Managing Driving Modes in Automated Driving Systems
Published Online:15 Mar 2022https://doi.org/10.1287/trsc.2021.1110
References
- (2011) A Bayesian approach for driving behavior inference. 2011 IEEE Intelligent Vehicles Sympos. (IV) (IEEE, Piscataway, NJ), 595–600.Crossref, Google Scholar
- (2019) Driving behavior modeling based on hidden Markov models with driver’s eye-gaze measurement and ego-vehicle localization. 2011 IEEE Intelligent Vehicles Sympos. (IV) (IEEE, Piscataway, NJ), 949–956.Crossref, Google Scholar
- (2006) Mesh adaptive direct search algorithms for constrained optimization. SIAM J. Optim. 17(1):188–217.Crossref, Google Scholar
- AutoMate Consortium (2019) Automate: Revolution in vehicle automation. Accessed October 24, 2019, http://www.automate-project.eu/.Google Scholar
- (2018) The moral machine experiment. Nature 563(7729):59–64.Crossref, Google Scholar
- (2020) Why everyone has it wrong about the ethics of autonomous vehicles. Frontiers Engrg. Rep. Leading-Edge Engrg. 2019 Sympos. (National Academies Press, Washington, DC), 87–102.Google Scholar
- (2018) Trustonomy: Building the acceptance of automated mobility. Intelligent Transport (May 29), https://www.intelligenttransport.com/transport-articles/99585/trustonomy-building-the-acceptance-of-automated-mobility/.Google Scholar
- (2010) Monitoring drivers’ mental workload in driving simulators using physiological measures. Accident Anal. Prevention 42(3):898–903.Crossref, Google Scholar
- (2021) Modeling ethical and operational preferences in automated driving systems. Decision Anal. Forthcoming.Google Scholar
- (2021) Decision support issues in autonomous driving systems. Internat. Trans. Oper. Res., ePub ahead of print January 17, https://doi.org/10.1111/itor.12936.Google Scholar
- (2018) A “driver-more” approach to vehicle automation. Proc. 6th Humanist Conf. (Humanist Publications, Lyon, France).Google Scholar
- (2019) A review of motion planning for highway autonomous driving. IEEE Trans. Intelligent Transportation Systems 21(5):1826–1848.Crossref, Google Scholar
- (2018) Operational design domain for automated driving systems taxonomy of basic terms. Technical Report, University of Waterloo, Waterloo, Canada.Google Scholar
- (2021) On incorporating forecasts into linear state space model Markov decision processes. Philos. Trans. Roy. Soc. A 379(2202):20190430.Crossref, Google Scholar
- (2011) Driver inattention monitoring system for intelligent vehicles: A review. IEEE Trans. Intelligent Transportation Systems 12(2):596–614.Crossref, Google Scholar
- (2017) Takeover time in highly automated vehicles: Noncritical transitions to and from manual control. Human Factors 59(4):689–705.Crossref, Google Scholar
- (2000) Statistical Decision Theory (Edward Arnold, London).Google Scholar
- (2016) A review of motion planning techniques for automated vehicles. IEEE Trans. Intelligent Transportation Systems 17(4):1135–1145.Crossref, Google Scholar
- (2018) A review of driver state monitoring systems in the context of automated driving. Bagnara S, Tartaglia R, Albolino S, Alexander T, Fujita Y, eds. IEA 2018 Proc. 20th Congress Internat. Ergonomics Assoc., Advances in Intelligent Systems and Computing, vol. 823 (Springer International Publishing, Cham, Switzerland), 398–408.Google Scholar
- (2014) Readiness for self-driving vehicles in Australia. Workshop Report, 26th Australian Road Research Board Conference, October 19–22, ANZ Stadium, Sydney.Google Scholar
- (2015) Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions. Transportation Res. Part C Emerging Tech. 60:416–442.Crossref, Google Scholar
- (2016) Recent trends in driver safety monitoring systems: State of the art and challenges. IEEE Trans. Vehicular Tech. 66(6):4550–4563.Crossref, Google Scholar
- (2020) Bringing trust to autonomous mobility. 2020 AEIT Internat. Conf. Electrical Electronic Tech. Automotive AEIT AUTOMOTIVE (Associazione Italiana di Elettrotecnica, Elettronica, Automazione, Informatica e Telecomunicazioni, Milan), TS12_p02.Crossref, Google Scholar
- (2014) Driving intention inference based on dynamic Bayesian networks. Wen Z, Li T, eds. Practical Applications of Intelligent Systems, Advances in Intelligent Systems and Computing, vol. 279 (Springer, Berlin), 1109–1119.Crossref, Google Scholar
- (2016) Why ethics matters for autonomous cars. Maurer M, Gerdes JC, Lenz B, Winner H, eds. Autonomous Driving: Technical, Legal and Social Aspects (Springer, Berlin), 69–85.Google Scholar
- (2016) 50th anniversary invited article-autonomous vehicles and connected vehicle systems: Flow and operations considerations. Transportation Sci. 50(4):1140–1162.Link, Google Scholar
- (2017) Concrete problems for autonomous vehicle safety: Advantages of Bayesian deep learning. Sierra C, ed. Proc. 26th Internat. Joint Conf. Artificial Intelligence (AAAI Press, Carles Sierra), 4745–4753.Crossref, Google Scholar
- (2019) A taxonomy of autonomous vehicle handover situations. Transportation Res. Part A Policy Pract. 124:507–522.Crossref, Google Scholar
- (2016) Response Surface Methodology: Process and Product Optimization Using Designed Experiments, Wiley Series in Probability and Statistics (John Wiley & Sons, Hoboken, NJ).Google Scholar
- (2001) NHTSA driver distraction research: Past, present, and future. SAE Technical Paper, Society of Automobile Engineers International, Warrendale, PA.Google Scholar
- Society of Automobile Engineers (2018) Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. Technical Report, Society of Automobile Engineers International, Warrendale, PA.Google Scholar
- (2014) Bayesian nonparametric modeling of driver behavior. 2014 IEEE Intelligent Vehicles Sympos. Proc. (IEEE, Piscataway, NJ), 932–938.Crossref, Google Scholar
- (2001) The role of driver distraction in traffic crashes. Technical Report, AAA Foundation for Traffic Safety, Washington, DC.Google Scholar
- (2018) Reinforcement Learning: An Introduction (MIT Press, Cambridge, MA).Google Scholar
- (2019) A machine-learning approach to distinguish passengers and drivers reading while driving. Sensors 19(14):3174.Crossref, Google Scholar
- Trustonomy (2020) Deliverable 2.2. methodological guidelines. Technical Report, Trustonomy. Accessed February 29, 2020, https://h2020-trustonomy.eu/download/d2-2-deliverable-trustonomy-methodological-guidelines-version-1-0/.Google Scholar
- (2015) Autonomous driving: Investigating the feasibility of car-driver handover assistance. AutomotiveUI’15 Proc. 7th Internat. Conf. Automotive User Interfaces Interactive Vehicular Appl. (Association for Computing Machinery, New York), 11–18.Crossref, Google Scholar
- (2020) Stability analysis of stochastic linear car-following models. Transportation Sci. 54(1):274–297.Link, Google Scholar
- (2006) Bayesian Forecasting and Dynamic Models, Springer Series in Statistics (Springer, New York).Google Scholar
- (2019b) A machine learning based personalized system for driving state recognition. Transportation Res. Part C Emerging Tech. 105:241–261.Crossref, Google Scholar
- (2019a) Implicit personalization in driving assistance: State-of-the-art and open issues. IEEE Trans. Intelligent Vehicles 5(3):397–413.Crossref, Google Scholar

