access icon free Developing a simulation framework for safe and optimal trajectories considering drivers’ driving style

Advanced driving assistance systems (ADAS) have huge potential for improving road safety and travel times. However, their take-up in the market is very slow; and these systems should consider driver's preferences to increase adoption rates. The aim of this study is to develop a model providing drivers with the optimal trajectory considering the motorist's driving style in real time. Travel duration and safety are the main parameters used to find the optimal trajectory. A simulation framework to determine the optimal trajectory was developed in which the ego car travels in a highway environment scenario, using an agent-oriented approach. The performance of the algorithm was compared against optimal trajectories computed offline with the hybrid A* algorithm. The new framework provides trajectories close to the optimal trajectory and is computationally achievable. The agents were shown to follow safe and fast trajectories in three tests scenarios: emergency braking, overtaking and a complex situation with multiple vehicles around the ego vehicle. Different driver profiles were then tested in the complex scenario, showing that the proposed approach can adapt to driver preferences and provide a solution close to the optimal solution given the defined safety constraints.

Inspec keywords: road vehicles; road safety; driver information systems; object-oriented programming; software agents

Other keywords: optimal trajectories; advanced driving assistance systems; agent-oriented approach; drivers preferences; optimal trajectory; optimal trajectories considering drivers driving style; travel times; ADAS; road safety; ego car travels; simulation framework; defined safety constraints; adoption rates; ego vehicle

Subjects: Object-oriented programming; Expert systems and other AI software and techniques; Traffic engineering computing

References

    1. 1)
      • 8. Björklund, G.M., Åberg, L.: ‘Driver behaviour in intersections: formal and informal traffic rules’, Transport. Res. F Traffic Psychol. Behav., 2005, 8, (3), pp. 239253.
    2. 2)
      • 6. Rittger, D.M.L., Maag, C., Kiesel, A.: ‘Anger and bother experience when driving with a traffic light assistant: a multi-driver simulator study’, in de Waard, J.S.D., Röttger, S., Kluge, A., Manzey, D., Weikert, C., Hoonhout, H. (Ed.): ‘Human factors in high reliability industries’ (2015), pp. 4152.
    3. 3)
      • 19. Montemerlo, M.,, Becker, J., Bhat, S., et al: ‘Junior: the Stanford entry in the urban challenge’, J. Field Robot., 2008, 25, (9), pp. 569597.
    4. 4)
      • 16. Lim, H., Su, W., Mi, C.C.: ‘Distance-based ecological driving scheme using a two-stage hierarchy for long-term optimization and short-term adaptation’, IEEE Trans. Veh. Technol., 2017, 66, (3), pp. 19401949.
    5. 5)
      • 1. Bureau of Infrastructure, Transport and Regional Economics [BITRE]: ‘Cost of road crashes in Australia 2006’. CanberraReport 118, 2009.
    6. 6)
      • 10. Muhrer, E., Vollrath, M.: ‘Expectations while car following–The consequences for driving behaviour in a simulated driving task’, Accid. Anal. Prevent., 2010, 42, (6), pp. 21582164.
    7. 7)
      • 15. Glaser, S., Vanholme, B., Mammar, S.,, et al: ‘Maneuver-based trajectory planning for highly autonomous vehicles on real road with traffic and driver interaction’, IEEE Trans. Intell. Transport. Syst., 2010, 11, (3), pp. 589606.
    8. 8)
      • 24. Hobbs, C.A., Mills, P.J.: ‘Injury probability for car occupants in frontal and side impacts’ (Transport and Road Research Laboratory. Road Safety Division, 1984).
    9. 9)
      • 21. Michon, J.A.: ‘A critical view of driver behavior models: what do we know, what should we do?’, in Evans, L., Schwing, R.C. (Eds.): ‘Human behavior and traffic safety’ (Springer US, Boston, MA, 1985), pp. 485524.
    10. 10)
      • 22. Glaser, S., Vanholme, B., Gruyer, D.,, et al: ‘Probability and risk based maneuver planning for collision avoidance’. Presented at the First Int. Symp. Future Active Safety Technology toward zero-traffic-accident, Tokyo, Japan, 5–9 September 2011.
    11. 11)
      • 30. Queensland Government: ‘Safe following distances’, 2010. Available at http://www.tmr.qld.gov.au/Safety/Queensland-road-rules/Road-rules-refresher/Safe-following-distances.aspx.
    12. 12)
      • 4. Miller, G., Taubman – Ben-Ari, O.: ‘Driving styles among young novice drivers–The contribution of parental driving styles and personal characteristics’, Accid. Anal. Prevent., 2010, 42, (2), pp. 558570.
    13. 13)
      • 9. Martens, M.H.: ‘Stimuli fixation and manual response as a function of expectancies’, Hum. Factors, 2004, 46, (3), pp. 410423.
    14. 14)
      • 2. World Health Organization: ‘Global status report on road safety 2013: supporting a decade of action’, 2013, Available at http://www.who.int/violence_injury_prevention/road_safety_status/2013/en/.
    15. 15)
      • 28. Vanholme, B., Gruyer, D., Lusetti, B.,, et al: ‘Highly automated driving on highways based on legal safety’, IEEE Trans. Intell. Transport. Syst., 2013, 14, (1), pp. 333347.
    16. 16)
      • 18. Bonsall, P., Liu, R., Young, W.: ‘Modelling safety-related driving behaviour – impact of parameter values’, Transport. Res. A, 2005, 39, (1), pp. 425444.
    17. 17)
      • 17. Moser, D., Schmied, R., Waschl, H.,, et al: ‘Flexible spacing adaptive cruise control using stochastic model predictive control’, IEEE Trans. Control Syst. Technol., 2017, PP, (99), pp. 114.
    18. 18)
      • 7. Preuk, K., Stemmler, E., Schießl, C.,, et al: ‘Does assisted driving behavior lead to safety-critical encounters with unequipped vehicles’ drivers?’, Accid. Anal. Prevent., 2016, 95, (Part A), pp. 149156.
    19. 19)
      • 29. Gipps, P.G.: ‘A behavioural car-following model for computer simulation’, Transport. Res. B Methodol., 1981, 15, (2), pp. 105111.
    20. 20)
      • 14. Mensing, F., Bideaux, E., Trigui, R.,, et al: ‘Trajectory optimization for eco-driving taking into account traffic constraints’, Transport. Res. D Transport Environ., 2013, 18, pp. 5561.
    21. 21)
      • 20. Thrun, S.,, Montemerlo, M., Dahlkamp, H., et al: ‘Stanley: the robot that won the DARPA grand challenge’, J. Field Robot., 2006, 23, (9), pp. 661692.
    22. 22)
      • 13. Saerens, B., Van den Bulck, E.: ‘Calculation of the minimum-fuel driving control based on pontryagin's maximum principle’, Transport. Res. D Transport Environ., 2013, 24, pp. 8997.
    23. 23)
      • 27. Russell, S.J., Norvig, P.: ‘Artificial intelligence: a modern approach(no. Book, Whole) (Pearson, Harlow, 2014).
    24. 24)
      • 11. Eis, V., Sferco, R., Fay, P.A.: ‘A detailed analysis of the characteristics of European rear impacts’. Proc.: Int. Technical Conf. Enhanced Safety of Vehicles, 2005, vol. 2005, pp. 9p9p.
    25. 25)
      • 12. Smart Cities and Communities – Key to Innovation Integrated Solution–Cooperative Intelligent Transport Systems and Services (C-ITS), 2013. Available at https://eu-smartcities.eu/sites/all/files/Cooperative%20Intelligent%20Transport%20Systems%20and%20Services%20-%20Smart%20Cities%20Stakeholder%20Platform%20january.pdf.
    26. 26)
      • 26. Kesting, A., Treiber, M.: ‘Traffic flow dynamics: data, models and simulation(no. Book, Whole) (Springer Berlin Heidelberg, Berlin, Heidelberg, 2013).
    27. 27)
      • 5. Son, J., Park, M., Oh, H.,, et al: ‘1G-33 age and gender difference in driving style and fuel efficiency on highway driving’, Jpn. J. Ergonom., 2013, 49, (Suppl.), pp. S548S551.
    28. 28)
      • 23. Vogel, K.: ‘A comparison of headway and time to collision as safety indicators’, Accid. Anal. Prevent., 2003, 35, (3), pp. 427433.
    29. 29)
      • 31. Kulmala, R.: ‘Ex-ante assessment of the safety effects of intelligent transport systems’, Accid. Anal. Prevent., 2010, 42, (4), pp. 13591369.
    30. 30)
      • 25. Zeidler, F., Schreier, H., Stadelmann, R.: ‘Accident research and accident reconstruction by the EES-accident reconstruction method’. SAE Technical Paper, 1985.
    31. 31)
      • 3. Shinar, D.: ‘Psychology on the road, the human factor in traffic safety’ (John Wiley and Sons, New York, USA, 1978), p. 212.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2017.0046
Loading

Related content

content/journals/10.1049/iet-its.2017.0046
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading