CN 106980871A
· 2017
[cited by applicant]
DE 102015221920A1
· 2017
[cited by applicant]
DE 102015226762A1
· 2017
[cited by applicant]
EP 1930863A2
· 2008
[cited by applicant]
EP 1930868A1
· 2008
[cited by applicant]
EP 2384009A2
· 2011
[cited by applicant]
KR 1020120009590A
· 2012
[cited by applicant]
WO 2012011713A2
· 2012
[cited by applicant]
WO 2016183074A1
· 2016
[cited by applicant]
WO 2017177128A1
· 2017
[cited by applicant]
WO 2017220705A1
· 2017
[cited by applicant]
WO 2018002910A1
· 2018
[cited by applicant]
WO 2018102717A1
· 2018
[cited by applicant]
“OpenCV: Cascade Classifier”, Retrieved from Internet URL : https://docs.opencv.org/3.4/db/d28/tutorial_cascade_classifier.html, accessed on Feb. 17, 2022, pp. 5.
[cited by applicant]
“Sklearn.cluster.DBSCAN”, Density-based spatial clustering of applications with noise (DBSCAN), scikit-learn developers, Retrieved from Internet URL :https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBS…
[cited by applicant]
Boland, P. J., “Majority Systems and the Condorcet Jury Theorem”, Journal of the Royal Statistical Society, Series D (The Statistician), vol. 38, No. 3., pp. 181-189 (Jan. 1989).
[cited by applicant]
Cai, Z., et al., “A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection”, arXiv:1607.07155v1, pp. 1-16 (Jul. 25, 2016).
[cited by applicant]
Kendall, A, et al., “Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics”, arXiv:1705.07115v3, pp. 1-14 (2018).
[cited by applicant]
Redmon, J., et al., “You Only Look Once: Unified, Real-Time Object Detection”, arXiv:1506.02640v5, pp. 1-10 (May 9, 2016).
[cited by applicant]
Rosebrock, A., “(Faster) Non-Maximum Suppression in Python”, PylmageSearch, Retrieved from Internet URL : https://www.pyimagesearch.com/2015/02/16/faster-non-maximum-suppression-python/, accessed on Feb. 17, 2022, pp. 3…
[cited by applicant]
Koivisto, Tommi; Notice of Allowance for U.S. Appl. No. 17/456,0415, filed Nov. 22, 2021, mailed Oct. 3, 2023, 14 pgs.
[cited by applicant]
Koivisto, Tommi; Second Office Action for Chinese Patent Application No. 201980004563.9, Mar. 17, 2020, Oct. 11, 2023, 6 pgs.
[cited by applicant]
He, et al.; (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
[cited by applicant]
ISO 26262, “Road vehicle—Functional safety,” International standard for functional safety of electronic system, https://en.wikipedia.org/wiki/ISO_26262, accessed on Sep. 13, 2021, 8 pgs.
[cited by applicant]
Final Office Action dated Dec. 17, 2021 in U.S. Appl. No. 16/355,328, 17 pgs.
[cited by applicant]
Asvadi, A., et al., “DepthCN: Vehicle Detection Using 3D-LIDAR and ConvNet”, International Conference on Intelligent Transportation Systems (ITSC), IEEE, pp. 1-6 (Oct. 16, 2017), XP033330533.
[cited by applicant]
Stein, G.P., et al., “Vision-Based ACC With A Single Camera: Bounds On Range And Range Rate Accuracy”, Proceedings of IEEE Intelligent Vehicle Symposium, pp. 1-6 (2003).
[cited by applicant]
Tateno, K., et al., “CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction”, Arxiv.Org, Cornell University Library, pp. 6243-6252 (Apr. 11, 2017).
[cited by applicant]
Everingham, M., et al., “The Pascal Visual Object Classes (voc) Challenge”, Int. J. Comput. Vision, 88(2):303-338, Jun. 2010, 36 pgs.
[cited by applicant]
Ren, S., et al., “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”; https://arxiv.org/abs/1506.01497; Jan. 6, 2016; 14 pgs.
[cited by applicant]
Szegedy, C., et al., “Going Deeper with Convolutions”, https://arxiv.org/abs/1409.4842; Sep. 17, 2014, 12 pgs.
[cited by applicant]
Zhong, Yiran et al; “Self-Supervised Learning for Stereo Matching with Self-Improving Ability”, Arxiv.org, Cornell University Library, 201 Olin Library Cornell University Ithaca, NY 14853, Sep. 4, 2017. 13 pgs.
[cited by applicant]
International Search Report and Written Opinion mailed Jul. 25, 2019 in International Patent Application No. PCT/US2019/018348, 22 pgs.
[cited by applicant]
“Euler spiral”, Wikipedia, Retrieved from Internet URL : https://en.wikipedia.org/wiki/Euler_spiral, accessed on Feb. 21, 2019, pp. 10.
[cited by applicant]
“F1 score”, Wikipedia, Retrieved from Internet URL : https://en.wikipedia.org/wiki/F-score, accessed on Feb. 21, 2019, pp. 3.
[cited by applicant]
“Polynomial curve fitting”, Retrieved from Internet URL : https://www.mathworks.com/help/matlab/ref/polyfit.html, accessed on Feb. 21, 2019, pp. 13.
[cited by applicant]
“Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles”, Society of Automotive Engineers (SAE), Standard No. J3016-201609, pp. 30 (Sep. 30, 2016).
[cited by applicant]
“Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles”, Society of Automotive Engineers (SAE), Standard No. J3016-201806, pp. 35 (Jun. 15, 2018).
[cited by applicant]
What is polyline?, Webopedia Definition, Retrieved from Internet URL : https://www.webopedia.com/TERM/P/polyline.html, accessed on Feb. 21, 2019, pp. 4.
[cited by applicant]
Bach, M., et al., “Multi-camera traffic light recognition using a classifying Labeled Multi-Bernoulli filter”, IEEE Intelligent Vehicles Symposium (IV), pp. 1045-1051 (Jun. 2017).
[cited by applicant]
Bidlack, C., et al., “Visual Robot Navigation using Flat Earth Obstacle Projection”, Proceedings Of The IEEE International Conference On Robotics And Automation, pp. 3374-3381 (1994).
[cited by applicant]
Bojarski, M., et al.,“End To End Learning For Self-Driving Cars”, arXiv: 1604.07316v1 [cs.CV], XP055570062, Retrieved from the Internet URL:https://nvidia.com/content/tegra/automotive/images/2016/solutions/pdf/end-to-en…
[cited by applicant]
Chilamkurthy, S., “A 2017 Guide to Semantic Segmentation with Deep Learning”, Qure.ai Blog, Retrieved from Internet URL : http://blog.qure.ai/notes/semantic-segmentation-deep-learning-review, accessed on Feb. 21, 2019, …
[cited by applicant]
Deshpande, A., “A Beginner's Guide to Understanding Convolutional Neural Networks”, accessed at: https://adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/, Accessed on Feb. 21, 201…
[cited by applicant]
Dipietro, R., “A Friendly Introduction to Cross-Entropy Loss,” Version 0.1, Accessed on Feb. 21, 2019 at: https://rdipietro.github.io/friendly-intro-to-cross-entropy-loss/, pp. 1-10 (May 2, 2016).
[cited by applicant]
Garnett, N., et al., “Real-Time Category-Based and General Obstacle Detection for Autonomous Driving”, IEEE International Conference on Computer Vision Workshops, pp. 198-205 (2017).
[cited by applicant]
Godard, C., et al., “Unsupervised Monocular Depth Estimation with Left-Right Consistency”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 270-279 (2017).
[cited by applicant]
He, L., et al., “Learning Depth from Single Images with Deep Neural Network Embedding Focal Length”, Cornell University Library, pp. 1-14 (Mar. 27, 2018).
[cited by applicant]
Huval, B. et al., “An Empirical Evaluation of Deep Learning on Highway Driving”, Apr. 17, 2015, 7 pages. Available at: https://arxiv.org/pdf/1504.01716.pdf.
[cited by applicant]
Ioffe, S., and Szegedy, C., “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift”, arXiv:1502.03167v3 [cs.LG], pp. 1-12 (Mar. 2, 2015), Available at: https://arxiv.org/abs/1502.0…
[cited by applicant]
Jayaraman, A., et al., “Creating 3D Virtual Driving Environments for Simulation-Aided Development of Autonomous Driving and Active Safety”, SAE Technical Paper, pp. 1-6 (2017).
[cited by applicant]
Kendall, A., et al., “End-to-end Learning of Geometry and Context for Deep Stereo Regression”, Cornell University Library, pp. 66-75 (2017).
[cited by applicant]
Kim, W., S., et al., “Depth Map Coding with Distortion Estimation of Rendered View”, Proceedings of Spie, vol. 7543, pp. 75430B1-75430B10, (2010).
[cited by applicant]
Kingma, D. P., and Ba, J. L., “Adam: A Method for Stochastic Optimization”, published as a conference paper at CLR 2015, arXiv:1412.6980v9 [cs.LG], pp. 1-15 (Jan. 30, 2017).
[cited by applicant]
Liu, H., et al., “Neural Person Search Machines”, IEEE International Conference on Computer Vision (ICCV), pp. 493-501 (2017).
[cited by applicant]
Neven, D., et al., “Towards end-to-end lane detection: an instance segmentation approach”, In 2018 IEEE intelligent vehicles symposium (IV), pp. 7 (2018).
[cited by applicant]
Pang, J., et al., “Cascade Residual Learning: A Two-Stage Convolutional Neural Network for Stereo Matching”, IEEE International Conference on Computer Vision Workshops, pp. 887-895 (2017).
[cited by applicant]
Rothe, R., et al., “Non-maximum Suppression for Object Detection by Passing Messages Between Windows”, ETH Library, pp. 1-17 (2015).
[cited by applicant]
Suorsa, R., E., and Sridhar, B., “A Parallel Implementation of a Multisensor Feature-Based Range-Estimation Method”, IEEE Transactions On Robotics And Automation, pp. 1-34 (1993).
[cited by applicant]
Tao, A., “Detectnet: Deep neural network for object detection in digits”, NVIDIA Developer Blog, Retrieved from Internet URL: https://devblogs.nvidia.com/detectnet-deep-neural-network-object-detection-digits/, accessed …
[cited by applicant]
International Preliminary Report on Patentability received for PCT Application No. PCT/US2019/018348, mailed on Aug. 27, 2020, 16 pages.
[cited by applicant]
Virgo, M., “Lane Detection with Deep Learning (Part 1)”, Accessed on Feb. 22, 2019 at: https://towardsdatascience.com/lane-detection-with-deep-learning-part-1-9e096f3320b7, pp. 1-10 (May 9, 2017).
[cited by applicant]
Weber, M., et al., “DeepTLR: A single deep convolutional network for detection and classification of traffic lights”, IEEE Intelligent Vehicles Symposium (IV), pp. 8 (Jun. 2016).
[cited by applicant]
Xie, S., and Tu, Z., “Holistically-Nested Edge Detection”, Computer Vision Foundation, pp. 1395-1403 (2015).
[cited by applicant]
“Conservative Control for Zone Driving of Autonomous Vehicles Using Safe Time of Arrival”, U.S. Appl. No. 62/628,831, filed Feb. 9, 2018.
[cited by applicant]
“Convolutional Neural Networks to Detect Drivable Freespace for Autonomous Vehicles”, U.S. Appl. No. 62/643,665, filed Mar. 15, 2018.
[cited by applicant]
“Deep Neural Network for Estimating Depth from Stereo Using Semi-Supervised Learning”, U.S. Appl. No. 62/646,148, filed Mar. 21, 2018.
[cited by applicant]
“Distance Based Ambient Occlusion Filter for Denoising Ambient Occlusions”, U.S. Appl. No. 62/644,601, filed Mar. 19, 2018.
[cited by applicant]
“Energy Based Reflection Filter for Denoising Ray-Traced Glossy Reflections”, U.S. Appl. No. 62/644,386, filed Mar. 17, 2018.
[cited by applicant]
“Geometric Shadow Filter for Denoising Ray-Traced Shadows”, U.S. Appl. No. 62/644,385, filed Mar. 17, 2018.
[cited by applicant]
“Methodology of Using a Single Controller (ECU) For a Fault-Tolerant/Fail-Operational Self-Driving System”, U.S. Appl. No. 62/524,283, filed Jun. 23, 2017.
[cited by applicant]
“Methods for Accurate Real-time Lane and Road Boundary Detection for Autonomous Driving”, U.S. Appl. No. 62/636,142, filed Feb. 27, 2018.
[cited by applicant]
“Methods for accurate real-time object detection and for determining confidence of object detection suitable for D autonomous vehicles”, U.S. Appl. No. 62/631,781, filed Feb. 18, 2018.
[cited by applicant]
“Network Injection Rate Limiting”, U.S. Appl. No. 62/648,326, filed Mar. 26, 2018.
[cited by applicant]
“Network Synchronization Using Posted Operation Tracking For Flush Semantics”, U.S. Appl. No. 62/648,333, filed Mar. 26, 2018.
[cited by applicant]
“Programmable Vision Accelerator”, U.S. Appl. No. 15/141,703, filed Apr. 28, 2016.
[cited by applicant]
“Pruning Convolutional Neural Networks for Autonomous Vehicles and Robotics”, U.S. Appl. No. 62/630,445, filed Feb. 14, 2018.
[cited by applicant]
“Reliability Enhancement Systems and Methods” U.S. Appl. No. 15/338,247, filed Oct. 28, 2016.
[cited by applicant]
“System and Method for Autonomous Shuttles, Robo-Taxis, Ride-Sharing and On-Demand Vehicles”, U.S. Appl. No. 62/635,503, filed Feb. 26, 2018.
[cited by applicant]
“System and Method for Controlling Autonomous Vehicles”, U.S. Appl. No. 62/614,466, filed Jan. 17, 2018.
[cited by applicant]
“System and Method for Safe Operation of Autonomous Vehicles”, U.S. Appl. No. 62/625,351, filed Feb. 2, 2018.
[cited by applicant]
“System and Method for Sharing Camera Data Between Primary and Backup Controllers in Autonomous Vehicle Systems”, U.S. Appl. No. 62/629,822, filed Feb. 13, 2018.
[cited by applicant]
“System and Methods for Advanced AI-Assisted Vehicles”, U.S. Appl. No. 62/648,358, filed Mar. 26, 2018.
[cited by applicant]
“Systems and Methods for Safe and Reliable Autonomous Vehicles”, U.S. Appl. No. 62/584,549, filed Nov. 10, 2017.
[cited by applicant]
“Video Prediction Using Spatially Displaced Convolution”, U.S. Appl. No. 62/646,309, filed Mar. 21, 2018.
[cited by applicant]
Adaptive Occlusion Sampling of Rectangular Area Lights with Voxel Cone Tracing, U.S. Appl. No. 62/644,806, filed Mar. 19, 2018.
[cited by applicant]
Ching Y. Hung et al. “Programmable Vision Accelerator”, U.S. Appl. No. 62/156,167, filed May 1, 2015.
[cited by applicant]
“Video Prediction Using Spatially Displaced Convolution”, U.S. Appl. No. 62/647,545, filed Mar. 23, 2018.
[cited by applicant]
Chen, Chenyi; Final Office Action dated Feb. 8, 2022 in U.S. Appl. No. 16/366,875, 20 pgs.
[cited by applicant]
Koivisto, Tommi; Non-Final Office Action for U.S. Appl. No. 17/456,045, filed Nov. 22, 2021, mailed Feb. 2, 2023, 41 pgs.
[cited by applicant]
“What are deconvolutional layers?”, Data Science Stack Exchange, Retrieved from Internet URL: https://datascience.stackexchange.com/questions/6107/what%E2%80%90are%E2%80%90deconvolutional%E280%90layers, accessed on Feb.…
[cited by applicant]
Foley, D., and Danskin, J., “Ultra-Performance Pascal GPU and NVLink Interconnect”, IEEE Computer Society, IEEE Micro, vol. 37, No. 2, pp. 1-11 (2017).
[cited by applicant]
Non-Final Office Action dated May 17, 2021 in U.S. Appl. No. 16/186,473, 13 pgs.
[cited by applicant]
Du, L., and Du, Y., “Hardware Accelerator Design for Machine Learning,” Machine Learning—Advanced Techniques and Emerging Applications, Retrieved from Internet URL: https://www.intechopen.com/chapters/58659, pp. 1-14.
[cited by applicant]
Notice of Allowance dated Dec. 14, 2021 in U.S. Appl. No. 16/728,595, 8 pages.
[cited by applicant]
Ditty, Michael Alan; Final Office Action for U.S. Appl. No. 16/186,473, filed Nov. 9, 2018, mailed Dec. 29, 2021, 22 pgs.
[cited by applicant]
Yang, Yilin; Non-Final Office Action for U.S. Appl. No. 16/728,595, filed Dec. 27, 2019, mailed May 13, 2021, 18 pgs.
[cited by applicant]
Long, et al.; “Fully convolutional networks for semantic segmentation”, CVPR, Nov. 2015, 10 pgs.
[cited by applicant]
Alvarez, et al.; “Road scene segmentation from a single image”, In Proceedings of the 12th European Conference on Computer Vision—vol. Part VII, ECCV'12, pp. 376-389, Berlin, Heidelberg, 2012.
[cited by applicant]
Brust, et al.; “Convolutional Patch networks with spatial prior for road detection and urban scene understanding”, In International Conference on Computer Vision Theory and Applications (VISAPP), 2015.
[cited by applicant]
Mohan, Rahul; “Deep deconvolutional networks for scene parsing”, CoRR, abs/1411.4101,2014.
[cited by applicant]
Oliveira, et al.; “Efficient Deep Models for Monocular Road Segmentation”, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016).
[cited by applicant]
Teichmann, et al.; “Multinet: Real-time joint semantic reasoning for autonomous driving”, arXiv preprint arXiv:1612.07695, 2016.
[cited by applicant]
Wang, et al.; “Embedding Structured Contour and Location Prior in Siamesed Fully Convolutional Networks for Road Detection”, in IEEE Transactions on Intelligent Transportation Systems, vol. 19, No. 1, pp. 230-241, Jan. …
[cited by applicant]
Mendes, et al.; “Exploiting fully convolutional neural networks for fast road detection”, Proc. IEEE Int. Conf. Robot. Auto. (ICRA), pp. 3174-3179, May 2016.
[cited by applicant]
Elfes; “Sonar-based real-world mapping and navigation”, Journal of Robotics and Automation, 1987.
[cited by applicant]
Thrun, et al.; “Probabilistic Robotics: Intelligent Robotics and Autonomous Agents” The MIT Press, 2005.
[cited by applicant]
Badino, et al.; “Free space computation using stochastic occupancy grids and dynamic programming”, In ICCV Workshop on Dynamical Vision, 2007.
[cited by applicant]
Franke, et al.; “Fast stereo based object detection for stop and go traffic”, In IV, 1996.
[cited by applicant]
Badino, et al.; “The stixel world—a compact medium level representation of the 3d-world”, in DAGM, 2009.
[cited by applicant]
Hirschmuller, “Stereo processsing by semiglobal matching and mutual information”, PAMI, 2008.
[cited by applicant]
Benenson; “Stixels estimation without depth map computation”, In ICCV, 2011.
[cited by applicant]
Yao, et al.; “Estimating drivable collision-free space from monocular video”, in Applications of Computer Vision, 2015, pp. 420-427.
[cited by applicant]
Levi, et al.; “Stixelnet: A deep convolutional network for obstacle detection and road segmentation”, 26th British Machine Vision Conference (BMVC) 2015.
[cited by applicant]
“Method Of Using A Signle Controller (ECU) For A Fault-Tolerant/Fail-Operational Self-Driving System” U.S. Appl. No. 15/881,426, filed Jan. 26, 2018.
[cited by applicant]
Koivisto, et al.; Final Office Action for U.S. Appl. No. 17/456,045, filed Nov. 22, 2021, mailed Jun. 29, 2023, 9 pgs.
[cited by applicant]
Liu, et al.; “SSD: Single Shot MultiBox Detector”; European Conference on Computer Vision, pp. 21-37 (2016), 17 pgs.
[cited by applicant]
Koivisto, et al.; First Office Action for Chinese Patent Application No. 201980004563.9, filed Mar. 17, 2020, mailed Jul. 6, 2023, 11 pgs.
[cited by applicant]
“Hyperopt: Distributed Asynchronous Hyper-parameter Optimization”, Hyperopt Documentation, Retrieved from Internet URL : http://hyperopt.github.io/hyperopt/, accessed on Feb. 17, 2022, pp. 3.
[cited by applicant]
“Multilayer Perceptron—Deeplearning 0.1 documentation”, The Wayback Machine, Retrieved from Internet URL : https://web.archive.org/web/20180216162302/http://deeplearning.net/tutorial/mlp.html, accessed on Feb. 17, 2022,…
[cited by applicant]