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access icon free Autonomous collision avoidance system based on accurate knowledge of the vehicle surroundings

© The Institution of Engineering and Technology
Received 30/06/2013, Accepted 31/05/2014, Revised 21/01/2014, Published 25/03/2014

In this study, a collision avoidance system is presented, based on the information provided by a laser-scanner sensor, in which two actions could be taken in case of danger. Firstly, the system tries to stop the vehicle in order to avoid the accident. If a reduction in speed is not sufficiently effective, the control system takes control of the steering and deviates the vehicle's trajectory in order to escape from the hazardous situation. The control system evaluates the situation and decides the most appropriate action in each case considering free areas on the surroundings using the information of a detailed digital map. This system has been implemented in a vehicle and has been tested with pedestrians and vehicles circulating along the private test track with satisfactory results.

Inspec keywords: road traffic control; road vehicles; collision avoidance; road accidents

Other keywords: digital map; autonomous collision avoidance system; private test track; steering control; vehicle surroundings; laser-scanner sensor; pedestrians; control system; vehicle trajectory deviation

Subjects: Road-traffic system control; Spatial variables control

References

    1. 1)
      • 36. Wender, S., Weiss, T., Fuerstenberg, K.Ch., Dietmayer, K.: Object classification exploiting high level maps of intersections, ‘Advanced Microsystems for Automotive Applications 2006', Springer, 2006, pp. 187–203.
    2. 2)
    3. 3)
      • 2. Aparicio, F., Arenas, B., Gómez, A., et al: Ingeniería del Transporte, (Dossat, 2008) (in Spanish).
    4. 4)
    5. 5)
      • 34. Available at http://www.euroncap.com/results/aeb.aspx.
    6. 6)
    7. 7)
    8. 8)
      • 8. Dietmayer, K., Sparbert, J., Streller, D.: ‘Model based object classification and object tracking in traffic scenes from range images’. IEEE Intelligent Vehicle Symp., Tokyo, 2001.
    9. 9)
      • 18. Kumar, R., Pathak, R.: ‘Adaptive cruise control – towards a safer driving experience’, Int. J. Sci. Eng. Res., 2012, 3, (8).
    10. 10)
      • 13. Rupp, J.D., King, A.G.: ‘Autonomous driving – a practical roadmap’. SAE Technical Paper, no 2010-01-2335, 2010.
    11. 11)
    12. 12)
      • 22. Available at http://www.media.ford.com/images/10031/APA_Toyota.pdf.
    13. 13)
      • 1. Elvik, R., Vaa, T.: ‘The handbook of road safety measures’ (Elsevier, 2004).
    14. 14)
    15. 15)
      • 9. Fürstenberg, K.Ch., Dietmayer, K.D.J., Eisenlauer, S.: ‘Multilayer laserscanner for robust object tracking and classification in urban traffic scenes’. Ninth World Congress on Intelligent Transport Systems, Chicago, 2002.
    16. 16)
      • 39. Wender, S., Schoenherr, M., Kaempchen, N., Dietmayer, K.: ‘Classification of laserscanner measurements at intersection scenarios with automatic parameter optimization’. IEEE Intelligent Vehicles Symp., Las Vegas, 2005, pp. 94–99.
    17. 17)
      • 23. Available at http://www.bosch-automotivetechnology.us/media/db_application/downloads/pdf/comfort_1/en_5/einparkenleichtgemachtparkassistenzsystemevonbosch.pdf.
    18. 18)
    19. 19)
    20. 20)
    21. 21)
      • 25. Ramesh, S., Ranjan, R., Mukherjee, R., Chaudhuri, S.: ‘Vehicle collision avoidance system using wireless sensor networks’, Int. J. Soft Comput. Eng. (IJSCE), 2012, 2, (5), pp. 300–303.
    22. 22)
      • 19. Available at http://www.audi.co.uk/new-cars/a7/a7-sportback/driver-assistants/adaptive-cruise-control.html.
    23. 23)
    24. 24)
    25. 25)
    26. 26)
    27. 27)
    28. 28)
      • 42. Miller, R., Huang, Q.: ‘An adaptive peer-to-peer collision warning system’. IEEE 55th Vehicular Technology Conf., 2002.
    29. 29)
    30. 30)
      • 21. Endo, T., Iwazaki, K., Tanaka, Y.: ‘Development of reverse parking assist with automatic steering’. 10th World Congress on ITS, Madrid, 2003.
    31. 31)
      • 40. Sparbert, J., Dietmayer, K., Streller, D.: ‘Lane detection and street type classification using laser range images’. Fourth Int. IEEE Conf. Intelligent Transportation Systems, Oakland, 2001.
    32. 32)
    33. 33)
    34. 34)
    35. 35)
      • 41. Streller, D., Dietmayer, K., Sparbert, J.: ‘Object tracking in traffic scenes with multi-hypothesis approach using laser range images’. Int. Conf. Intelligent Transportation Systems, Sydney, 2001.
    36. 36)
    37. 37)
      • 3. European Enhanced Vehicle Safety Committee. ‘WG 19, Primary and secondary safety interaction’. Final Report, EEVC, 2004.
    38. 38)
    39. 39)
      • 38. Wender, S., Fuerstenberg, K.Ch., Dietmayer, K.: ‘Object tracking and classification for intersection scenarios using a multilayer laserscanner’. 11th World Congress on Intelligent Transportation Systems, Nagoya (Japan), 2004.
    40. 40)
      • 20. EURO-FOT Project. Available at http://www.eurofot-ip.eu/en/intelligent_vehicle_systems/acc/.
    41. 41)
    42. 42)
    43. 43)
      • 28. Zohdy, I., Rakha, H.: ‘Optimizing driverless vehicles at intersections’. 19th ITS World Congress, Vienna (Austriax), 22–26 October 2012.
    44. 44)
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