1,117
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Scenario-based collision detection using machine learning for highly automated driving systems

ORCID Icon, &
Article: 2169384 | Received 20 Sep 2022, Accepted 12 Jan 2023, Published online: 28 Jan 2023

References

  • Adewopo, V., Elsayed, N., ElSayed, Z., Ozer, M., Abdelgawad, A., & Bayoumi, M. (2022). Review on action recognition for accident detection in smart city transportation systems. arXiv preprint:arXiv:220809588. https://doi.org/10.48550/arxiv.2208.09588
  • Ahmed, S., Alshater, M. M., Ammari, A. E., & Hammami, H. (2022). Artificial intelligence and machine learning in finance: A bibliometric review. Research in International Business and Finance, 61, 10164661. https://doi.org/10.1016/j.ribaf.2022.101646
  • Ahsan, M. M., & Siddique, Z. (2022). Machine learning-based heart disease diagnosis: A systematic literature review. Artificial Intelligence in Medicine, 128, 102289128. https://doi.org/10.1016/j.artmed.2022.102289
  • Almutairi, M., Muneer, K., & Rehman, A. (2022). Vehicles auto collision detection & avoidance protocol. International Journal of Computer Science and Network Security, 22(3), 107–112. https://doi.org/10.22937/IJCSNS.2022.22.3.15
  • Anvaripour, M., & Saif, M. (2019). Collision detection for human-robot interaction in an industrial setting using force myography and a deep learning approach. In IEEE international conference on systems, man and cybernetics (SMC) (pp. 2149–2154). IEEE. https://doi.org/10.1109/SMC.2019.8914660
  • Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics Surveys, 4(1), 40–79. https://doi.org/10.1214/09-SS054
  • Ba, J. L., Kiros, J. R., & Hinton, G. E. (2016). Layer normalization. Machine Learning (stat.ML), Machine Learning (cs.LG), Computer and information sciences. arXiv preprint arXiv. https://arxiv.org/pdf/1607.06450.pdf.
  • Bagschik, G., Menzel, T., & Maurer, M. (2018). Ontology based scene creation for the development of automated vehicles. In 2018 IEEE intelligent vehicles symposium (IV) (pp. 1813–1820). IEEE. https://doi.org/10.1109/IVS.2018.8500632
  • Berrar, D. (2019). Cross-validation. In S. Ranganathan, M. Gribskov, K. Nakai, & C. Schönbach (Eds.), Academic Press. https://www.sciencedirect.com/science/article/pii/B978012809633820349X. https://doi.org/10.1016/B978-0-12-809633-8.20349-X
  • Bhavsar, P., Safro, I., Bouaynaya, N., Polikar, R., & Dera, D. (2017). Chapter 12 – machine learning in transportation data analytics. In M. Chowdhury, A. Apon, & K. Dey (Eds.), Data Analytics for Intelligent Transportation Systems (pp. 283–307). Elsevier. https://www.sciencedirect.com/science/article/pii/B9780128097151000122. https://doi.org/10.1016/B978-0-12-809715-1.00012-2
  • Borg, M., Englund, C., Wnuk, K., Duran, B., Levandowski, C., Gao, S., Tan, Y., Kaijser, H., L'onn, H., T'ornqvist, J., & Gao, S. (2018). Safely entering the deep: A review of verification and validation for machine learning and a challenge elicitation in the automotive industry. Journal of Automotive Software Engineering, 1(1), 1–19. https://doi.org/10.48550/arXiv.1812.05389
  • CarMaker. (n.d.). Carmaker-ipg automotive. https://ipg-automotive.com/de/produkte-loesungen/software/carmaker/.
  • Choi, J. G., Kong, C. W., Kim, G., & Lim, S. (2021). Car crash detection using ensemble deep learning and multimodal data from dashboard cameras. Expert Systems with Applications, 183, 115400. https://doi.org/10.1016/j.eswa.2021.115400
  • Cima, G., Cluzeau, M. J., Henriquel, X., Rebender, G., Soudain, G., Dijk, L.V., Gronskiy, A., Haber, D., Perret-Gentil, C., & Polak, R. (2020). Concepts of design assurance for neural networks (CoDANN) (3rd ed., Tech. Rep.). Published by: European Union Aviation Safety Agency (EASA). https://www.easa.europa.eu/sites/default/files/dfu/EASA-DDLN-Concepts-of-Design-Assurance-for-Neural-Networks-CoDANN.pdf
  • de Gelder, E., Paardekooper, J. P., Saberi, A. K., Elrofai, H., Camp, O. O. D., Kraines, S., Ploeg, J., & De Schutter, B. (2022). Towards an ontology for scenario definition for the assessment of automated vehicles: an object-oriented framework. IEEE Transactions on Intelligent Vehicles, 7(2), 300–314. https://doi.org/10.1109/TIV.2022.3144803
  • Devies, A. (2016). Google's self-driving car caused its first crash. transportation: In Wired. https://www.wired.com/2016/02/googles-self-driving-car-maycaused-irst-crash/
  • Eder, M., Reip, M., & Steinbauer, G. (2022). Creating a robot localization monitor using particle filter and machine learning approaches. Applied Intelligence, 52(6), 6955–6969. https://doi.org/10.1007/s10489-020-02157-6
  • Elrofa, H., Paardekooper, J., Gelde, E. D., Kalisvaart, S., & Camp, O. O. D. (2018). Streetwise-scenario-based safety validation of connected and automated driving, Technical Paper. Helmond, Netherland, TNO innovation for life.
  • Erz, J., Schütt, B., Braun, T., Guissouma, H., & Sax, E. (2022). Towards an ontology that reconciles the operational design domain, scenario-based testing, and automated vehicle architectures. In 2022 IEEE international systems conference (SYSCON) (pp. 1–8). IEEE. https://doi.org/10.1109/SysCon53536.2022.9773840
  • Garcia, I., Martin-Guerrero, J., Soria-Olivas, E., Martinez, R., Rueda, S., & Magdalena, R. (2002). A neural network approach for real-time collision detection. In IEEE international conference on systems, man and cybernetics (Vol. 5). IEEE. https://doi.org/10.1109/ICSMC.2002.1176371
  • Gupta, A. (2021 October). Lesson 13 – mean squared error : Overview, examples, concepts and more. In Data science & business analytics, presention in Simplelearn. Online course-webinars. https://www.simplilearn.com/tutorials/statistics-tutorial/mean-squared-error
  • Heo, Y. J., Kim, D., Lee, W., Kim, H., Park, J., & Chung, W. K. (2019). Collision detection for industrial collaborative robots: A deep learning approach. IEEE Robotics and Automation Letters, 4(2), 740–746. https://doi.org/10.1109/LRA.2019.2893400
  • Huang, T., Wang, S., & Sharma, A. (2020). Highway crash detection and risk estimation using deep learning. Accident Analysis and Prevention, 135, 105392. 135https://doi.org/10.1016/j.aap.2019.105392
  • Hülsen, M., Zöllner, J. M., & Weiss, C. (2011). Traffic intersection situation description ontology for advanced driver assistance. In 2011 IEEE intelligent vehicles symposium (IV) (pp. 993–999). IEEE.
  • ISO21448. (2022). Road vehicles – safety of the intended functionality (1st ed., Tech. Rep.). Technical Committee:ISO/TC 22/SC 32. https://www.iso.org/standard/77490.html
  • ISO26262. (2018). Road vehicles – functional safety, part 1 to part 13 (2nd ed., Tech. Rep.). Technical Committee:ISO/TC 22/SC 32.
  • ISO/AWI/TS5083. (n.d.). Road vehicles – safety for automated driving systems – design, verification and validation (1st ed., Tech. Rep.). Status-Under development, Technical Committee:ISO/TC 22/SC 32. https://www.iso.org/standard/81920.html
  • Julian, K., Kochenderfer, M. J., & Owen, M. (2018). Deep neural network compression for aircraft collision avoidance systems. arXiv preprint arXiv. https://arxiv.org/pdf/1810.04240.pdf
  • Khatun, M., Caldeira, G. B., Jung, R., & Glaß, M. (2021a). An optimization and validation method to detect the collision scenarios and identifying the safety specification of highly automated driving vehicle. In 21st international conference on control, automation and systems (ICCAS) (pp. 1570–1575). IEEE. https://doi.org/10.23919/ICCAS52745.2021.9649806
  • Khatun, M., Caldeira, G. B., Jung, R., & Glaß, M. (2021b). A systematic approach of reduced scenario-based safety analysis for highly automated driving Function. In Proceedings of the 7th international conference on vehicle technology and intelligent transport systems – Volume 1: VEHITS (pp. 301–308). SciTePress. https://doi.org/10.5220/0010397403010308
  • Khatun, M., Litagin, H., Jung, R., & Glaß, M. (2022). An approach for deriving reduced collision scenarios for highly automated driving systems. In International conference on computer safety, reliability, and security (pp. 166–177). Springer.
  • Kim, K., & Lee, K. (2018). Context-Aware information provisioning for vessel traffic service using rule-based and deep learning techniques. International Journal of Fuzzy Logic and Intelligent Systems., 18(1), 13–19. https://doi.org/10.5391/IJFIS.2018.18.1.13
  • Kingma, D. P., & Ba, J. L. (2017). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. Published as a conference paper at ICLR 2015. https://arxiv.org/pdf/1412.6980.pdf
  • Knupp, J. (2017). Einführung in deep learning – LSTM & CNN, Technical University Munich-Faculaty of computer science. Proseminar Data Mining.
  • Koduri, T., Bogdoll, D., Paudel, S., & Sholingar, G. (2018). Aureate: An augmented reality test environment for realistic simulations (SAE Technical Paper No.1080). SAE International. https://doi.org/10.4271/2018-01-1080
  • Koopman, P., & Sholingar, G. (2016). Challenges in autonomous vehicle testing and validation. SAE International Journal of Transportation Safety, 4(1), 15–24. https://doi.org/10.4271/2016-01-0128
  • Krajewski, R., Moers, T., Nerger, D., & Eckstein, L. (2018). Data-Driven Maneuver modeling using generative adversarial networks and variational autoencoders for safety validation of highly automated vehicles. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC) (pp. 2383–2390). IEEE. https://doi.org/10.1109/ITSC.2018.8569971
  • Krebs-Radic, S., & Körtke, F. (2022 May). Scenario-based-testing at scale II. https://www.vvm-projekt.de/midterm-docs
  • Levin, S., & Carrie, J. W. (2018). Self-driving Uber kills Arizona woman in IRST fatal crash involving pedestrian. The Guardian. www.theguardian.com/technology/2018/mar/19/uber-self-driving-car-kills-woman-arizona-tempe
  • Li, P. (2020). A deep learning approach for real-time crash risk prediction at urban arterials. Electronic Thesis and Dissertations. University of Central Florida-department of Civil, Environmental, and Construction Engineering. https://stars.library.ucf.edu/etd2020/89
  • Menzel, T. (2020 April). Knowledge-based scenario generation using ontologies. Paper presented at technical university Braunschwig. Institute of Control Engineering (TFR). https://www.asam.net/index.php?eID=dumpFile&t=f&f=3529&token=86706043641c4f77ada441a81ef6ae2b23e26f75
  • Menzel, T., Bagschik, G., Isensee, L., Schomburg, A., & Maurer, M. (2019). From functional to logical scenarios: Detailing a keyword-based scenario description for execution in a simulation environment. IEEE Intelligent Vehciles Symposium (IV). IEEE.
  • Menzel, T., Bagschik, G., & Maurer, M. (2018). Scenarios for development, test and validation of automated vehicles. IEEE Intelligent Vehciles Symposium (IV). IEEE.
  • Miguelañez, E., Patrón, P., Brown, K. E., Petillot, Y. R., & Lane, D. M. (2011). Semantic knowledge-based framework to improve the situation awareness of autonomous underwater vehicles. IEEE Transactions on Knowledge and Data Engineering, 23(5), 759–773. https://doi.org/10.1109/TKDE.2010.46
  • Muzammel, M., Yusoff, M. Z., Saad, M. N. M., Sheikh, F., & Awais, M. A. (2022). Blind-spot collision detection system for commercial vehicles using multi deep CNN architecture. MDPI. https://arxiv.org/pdf/2208.08224.pdf
  • Neurohr, C., Westhofen, L., Henning, T., de Graaff, T., Möhlmann, E., & Böde, E. (2020). Fundamental considerations around scenario-based testing for automated driving. In 2020 IEEE intelligent vehicles symposium (IV) (121–127). IEEE. https://arxiv.org/abs/2005.04045. https://doi.org/10.48550/ARXIV.2005.04045
  • Nikitin, N. O., Vychuzhanin, P., Sarafanov, M., Polonskaia, I. S., Revin, I., Barabanova, I. V., Maximov, G., Kalyuzhnaya, A. V., & Boukhanovsky, A. (2022). Automated evolutionary approach for the design of composite machine learning pipelines. Future Generation Computer Systems, 127(127), 109–125. https://doi.org/10.1016/j.future.2021.08.022. https://www.sciencedirect.com/science/article/pii/S0167739X21003307
  • Peres, R. S., Barata, J., Leitao, P., & Garcia, G. (2019). Multistage quality control using machine learning in the automotive industry. IEEE Access, 7(1), 79908–79916. https://doi.org/10.1109/ACCESS.2019.2923405
  • Ponn, T., Diermeyer, F., & Gnandt, C. (2019). An optimization-based method to identify relevant scenarios for type approval of automated vehicles. National Academies: Sciences Engineering Medicine. In 26th international technical conference on the enhanced safety of vehicles (ESV): Technology: Enabling a safer tomorrow. Published by Transportation Research Board. https://trid.trb.org/view/1755720
  • SAEJ3016. (2021). Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles (2nd ed., Tech. Rep.). SAE International. https://www.sae.org/standards/content/j3016_202104/
  • Salay, R., Queiroz, R., & Czarnecki, K. (2017). An analysis of ISO 26262: Using machine learning safely in automotive software. In Computer Science – Artificial Intelligence. arXiv preprint arXiv. https://arxiv.org/abs/1709.02435. https://doi.org/10.48550/ARXIV.1709.02435
  • Salay, R., Queiroz, R., & Czarnecki, K. (2018). An analysis of iso 26262: Machine learning and safety in automotive software. (SAE Technical Paper No.1075). WCX World Congress Experience, SAE International. https://doi.org/10.4271/2018-01-1075
  • Sen, J., Sen, R., & Dutta, A. (2021). Machine learning in finance-emerging trends and challenges. arXiv preprint arXiv. https://arxiv.org/abs/2110.11999. https://doi.org/10.48550/ARXIV.2110.11999
  • Sharkawy, A. N. (2022). Neural networks for robot collision estimation and detection. PriMera Scientific Engineering, 1(1), 12–15. https://doi.org/10.2139/ssrn.4206497. https://hal.archives-ouvertes.fr/hal-03767302/document
  • Spanfelner, B., Richter, D., Ebel, S., Wilhelm, U., & Patz, C. (2012). Challenges in applying the ISO 26262 for driver assistance systems. Tagung Fahrerassistenz, Munich, 15(16), 2012. https://pdf4pro.com/view/challenges-in-applying-the-iso-26262-for-driver-e028c.html
  • Stewart, J. (2018). Tesla's self-driving autopilot involved in another deadly car crash. Transportation: Wired. https://www.wired.com/story/tesla-autopilot-selfdriving-crash-california/
  • Strömgren, O. (2018). Deep learning for autonomous collision avoidance, Department of Electrical Engineering. Linköping University. Master of Science Thesis in Computer Science. https://www.diva-portal.org/smash/get/diva2:1204063/FULLTEXT01.pdf
  • Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., & Elge, G. (2021). Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry. Reliablity Engineering and System Safety, 215, 107864. https://doi.org/10.1016/j.ress.2021.107864
  • Unfalltypen-Katalog. (2016). Leitfaden zur bestimmung des unfalltyps, gesamtverband der deutschen versicherungswirtschaft e. v. (Technical Paper). Germany, TUnfallforschung der Versicherer.
  • Valls, J. J., García-Gordillo, M., & Sáez, S. (2020). Scenario-Based validation & verification: The ENABLE-S3 approach. ACM SIGAda Ada Letters, 40(1), 79–84. https://doi.org/10.1145/3431235.3431242
  • Vayena, E., & Blasimme, A. (2022). A systemic approach to the oversight of machine learning clinical translation. The American Journal of Bioethics, 22(5), 23–25. https://doi.org/10.1080/15265161.2022.2055216
  • Wang, W., & Siau, K. (2019). Artificial intelligence, machine learning, automation, robotics, future of work and future of humanity: A review and research agenda. Journal of Database Management (JDM), 30(1), 61–79. http://doi.org/10.4018/JDM.2019010104
  • Winner, H., Lemmer, K., Form, T., & Mazzega, J. (2019). PEGASUS – First Steps for the Safe Introduction of Automated Driving. In G. Meyer and S. Beiker (Eds), Road Vehicle Automation 5 (pp. 185–195). Springer International Publishing.
  • Wood, M., Wittman, D., Srivastava, T., Liu, S., Wang, Y., Knobel, C., Boymanns, D., Syguda, S., Wiltschko, T., Garbacik, N., O'Brien, M., Dannerbaum, U., Weast, J., & Dornieden, B. (2019). Safety first for automated driving. (Tech. Rep.). https://www.daimler.com/innovation/case/autonomous/safety-first-for-automated-driving-2.html
  • Xiao, X., Liu, B., Warnell, G., & Stone, P. (2022). Creating a robot localization monitor using particle filter and machine learning approaches. Autonomous Robots, 46(5), 569–597. https://doi.org/10.1007/s10514-022-10039-8
  • Yao, B., & Feng, T. (2018). Machine learning in automotive industry. Advances in Mechanical Engineering, 10(10), 1687814018805787. https://doi.org/10.1177/1687814018805787
  • Zhang, X., Tao, J., Tan, K., Törngren, M., Sánchez, J. M. G., Tao Ramli, M. R., Wotawa, F., Mohan, N., Nica, M., & Felbinger, H. (2021). Finding critical scenarios for automated driving systems: A systematic literature review. Artificial Intelligence (cs.AI); Systems and Control (eess.SY). arXiv preprint arXiv. https://arxiv.org/abs/2110.08664. https://doi.org/10.48550/ARXIV.2110.08664
  • Zofka, M. R., Kuhnt, F., Kohlhaas, R., Rist, C., Schamm, T., & Zöllner, J. M. (2015). Data-driven simulation and parametrization of traffic scenarios for the development of advanced driver assistance systems. In 2015 18th international conference on information fusion (FUSION) (pp. 1422–1428). IEEE.