References

• 1. 1)
     • 10. ‘Deep Learning Makes Driverless Cars Better at Spotting Pedestrians’, http://spectrum.ieee.org/cars-that-think/transportation/advanced-cars/deep-learning-makes-driverless-cars-better-at-spotting-pedestrians, accessed 12 April 2017.
  2. 2)
     • 17. Broggi, A., Buzzoni, M., Debattisti, S., et al: ‘Extensive tests of autonomous driving technologies’, IEEE Trans. Intell. Transp. Syst., 2013, 14, (3), pp. 1403–1415.
  3. 3)
     • 12. Stellet, J., Zofka, M., Schumacher, J., et al: ‘Testing of advanced driver assistance towards automated driving: a survey and taxonomy on existing approaches and open questions’. 2015 IEEE 18th Int. Conf. on Intelligent Transportation Systems, Canary Islands, Spain, September 2015, pp. 1455–1462.
  4. 4)
     • 13. Bengler, K., Dietmayer, K., Farber, B., et al: ‘Three decades of driver assistance systems: review and future perspectives’, IEEE Intell. Transp. Syst. Magaz., winter, 2014, 6, (4), pp. 6–22.
  5. 5)
     • 22. Sato, N., Ikeda, K.: ‘Three types of forward pruning techniques to apply the alpha beta algorithm to turn-based strategy games’. 2016 IEEE Conf. on Computational Intelligence and Games (CIG), Santorini, Greece, September 2016, pp. 1–8.
  6. 6)
     • 1. ‘How Google's Self-Driving Car Works’, http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/how-google-self-driving-car-works, accessed 12 April 2017.
  7. 7)
     • 16. Zofka, M., Klemm, S., Kuhnt, F., et al: ‘Testing and validating high level components for automated driving: simulation framework for traffic scenarios’. In 2016 IEEE Intelligent Vehicles Symp. (IV), Gothenburg, Sweden, June 2016, pp. 144–150.
  8. 8)
     • 21. Baudet, G.: ‘An analysis of the full alpha-beta pruning algorithm’. In Proc. of the Tenth Annual ACM Symp. on Theory of Computing, STOC'78, New York, NY, USA, 1978, pp. 296–313.
  9. 9)
     • 4. Pereira, J., Rossetti, R.: ‘An integrated architecture for autonomous vehicles simulation’. Proc. of the 27th Annual ACM Symp. on Applied Computing, SAC ‘12, New York, NY, USA, 2012, pp. 286–292.
  10. 10)
      • 5. Ballance, W., Vilkomir, S., Jenkins, W.: ‘Effectiveness of pair-wise testing for software with boolean inputs’. 2012 IEEE Fifth Int. Conf. on Software Testing, Verification and Validation, Montreal, Canada, April 2012, pp. 580–586.
  11. 11)
      • 24. Marczuk, K., Hong, H., Azevedo, C., et al: ‘Autonomous mobility on demand in simmobility: case study of the central business district in Singapore’. 2015 IEEE 7th Int. Conf. on Cybernetics and Intelligent Systems (CIS) and IEEE Conf. on Robotics, Automation and Mechatronics (RAM), Angkor Wat, Cambodia, July 2015, pp. 167–172.
  12. 12)
      • 2. ‘Apple Aims to Test Self-Driving Cars at Military Base’, http://spectrum.ieee.org/cars-that-think/transportation/self-driving/apple-aims-to-test-selfdriving-car-at-military-base, accessed 12 April 2017.
  13. 13)
      • 11. Berger, C., Chaudron, M., Heldal, R., et al: ‘Model-based, composable simulation for the development of autonomous miniature vehicles’. Proc. of the Symp. on Theory of Modeling & Simulation - DEVS Integrative M&S Symp., DEVS 13, San Diego, CA, USA, 2013, pp. 17:1–17:8.
  14. 14)
      • 8. Abdessalem, R., Nejati, S., Briand, L., et al: ‘Testing advanced driver assistance systems using multi-objective search and neural networks’. 2016 31st IEEE/ACM Int. Conf. on Automated Software Engineering (ASE), Singapore, September 2016, pp. 63–74.
  15. 15)
      • 18. ‘After Mastering Singapore ‘s Streets, NuTonomy ‘s Robo-t-axis Are Poised to Take on New Cities’, http://spectrum.ieee.org/transportation/self-driving/after-mastering-singapores-streets-nutonomys-robotaxis-are-poised-to-take-on-new-cities, accessed 12 April 2017.
  16. 16)
      • 23. Bento, L., Parafita, R., Nunes, U.: ‘Intelligent traffic management at intersections supported by v2v and v2i communications’. 2012 15th Int. IEEE Conf. on Intelligent Transportation Systems, Anchorage, AK, USA, September 2012, pp. 1495–1502.
  17. 17)
      • 9. Althoff, M., Stursberg, O., Buss, M.: ‘Model-based probabilistic collision detection in autonomous driving’, IEEE Trans. Intell. Transp. Syst., 2009, 10, (2), pp. 299–310.
  18. 18)
      • 25. ISO/IEC/IEEE 29119: ‘Software and systems engineering – software testing – part 4:test techniques’, 2015..
  19. 19)
      • 6. Mirzaei, N., Garcia, J., Bagheri, H., et al: ‘Reducing combinatorics in gui testing of android applications’. 2016 IEEE/ACM 38th Int. Conf. on Software Engineering (ICSE), Austin, TX, USA, May 2016, pp. 559–570.
  20. 20)
      • 14. Zhao, D., Lam, H., Peng, H., et al: ‘Accelerated evaluation of automated vehicles safety in lane-change scenarios based on importance sampling techniques’, IEEE Trans. Intell. Transp. Syst., 2017, 18, (3), pp. 595–607.
  21. 21)
      • 19. Liu, B., Sun, N., Zhang, Q., et al: ‘A surrogate model assisted evolutionary algorithm for computationally expensive design optimization problems with discrete variables’. 2016 IEEE Congress on Evolutionary Computation(CEC), Vancouver, Canada, July 2016, pp. 1650–1657.
  22. 22)
      • 3. ‘Toyota's Robot-Car Line In The Sand: 8.8 Billion Test Miles To Ensure Safety’, https://www.forbes.com/sites/alanohnsman/2016/10/03/toyotas-robot-car-line-in-the-sand-8-8-billion-test-miles-to-ensure-safety/#21d0f63f16f0, accessed 12 April 2017.
  23. 23)
      • 7. Kuhn, D., Wallace, D., Gallo, A.: ‘Software fault interactions and implications for software testing’, IEEE Trans. Softw. Eng., 2004, 30, (6), pp. 418–421.
  24. 24)
      • 20. Fuller, S., Gaschnig, J., Gillogly, J.J., et al: ‘Analysis of the alpha-beta pruning algorithm’. Research Showcase @ CMU, Pittsburgh, PA, USA, 1973..
  25. 25)
      • 15. Sebastian, A., Tang, M., Feng, Y., et al: ‘Multi-vehicles interaction graph model for cooperative collision warning system’. Intelligent Vehicles symp. 2009 IEEE, Xi'an, China, June 2009, pp. 929–934.