Abstract
Autonomous vehicles are considered to be the next big thing. Several companies are racing to put self-driving vehicles on the road by 2020. Regulations and standards are not ready for such a change. New technologies, such as the intensive use of machine learning, are bringing new solutions but also opening new challenges. This chapter reports the state of the art, future trends, and challenges of autonomous vehicles, with a special focus on software. One of the major challenges we further elaborate on is using machine learning techniques in order to deal with uncertainties that characterize the environments in which autonomous vehicles will need to operate while guaranteeing safety properties.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Argall BD, Chernova S, Veloso M, Browning B (2009) A survey of robot learning from demonstration. Robot Auton Syst 57(5):469–483
Autili M, Cortellessa V, Di Ruscio D, Inverardi P, Pelliccione P, Tivoli M (2011) Eagle: engineering software in the ubiquitous globe by leveraging uncertainty. In: Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on foundations of software engineering, ESEC/FSE ’11. ACM, New York, NY, pp 488–491. https://doi.org/10.1145/2025113.2025199
Bacha A, Bauman C, Faruque R, Fleming M, Terwelp C, Reinholtz C, Hong D, Wicks A, Alberi T, Anderson D, Cacciola S, Currier P, Dalton A, Farmer J, Hurdus J, Kimmel S, King P, Taylor A, Covern DV, Webster M (2008) Odin: team VictorTango’s entry in the DARPA urban challenge. J Field Robot 25(9):467–492. https://doi.org/10.1002/rob.v25:8
Baker CR, Dolan JM (2008) Traffic interaction in the urban challenge: putting boss on its best behavior. In: IROS, pp 1752–1758
Behere S, Törngren M (2016) A functional reference architecture for autonomous driving. Inf Softw Technol 73:136–150
Berger C, Dukaczewski M (2014) Comparison of architectural design decisions for resource-constrained self-driving cars – a multiple case-study. In: Plödereder E, Grunske L, Schneider E, Ull D (eds) Proceedings of the INFORMATIK 2014, Gesellschaft für Informatik e.V. (GI), Stuttgart, pp 2157–2168. http://subs.emis.de/LNI/Proceedings/Proceedings232/2157.pdf
Berger C, Rumpe B (2012) Autonomous driving – 5 years after the urban challenge: the anticipatory vehicle as a cyber-physical system. In: Goltz U, Magnor M, Appelrath HJ, Matthies HK, Balke WT, Wolf L (eds) Proceedings of the INFORMATIK 2012, Braunschweig, pp 789–798. https://arxiv.org/pdf/1409.0413v1.pdf
Berger C, Rumpe B (2012) Engineering autonomous driving software. In: Rouff C, Hinchey M (eds) Experience from the DARPA urban challenge. Springer, London, pp 243–271. https://doi.org/10.1007/978-0-85729-772-3_10
Berger C, Nguyen B, Benderius O (2017) Containerized development and microservices for self-driving vehicles: experiences & best practices. In: Proceedings of the third international workshop on automotive software architectures (WASA), p 6
Berns A, Ghosh S (2009) Dissecting self-* properties. In: Third IEEE international conference on self-adaptive and self-organizing systems, 2009. SASO’09. IEEE, Piscataway, pp 10–19
Bila C, Sivrikaya F, Khan MA, Albayrak S (2017) Vehicles of the future: a survey of research on safety issues. IEEE Trans Intell Transp Syst 18(5):1046–1065
Bojarski M, Del Testa D, Dworakowski D, Firner B, Flepp B, Goyal P, Jackel LD, Monfort M, Muller U, Zhang J, et al (2016) End to end learning for self-driving cars. arXiv preprint arXiv:160407316
Brun Y, Serugendo GDM, Gacek C, Giese H, Kienle HM, Litoiu M, Müller HA, Pezzè M, Shaw M (2009) Engineering self-adaptive systems through feedback loops. In: Software engineering for self-adaptive systems, vol 5525. Springer, Berlin, pp 48–70
Carlson A, Betteridge J, Kisiel B, Settles B, Hruschka ER Jr, Mitchell TM (2010) Toward an architecture for never-ending language learning. In: AAAI, vol 5, p 3
Chavez-Garcia RO, Aycard O (2016) Multiple sensor fusion and classification for moving object detection and tracking. IEEE Trans Intell Transp Syst 17(2):525–534
Cheng BH, Giese H, Inverardi P, Magee J, de Lemos R, Andersson J, Becker B, Bencomo N, Brun Y, Cukic B, et al (2008) Software engineering for self-adaptive systems: a research road map. In: Dagstuhl seminar proceedings, Schloss Dagstuhl-Leibniz-Zentrum für Informatik
de Lemos R et al (2013) Software engineering for self-adaptive systems: a second research roadmap. In: Software engineering for self-adaptive systems II. Springer, Berlin, pp 1–32
Dollar P, Wojek C, Schiele B, Perona P (2012) Pedestrian detection: an evaluation of the state of the art. IEEE Trans Pattern Anal Mach Intell 34(4):743–761
El Sallab A, Abdou M, Perot E (2017) Deep reinforcement learning framework for autonomous driving. In: Electronic imaging. Autonomous vehicles and machines
Eliasson U, Heldal R, Lantz J, Berger C (2014) Agile model-driven engineering in mechatronic systems-an industrial case study. In: International conference on model driven engineering languages and systems. Springer, Cham, pp 433–449
Esfahani N, Malek S (2013) Uncertainty in self-adaptive software systems. Springer, Berlin, pp 214–238. https://doi.org/10.1007/978-3-642-35813-5_9
Fleming B (2014) An overview of advances in automotive electronics [automotive electronics]. IEEE Veh Technol Mag 9(1):4–9. https://doi.org/10.1109/MVT.2013.2295285
Florbäck J, Tornberg L, Mohammadiha N (2016) Offline object matching and evaluation process for verification of autonomous driving. In: International conference on intelligent transportation systems (ITSC), pp 107–112. https://doi.org/10.1109/ITSC.2016.7795539
Garlan D (2010) Software engineering in an uncertain world. In: Proceedings of the FSE/SDP workshop on future of software engineering research, FoSER ’10. ACM, New York, NY, pp 125–128. https://doi.org/10.1145/1882362.1882389
Giaimo F, Berger C (2017) Design criteria to architect continuous experimentation for self-driving vehicles. In: Proceedings of the international conference on software architecture (ICSA), pp 203–210. http://arxiv.org/abs/1705.05170
Hammett R (2001) Design by extrapolation: an evaluation of fault-tolerant avionics. In: 20th DASC. 20th Digital avionics systems conference (Cat. No.01CH37219), vol 1, pp 1C5/1–1C5/12. https://doi.org/10.1109/DASC.2001.963314
ISO/IEC (2011) ISO/IEC/IEEE 42010:2011 Systems and software engineering – architecture description. https://www.iso.org/standard/50508.html
Kalra N, Paddock SM (2016) Driving to safety: how many miles of driving would it take to demonstrate autonomous vehicle reliability? Transp Res A Policy Pract 94:182–193
Kephart JO, Chess DM (2003) The vision of autonomic computing. Computer 36(1):41–50
Knauss E, Pelliccione P, Heldal R, gren M, Hellman S, Maniette D (2016) Continuous integration beyond the team: a tooling perspective on challenges in the automotive industry. In: Proceedings of ESEM ’16. ACM, New York. https://doi.org/10.1145/2961111.2962639
Knauss A, Schroeder J, Berger C, Eriksson H (2017) Paving the roadway for safety of automated vehicles: an empirical study on testing challenges. In: Proceedings of intelligent vehicle symposium (IV)
Koopman P, Wagner M (2016) Challenges in autonomous vehicle testing and validation. Technical report, Carnegie Mellon University; Edge Case Research LLC
Koopman P, Wagner M (2016) Challenges in autonomous vehicle testing and validation. SAE Int J Transp Saf 4(1):15–24
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
Lu Z, Happee R, Cabrall C, Kyriakidis M, de Winter J (2016) Human factors of transitions in automated driving: a general framework and literature survey. Transp Res F 43:183–198
Maldonado-Bascon S, Lafuente-Arroyo S, Gil-Jimenez P, Gomez-Moreno H, López-Ferreras F (2007) Road-sign detection and recognition based on support vector machines. IEEE Trans Intell Transp Syst 8(2):264–278
Mallozzi P (2017) Combining machine-learning with invariants assurance techniques for autonomous systems. In: Proceedings of the 39th international conference on software engineering companion. IEEE, Piscataway, pp 485–486
Martens M, van den Beukel A (2013) The road to automated driving: dual mode and human factors considerations. In: Proceedings of conference on intelligent transportation systems, pp 2262–2267
Masek P, Thulin M, Andrade H, Berger C, Benderius O (2016) Systematic evaluation of sandboxed software deployment for real-time software on the example of a self-driving heavy vehicle. In: Proceedings of the 19th IEEE intelligent transportation systems conference (ITSC), pp 2398–2403. https://doi.org/10.1109/ITSC.2016.7795942, http://ieeexplore.ieee.org/abstract/document/7795942/
Montemerlo M, Thrun S, Koller D, Wegbreit B, et al (2002) Fastslam: a factored solution to the simultaneous localization and mapping problem. In: Aaai/iaai, pp 593–598
Montemerlo M, Becker J, Bhat S, Dahlkamp H, Dolgov D, Ettinger S, Haehnel D, Hilden T, Hoffmann G, Huhnke B, Johnston D, Klumpp S, Langer D, Levandowski A, Levinson J, Marcil J, Orenstein D, Paefgen J, Penny I, Petrovskaya A, Pflueger M, Stanek G, Stavens D, Vogt A, Thrun S (2008) Junior: the Stanford entry in the urban challenge. J Field Robot 25(9):569–597. https://doi.org/10.1002/rob.v25:9, http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6681152
Nassim NT (2007) The black swan: the impact of the highly improbable. Random House, New York
Panahandeh G, Ek E, Mohammadiha N (2017) Road friction estimation for connected vehicles using supervised machine learning. In: IEEE intelligent vehicles symposium (IV)
Parno B, Perrig A (2005) Challenges in securing vehicular networks. In: Workshop on hot topics in networks (HotNets-IV)
Pelliccione P, Knauss E, Heldal R, Ågren SM, Mallozzi P, Alminger A, Borgentun D (2017) Automotive architecture framework: the experience of Volvo cars. J Syst Archit 77:83–100. https://doi.org/10.1016/j.sysarc.2017.02.005, http://www.sciencedirect.com/science/article/pii/S1383762117300954
Pelliccione P, Kobetski A, Larsson T, Aramrattan M, Aderum T, gren M, Jonsson G, Heldal R, Bergenhem C, Thorsén A (2017) Architecting cars as constituents of a system of systems. In: Software-intensive systems-of-systems. ACM, New York
Pinheiro P, Collobert R (2014) Recurrent convolutional neural networks for scene labeling. In: International conference on machine learning, pp 82–90
Rauskolb FW, Berger K, Lipski C, Magnor M, Cornelsen K, Effertz J, Form T, Graefe F, Ohl S, Schumacher W, Wille JM, Hecker P, Nothdurft T, Doering M, Homeier K, Morgenroth J, Wolf L, Basarke C, Berger C, Gülke T, Klose F, Rumpe B (2008) Caroline: an autonomously driving vehicle for urban environments. J Field Robot 25(9):674–724. https://doi.org/10.1002/rob.20254
Russell S, Norvig P, Intelligence A (1995) A modern approach. Artificial Intelligence Prentice-Hall, Englewood Cliffs, pp 25–27
Salehie M, Tahvildari L (2009) Self-adaptive software: landscape and research challenges. ACM Trans Auton Adapt Syst 4(2):14
Schmittner C, Ma Z, Gruber T (2014) Standardization challenges for safety and security of connected, automated and intelligent vehicles. In: 2014 International conference on connected vehicles and expo (ICCVE), pp 941–942
Sculley D, Holt G, Golovin D, Davydov E, Phillips T, Ebner D, Chaudhary V, Young M, Crespo JF, Dennison D (2015) Hidden technical debt in machine learning systems. In: Advances in neural information processing systems, pp 2503–2511
Shalev-Shwartz S, Ben-Zrihem N, Cohen A, Shashua A (2016) Long-term planning by short-term prediction. arXiv preprint arXiv:160201580
Ståhl D, Bosch J (2014) Modeling continuous integration practice differences in industry software development. J Syst Softw 87:48–59. https://doi.org/10.1016/j.jss.2013.08.032
Sutcliffe A, Sawyer P (2013) Requirements elicitation: towards the unknown unknowns. In: 2013 21st IEEE international requirements engineering conference (RE). IEEE, Piscataway, pp 92–104
Sutton RS, Barto AG (1998) Reinforcement learning: an introduction, vol 1. MIT Press, Cambridge
Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2016) Inception-v4, inception-resnet and the impact of residual connections on learning. arXiv preprint arXiv:160207261
Tashvir A, Sjöberg J, Mohammadiha N (2017) Sensor error prediction and anomaly detection using neural networks. In: The first Swedish symposium on deep learning (SSDL)
Thrun S, Montemerlo M, Dahlkamp H, Stavens D, Aron A, Diebel J, Fong P, Gale J, Halpenny M, Hoffmann G, Lau K, Oakley C, Palatucci M, Pratt V, Stang P, Strohband S, Dupont C, Jendrossek LE, Koelen C, Markey C, Rummel C, van Niekerk J, Jensen E, Alessandrini P, Bradski G, Davies B, Ettinger S, Kaehler A, Nefian A, Mahoney P (2006) Stanley: the robot that won the DARPA grand challenge. J Field Robot 23(9):661–692. https://doi.org/10.1002/rob.20147
Wintersbeger P, Green P, Riener A (2017) Am I driving or are you or are we both? A taxonomy for handover and handback in automated driving. In: Proceedings of driving assessment conference
Zeeb K, Buchner A, Schrauf M (2015) What determines the take-over time? An integrated model approach of driver take-over after automated driving. Accid Anal Prev 78:212–221
Zeeb K, Buchner A, Schrauf M (2016) Is take-over time all that matters? The impact of visual-cognitive load on driver take-over quality after conditionally automated driving. Accid Anal Prev 92:230–239
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Mallozzi, P., Pelliccione, P., Knauss, A., Berger, C., Mohammadiha, N. (2019). Autonomous Vehicles: State of the Art, Future Trends, and Challenges. In: Dajsuren, Y., van den Brand, M. (eds) Automotive Systems and Software Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-12157-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-12157-0_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-12156-3
Online ISBN: 978-3-030-12157-0
eBook Packages: Computer ScienceComputer Science (R0)