IP Library › Granted Patent US 12,286,138
Granted Patent B2
US 12,286,138 · App. 18/077,904 · Granted Apr 29, 2025

Adaptive trust calibration

Inventors: Kumar Akash (Milpitas, CA); Teruhisa Misu (San Jose, CA); Zhaobo K. Zheng (San Jose, CA)
Assignee: Honda Motor Co., Ltd.
B60W60/0054B60W60/001B60W2050/0075B60W2554/4029
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,286,138
App. No.
18/077,904
Granted
Apr 29, 2025
Kind
B2
Abstract

According to one aspect, systems and techniques for adaptive trust calibration may include usage of a driving style predictor, including a memory and a processor. The memory may store one or more instructions and the processor may execute one or more of the instructions stored on the memory to perform one or more acts, actions, or steps, such as receiving a current automated vehicle (AV) driving style, receiving an indication of an event and an associated event type, receiving an indication of a driver takeover, concatenating the current AV driving style and one or more of the event type or the driver takeover to generate an input, and passing the input through a neural network, which may include a gated recurrent unit (GRU), to generate a preference change associated with the AV driving style.

Claims (38)

1. A system for adaptive trust calibration, comprising:

a driving style predictor including:

a memory storing one or more instructions; and

a processor executing one or more of the instructions stored on the memory to perform:

receiving a current automated vehicle (AV) driving style;

receiving an indication of an event and an associated event type;

concatenating the current AV driving style and the event type to generate an input; and

passing the input through a neural network to generate a preference change associated with the AV driving style.

2. The system for adaptive trust calibration of claim 1 , comprising a driving style controller, implementing the preference change and generating an updated AV driving style.

3. The system for adaptive trust calibration of claim 2 , comprising a driving automation controller operating an AV by implementing the updated AV driving style.

4. The system for adaptive trust calibration of claim 3 , wherein the driving automation controller includes a Stanley controller generating a steering output based on the event.

5. The system for adaptive trust calibration of claim 3 , wherein the driving automation controller includes an intelligent driver model (IDM) controller generating throttle and brake outputs based on the event.

6. The system for adaptive trust calibration of claim 1 , wherein the processor receives an indication of a driver takeover.

7. The system for adaptive trust calibration of claim 6 , wherein the processor concatenates the current AV driving style, the event type, and the driver takeover to generate the input.

8. The system for adaptive trust calibration of claim 1 , wherein the neural network is a recurrent neural network or a gated recurrent unit (GRU).

9. The system for adaptive trust calibration of claim 1 , wherein the driving style predictor is initialized based on an initial AV driving style, an initial event and an associated initial event type, an indication of an initial driver takeover, or an initial preference change.

10. The system for adaptive trust calibration of claim 9 , wherein the initial preference change is received as a user input.

11. A system for adaptive trust calibration, comprising:

a driving style predictor including:

a memory storing one or more instructions; and

a processor executing one or more of the instructions stored on the memory to perform:

receiving a current automated vehicle (AV) driving style;

receiving an indication of a driver takeover;

concatenating the current AV driving style and the driver takeover to generate an input; and

passing the input through a neural network to generate a preference change associated with the AV driving style.

12. The system for adaptive trust calibration of claim 11 , comprising a driving style controller, implementing the preference change and generating an updated AV driving style.

13. The system for adaptive trust calibration of claim 12 , comprising a driving automation controller operating an AV by implementing the updated AV driving style.

14. The system for adaptive trust calibration of claim 13 , wherein the driving automation controller includes a Stanley controller generating a steering output based on a detected event and an intelligent driver model (IDM) controller generating throttle and brake outputs based on the detected event.

15. A method for adaptive trust calibration, comprising:

receiving a current automated vehicle (AV) driving style;

receiving an indication of an event and an associated event type or receiving an indication of a driver takeover;

concatenating the current AV driving style and one of the event type or the driver takeover to generate an input; and

passing the input through a neural network to generate a preference change associated with the AV driving style.

16. The method for adaptive trust calibration of claim 15 , comprising implementing the preference change and generating an updated AV driving style.

17. The method for adaptive trust calibration of claim 16 , comprising operating an AV by implementing the updated AV driving style.

18. The method for adaptive trust calibration of claim 17 , comprising generating a steering output based on the event.

19. The method for adaptive trust calibration of claim 17 , comprising generating throttle and brake outputs based on the event.

20. The method for adaptive trust calibration of claim 15 , wherein the event type is one of a pedestrian related event or a vehicle related event.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 8, 2022
From: AKASH, KUMAR; MISU, TERUHISA; ZHENG, ZHAOBO K.
To: HONDA MOTOR CO., LTD.
Reel/Frame 062031/0794 →
Continuity (1)
Related Publication 20240190481A1 · Jun 13, 2024
References Cited (127)
US 10759424B2 · Misu et al. · 2020 [cited by applicant]
US 11430231B2 · Takamoto · 2022 [cited by applicant]
US 11538259B2 · Martin · 2022 [cited by examiner]
US 11577722B1 · Packer et al. · 2023 [cited by applicant]
US 12005922B2 · Misu · 2024 [cited by examiner]
US 20150149017A1 · Attard et al. · 2015 [cited by applicant]
US 20150291146A1 · Prakah-Asante et al. · 2015 [cited by applicant]
US 20170031364A1 · Takahashi et al. · 2017 [cited by applicant]
US 20170039890A1 · Truong · 2017 [cited by examiner]
US 20170088143A1 · Goldman-Shenhar et al. · 2017 [cited by applicant]
US 20170136842A1 · Anderson et al. · 2017 [cited by applicant]
US 20170267256A1 · Minster et al. · 2017 [cited by applicant]
US 20180361972A1 · Zagorski · 2018 [cited by applicant]
US 20190071101A1 · Emura · 2019 [cited by examiner]
US 20190094865A1 · Fletcher · 2019 [cited by applicant]
US 20190126914A1 · Nojoumian · 2019 [cited by examiner]
US 20190213429A1 · Sicconi et al. · 2019 [cited by applicant]
US 20190291728A1 · Shalev-Shwartz et al. · 2019 [cited by applicant]
US 20200216094A1 · Zhu et al. · 2020 [cited by applicant]
US 20200369268A1 · McGill · 2020 [cited by examiner]
US 20210078608A1 · Misu · 2021 [cited by examiner]
US 20220126864A1 · Moustafa · 2022 [cited by examiner]
US 20220324449A1 · Shi · 2022 [cited by applicant]
US 20220324481A1 · Cheng · 2022 [cited by examiner]
US 20220324490A1 · Akash et al. · 2022 [cited by applicant]
US 20220343762A1 · Alvarez · 2022 [cited by examiner]
US 20220388543A1 · David et al. · 2022 [cited by applicant]
US 20220396287A1 · Akash · 2022 [cited by examiner]
US 20230109398A1 · Kranski et al. · 2023 [cited by applicant]
US 20230110713A1 · Degirmenci et al. · 2023 [cited by applicant]
US 20230128456A1 · Natarajan · 2023 [cited by examiner]
US 20230309053A1 · Kennouche et al. · 2023 [cited by applicant]
US 20230341825A1 · Dabija et al. · 2023 [cited by applicant]
US 20240012992A1 · Tamm et al. · 2024 [cited by applicant]
US 20240025404A1 · Gupta et al. · 2024 [cited by applicant]
US 20240177528A1 · Olin · 2024 [cited by examiner]
Notice of Allowance of U.S. Appl. No. 17/510,284 dated Feb. 14, 2024, 7 pages. [cited by applicant]
Notice of Allowance of U.S. Appl. No. 17/883,540 dated Oct. 28, 2024, 18 pages. [cited by applicant]
Office Action of U.S. Appl. No. 17/883,540 dated Jul. 25, 2024, 64 pages. [cited by applicant]
“Intelligent driver model.” Wikipedia, Wikimedia Foundation, Published date Oct. 25, 2021 https://en.wikipedia.org/wiki/Intelligent_driver_model. Accessed date Oct. 25, 2021. [cited by applicant]
Shubham Agrawal and Srinivas Peeta. 2021. Evaluating the impacts of driver's pre-warning cognitive state on takeover performance under conditional automation. Transportation Research Part F: Traffic Psychology and Behav… [cited by applicant]
Kumar Akash, Wan-Lin Hu, Neera Jain, and Tahira Reid. 2018. A Classification Model for Sensing Human Trust in Machines Using EEG and GSR. ACM Trans. Interact. Intell. Syst. 8, 4, Article 8, 4 (2018), 1-20. https://doi.o… [cited by applicant]
Kumar Akash, Neera Jain, and Teruhisa Misu. 2020. Toward Adaptive Trust Calibration for Level 2 Driving Automation. In ICMI 2020—Proceedings of the 2020 International Conference on Multimodal Interaction. Association fo… [cited by applicant]
Bradley M. Appelhans and Linda J. Luecken. 2006. Heart Rate Variability as an Index of Regulated Emotional Responding:. https://doi.org/10.1037/1089-2680.10.3.229 10, 3 (Sep. 2006), 229-240. https://doi.org/10.1037/1089… [cited by applicant]
C. Basu, Q. Yang, D. Hungerman, M. Sinahal, and A. D. Dragan, “Do you want your autonomous car to drive like you?” in 2017 12th ACM/IEEE Intemational Conference on Human-Robot Interaction (HRI. IEEE, 2017, pp. 417-425. [cited by applicant]
Bellem, H., Thiel, B., Schrauf, M. and Krems, J.F., 2018. Comfort in automated driving: An analysis of preferences for different automated driving styles and their dependence on personality traits. Transportation resear… [cited by applicant]
Hanna Bellem, Thorben Schönenberg, Josef F. Krems, and Michael Schrauf. 2016. Objective metrics of comfort: Developing a driving style for highly automated vehicles. Transportation Research Part F: Traffic Psychology an… [cited by applicant]
Stephen M. Casner, Edwin L. Hutchins, and Don Norman. 2016. The challenges of partially automated driving. Commun. ACM 59, 5 (2016), 70-77. https://doi.org/10.1145/2830565. [cited by applicant]
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. 2018. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. In In Proceedings of the European conference on… [cited by applicant]
J. K. Choi and Y. G. Ji, “Investigating the importance of trust on adopting an autonomous vehicle,” International Journal of Human-Computer Interaction, vol. 31, No. 10, pp. 692-702, 2015. [Online]. Available: https://d… [cited by applicant]
N. Dillen, M. Ilievski, E. Law, L. E. Nacke, K. Czamecki, and O. Schneider, “Keep calm and ride along: Passenger comfort and anxiety as physiological responses to autonomous driving styles,” in Proceedings of the 2020 C… [cited by applicant]
Epic Games. 2022. Unreal Engine. https://www.unrealengine.com/en-US/. [cited by applicant]
A. Eugensson, M. Brannstrom, D. Frasher, M. Rothoff, S. Solyom, and A. Robertsson, “Environmental, safety legal and societal implications of autonomous driving systems,” in International Technical Conference on the Enha… [cited by applicant]
Yannick Forster, Johannes Kraus, Sophie Feinauer, and Martin Baumann. 2018. Calibration of trust expectancies in conditionally automated driving by brand, reliability information and introductionary videos: An online st… [cited by applicant]
Sabu George, Manohara Pai, Radhika M Pai, and Samir Kumar Praharaj. 2017. Eye blink count and eye blink duration analysis for deception detection; Eye blink count and eye blink duration analysis for deception detection.… [cited by applicant]
Brano Glumac and François Des Rosiers. 2020. Practice briefing-Automated valuation models (AVMs): their role, their advantages and their limitations. Journal of Property Investment&Finance 39, 5 (2020), 481-491. https:/… [cited by applicant]
C. Gold, M. Korber, C. Hohenberger, D. Lechner, and K. Bengler, “Trust in automation—before and after the experience of take-over scenarios in a highly automated vehicle,” Procedia Manufacturing, vol. 3, pp. 3025-3032, … [cited by applicant]
Stefan Griesche Bosch, Mandy Dotzauer, Stefan Griesche, Enc Nicolay, Dirk Assmann, and David Käthner. 2016. Should my car drive as I do? What kind of driving style do drivers prefer for the design of automated driving f… [cited by applicant]
Franziska Hartwich, Matthias Beggiato, and Josef F Krems. “Driving Comfort, Enjoyment, and Acceptance of Automated Driving—Effects of Drivers' Age and Driving Style Familiarity.” Ergonomics, vol. 61, No. 8, pp. 1017-103… [cited by applicant]
M. Hasenjager and H. Wersing, “Personalization in advanced driver assistance systems and autonomous vehicles: A review,” in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017, pp.… [cited by applicant]
Hoffmann, Gabriel M., Claire J. Tomlin, Michael Montemerlo, and Sebastian Thrun. “Autonomous Automobile Trajectory Tracking for Off-Road Driving: Controller Design, Experimental Validation and Racing.” American Control … [cited by applicant]
Jesper F. Hopstaken, Dimitri van der Linden, Arnold B. Bakker, and Michiel A.J. Kompier. 2015. The window of my eyes: Task disengagement and mental fatigue covary with pupil dynamics. Biological Psychology 110 (Sep. 201… [cited by applicant]
Zhongxu Hu, Chen Lv, Peng Hang, Chao Huang, and Yang Xing. 2021. Data-driven Estimation of Driver Attention using Calibration-free Eye Gaze and Scene Features. IEEE Transactions on Industrial Electronics 69, 2 (2021), 1… [cited by applicant]
A. Kesting, M. Treiber, M. Schonhof, and D. Helbing, “Extending adaptive cruise control to adaptive driving strategies,” Transportation Research Record, vol. 2000, No. 1, pp. 16-24, 2007. [Online]. Available: https://do… [cited by applicant]
Keunwoo Kim, Minjung Park, and Youn Kyung Lim. 2021. Guiding Preferred Driving Style Using Voice in Autonomous Vehicles: An On-Road Wizard-of-Oz Study. In Designing Interactive Systems Conference 2021. Association for C… [cited by applicant]
Victor L. Knoop, Meng Wang, Isabel Wilmink, D. Marika Hoedemaeker, Mark Maaskant, and Evert Jeen Van der Meer. 2019. Platoon of SAE Level-2 Automated Vehicles on Public Roads: Setup, Traffic Interactions, and Stability.… [cited by applicant]
Körber, M., Baseler, E. and Bengler, K., 2018. Introduction matters: Manipulating trust in automation and reliance in automated driving. Applied ergonomics,66, pp. 18-31. [cited by applicant]
R. Krueger, T. H. Rashidi, and J. M. Rose, “Preferences for shared autonomous vehicles,” Transportation Research Part C: Emerging Technologies, vol. 69, pp. 343-355, 2016. [cited by applicant]
Lee, J.D., Liu, S.Y., Domeyer, J. and DinparastDjadid, A. “Assessing drivers' trust of automated vehicle driving styles with a two-part mixed model of intervention tendency and magnitude.” Human Factors, vol. 63, No. 2,… [cited by applicant]
John D. Lee and Katrina A. See. “Trust in Automation: Designing for Appropriate Reliance.” Human Factors, vol. 46, No. 1, pp. 50-80, 2004. [cited by applicant]
David G. Lowe. 2004. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 2004 60:2 60, 2 (Nov. 2004), 91-110. https://doi.org/10.1023/B:VISI.0000029664.99615.94. [cited by applicant]
Ma, Z. and Zhang, Y., Dec. 2020. Investigating the Effects of Automated Driving Styles and Driver's Driving Styles on Driver Trust, Acceptance, and Take Over Behaviors. In Proceedings of the Human Factors and Ergonomics… [cited by applicant]
Zheng Ma and Yiqi Zhang. 2021. Drivers trust, acceptance, and takeover behaviors in fully automated vehicles: Effects of automated driving styles and driver's driving styles. Accident Analysis & Prevention 159 (Sep. 202… [cited by applicant]
Marius Muja and David G Lowe. 2009. Fast Approximate Nearest Neighbors With Automatic Algorithm Configuration. Visapp 1, 2 (2009), 331-340. [cited by applicant]
Manisha Natarajan, Kumar Akash, and Teruhisa Misu. “Toward Adaptive Driving Styles for Automated Driving with Users' Trust and Preferences.” in Proceedings of the 2022 ACM/IEEE International Conference on Human-Robot In… [cited by applicant]
Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapea… [cited by applicant]
Rachel Phinnemore, Gabriele Cimolino, Pritam Sarkar, Ali Etemad, and T. C.Nicholas Graham. 2021. Happy Driver: Investigating the Effect of Mood on Preferred Style of Driving in Self-Driving Cars. In In Proceedings of th… [cited by applicant]
PPS. 2022. TactileGlove. https://pressureprofile.com/body-pressure-mapping/tactile-glove. [cited by applicant]
Christina M. Rudin-Brown and Heather A. Parker. 2004. Behavioural adaptation to adaptive cruise control (ACC): Implications for preventive strategies. Transportation Research Part F: Traffic Psychology and Behaviour 7, … [cited by applicant]
SAE. 2018. Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles—SAE International. https://www.sae.org/standards/content/j3016_201806/. [cited by applicant]
Shimmer. 2022. Shimmer3 GSR+ Unit. https://shimmersensing.com/product/shimmer3-gsr-unit/. [cited by applicant]
Sónia Soares, António Lobo, Sara Ferreira, Liliana Cunha, and António Couto. 2021. Takeover performance evaluation using driving simulation: a systematic review and meta analysis. European Transport Research Review 13 (… [cited by applicant]
Julia Trabulsi, Kian Norouzi, Seidi Suurmets, Mike Storm, and Thomas Zoëga Ramsøy. 2021. Optimizing Fixation Filters for Eye-Tracking on Small Screens. Frontiers in Neuroscience 15 (Nov. 2021), 1257. https://doi.org/10.… [cited by applicant]
Tobii. 2022. Tobii Pro Glasses 3. https://www.tobiipro.com/product-listing/tobii-pro-glasses-3/. [cited by applicant]
Treiber, Martin; Hennecke, Ansgar; Helbing, Dirk (2000), “Congested traffic states in empirical observations and microscopic simulations”, Physical Review E,62(2): 1805-1824. (https://en.wikipedia.org/wiki/Intelligent_d… [cited by applicant]
Treiber, M. and Kesting, A., 2013. Traffic flow dynamics. Traffic Flow Dynamics: Data, Models and Simulation, Springer-Verlag Berlin Heidelberg. [cited by applicant]
Walker, F., Verwey, W. and Martens, M., Jun. 2018. Gaze behaviour as a measure of trust in automated vehicles. In Proceedings of the 6th Humanist Conference (Jun. 2018). [cited by applicant]
Ekman, F., Johansson, M., Karlsson, M., Strömberg, H. and Bligård, L.O., 2021. Trust in what? Exploring the interdependency between an automated vehicle's driving style and traffic situations. Transportation research pa… [cited by applicant]
Ekman, F., Johansson, M., Bligård, L.O., Karlsson, M. and Strömberg, H., 2019. Exploring automated vehicle driving styles as a source of trust information. Transportation research part F: traffic psychology and behaviou… [cited by applicant]
N. Adnan, S. Md Nordin, M. A. bin Bahruddin, and M. Ali, “How trust can drive forward the user acceptance to the technology? Invehicle technology for autonomous vehicle,” Transportation Research Part A: Policy and Pract… [cited by applicant]
F. Chollet et al., “Keras,” 2015. https://keras.io/. [cited by applicant]
A. Dettmann, F. Hartwich, P. Roßner, M. Beggiato, K. Felbel, J. Krems, and A. C. Bullinger, “Comfort or not? Automated driving style and user characteristics causing human discomfort in automated driving,” Intemational … [cited by applicant]
L. Kleisen, The relationship between thinking and driving styles and their contribution to young driver road safety. University of Canberra Bruce, Australia, 2011. [cited by applicant]
D. Vaughan, Sep. 2022. [Online]. Available: https://cran.r-project.org/web/packages/yardstick/vignettes/multiclass.html. [cited by applicant]
J. L. Andreassi, Psychophysiology: Human behavior and physiological response. Psychology Press, 2010. [cited by applicant]
S. E. Baek, H. S. Kim, and M. Han, “Personalized speed planning algorithm using a statistical driver model in car-following situations,” International Journal of Automotive Technology, vol. 23, No. 3, pp. 829-840, 2022. [cited by applicant]
T. Chen, S. Komblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International conference on machine learning. PMLR, 2020, pp. 1597-1607. [cited by applicant]
D. Chicco, “Siamese neural networks: An overview,” Artificial Neural Networks, pp. 73-94, 2021. [cited by applicant]
D. Chung, K. Tahboub, and E. J. Delp, “A two stream siamese convolutional neural network for person re-dentification,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 1983-1991. [cited by applicant]
E. de Gelder, I. Cara, J. Uittenbogaard, L. Kroon, S. van Iersel, and J. Hogema, “Towards personalised automated driving: Prediction of preferred acc behaviour based on manual driving,” in 2016 IEEE Intelligent Vehicles… [cited by applicant]
N. Du, F. Zhou, E. M. Pulver, D. M. Tilbury, L. P. Robert, A. K. Pradhan, and X. J. Yang, “Examining the effects of emotional valence and arousal on takeover performance in conditionally automated driving,” Transportati… [cited by applicant]
J. B. Greenblatt and S. Shaheen, “Automated vehicles, on-demand mobility, and environmental impacts,” Current sustainable/renewable energy reports, vol. 2, No. 3, pp. 74-81, 2015. [cited by applicant]
W. Hayale, P. Negi, and M. Mahoor, “Facial expression recognition using deep siamese neural networks with a supervised loss function,” in 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (… [cited by applicant]
S.-C. Hsiao, D.-Y. Kao, Z.-Y. Liu, and R. Tso, “Malware image classification using one-shot learning with siamese hetworks,” Procedia Computer Science, vol. 159, pp. 1863-1871, 2019. [cited by applicant]
S.-A. Kaye, S. Nandavar, S. Yasmin, I. Lewis, and O. Oviedo-Trespalacios, “Consumer knowledge and acceptance of advanced driver assistance systems,” Transportation Research Part F: Traffic Psychology and Behaviour, vol.… [cited by applicant]
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014. [cited by applicant]
V. K. Kukkala, J. Tunnell, S. Pasricha, and T. Bradley, “Advanced driver-assistance systems: A path toward autonomous vehicles,” IEEE Consumer Electronics Magazine, vol. 7, No. 5, pp. 18-25, 2018. [cited by applicant]
C. Lee, B. Seppelt, B. Reimer, B. Mehler, and J. F. Coughlin, “Acceptance of vehicle automation: Effects of demographic traits, technology experience and media exposure,” in Proceedings of the Human Factors and Ergonomi… [cited by applicant]
Y. Y. Lee, M. Dong, V. Krishnamoorthy, K. Akash, Z. Zheng, T. Misu, and G. Huang, “The impacts of adaptive driving styles on trust in level 2 automated vehicles,” in Proceedings of the Human Factors and Ergonomic Societ… [cited by applicant]
B. C. Lee and V. G. Duffy, “The effects of task interruption on human performance: A study of the systematic classification of human behavior and interruption frequency,” Human Factors and Ergonomics in Manufacturing & … [cited by applicant]
S. Mai, H. Hu, and S. Xing, “Divide, conquer and combine: Hierarchical feature fusion network with local and global perspectives for multimodal affective computing,” in Proceedings of the 57th annual meeting of the asso… [cited by applicant]
B. Moradi, “The new optimization algorithm for the vehicle routing problem with time windows using multi-objective discrete learnable evolution model,” Soft Computing, vol. 24, No. 9, pp. 6741-6769, 2020. [cited by applicant]
I. Nastjuk, B. Herrenkind, M. Marrone, A. B. Brendel, and L. M. Kolbe, “What drives the acceptance of autonomous driving? an investigation of acceptance factors from an end-user's perspective,” Technological Forecasting… [cited by applicant]
Y. Niu, D. Kong, R. Wen, Z. Cao, and J. Xiao, “An improved learnable evolution model for solving multi-objective vehicle routing problem with stochastic demand,” Knowledge-Based Systems, vol. 230, p. 107378, 2021. [cited by applicant]
Z. Sajedinia, K. Akash, Z. Zheng, T. Misu, M. Dong, V. Krishnamoorthy, K. Martinez, K. Sureshbabu, and G. Huang, “Investigating users' preferences in adaptive driving styles for level 2 driving automation,” in 14th Inte… [cited by applicant]
Jarrod M Snider, “Automatic Steering Methods for Autonomous Automobile Path Tracking”, Feb. 2009, Carnegie Mellon University (Year. 2009). [cited by applicant]
M. Teste, A. Broutin, M. Marty, M. C. Valera, F. Soares Cunha, and E. Noirrit-Esclassan. 2021. Toothbrushing in children with autism spectrum disorders: qualitative analysis of parental difficulties and solutions in Fra… [cited by applicant]
A. Ullah, K. Muhammad, K. Haydarov, I. U. Haq, M. Lee, and S. W. Baik, “One-shot learning for surveillance anomaly recognition using siamese 3d cnn,” in 2020 International Joint Conference on Neural Networks (IJCNN). IE… [cited by applicant]
F. Wang and H. Liu, “Understanding the behaviour of contrastive loss,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 2495-2504. [cited by applicant]
L. Yue, M. Abdel-Aty, Y. Wu, and L. Wang, “Assessment of the safety benefits of vehicles' advanced driver assistance, connectivity and low level automation systems,” Accident Analysis & Prevention, vol. 117, pp. 55-64, … [cited by applicant]
N. Zeghidour, G. Synnaeve, N. Usunier, and E. Dupoux, “Joint learning of speaker and phonetic similarities with siamese networks.” in Interspeech, 2016, pp. 1295-1299. [cited by applicant]
C. Zhang, W. Liu, H. Ma, and H. Fu, “Siamese neural network based gait recognition for human identification,” in 2016 ieee international conference on acoustics, speech and signal processing (ICASSP). IEEE, 2016, pp. 28… [cited by applicant]
Z. Zheng, K. Akash, T. Misu, V. Krishnamoorthy, M. Dong, Y. Lee, and G. Huang, “Identification of adaptive driving style preference through implicit inputs in sae 12 vehicles,” in Proceedings of the 2022 International C… [cited by applicant]
Z. K. Zheng, J. E. Staubitz, A. S. Weitlauf, J. Staubitz, M. Pollack, L. Shibley, M. Hopton, W. Martin, A. Swanson, P. Juárez, et al., “A predictive multimodal framework to alert caregivers of problem behaviors for chil… [cited by applicant]
Z. K. Zheng, K. Akash, and T. Misu, “Detection of perceived discomfort in sae l2 automated vehicles through driver takeovers and physiological spikes,” in 2022 IEEE 25th International Conference on Intelligent Transport… [cited by applicant]
K. H. Zou, J. R. Fielding, S. G. Silverman, and C. M. Tempany, “Hypothesis testing i: proportions,” Radiology, vol. 226, No. 3, pp. 609-613, 2003. [cited by applicant]
Office Action of U.S. Appl. No. 17/510,284 dated Jul. 28, 2023, 24 pages. [cited by applicant]