Collision Avoidance Via Emergency Steering Warning System: A Driving Simulator Approach

Öz

This study analyzes the viability of an Emergency Steering Warning System (ESWS) to improve the safety of vehicles on highways traveling in the same direction. The proposed system evaluates the vehicle’s physical limits, driver’s reaction and assists in making the most logical decision to avoid a crash using a sound or a similar stimulus. Typical driving simulator events were designed in MATLAB/Simulink and IPG/CarMaker co-simulation environment. In the predetermined scenario, the leading vehicles suddenly move into the host vehicle’s lane and the driver is expected to avoid crash by either steering or braking. The ESWS system generates a sound stimulus when it is determined that the crash is unavoidable with the use of service brakes and the only way to avoid the obstacle is steering. The simulation events were performed by a group of participants using a driver simulator with and without the ESWS system. The proposed ESWS encouraged participants to do an earlier and smoother steering maneuver which can be advantageous in some certain critical traffic situations. The statistical results showed that the sound stimulus reduced the drivers’ reaction time significantly and a number of accidents can be avoided by the suggested crash warning system.

Anahtar Kelimeler

• MATLAB/Simulink,steering warning,autonomous control systems,vehicle control systems,advanced driver assistance systems

Kaynakça

1. [1] Thierry P., Kassaagi M. and Brissart G., “Active Safety Experiments with Common Drivers for the Specification of Active Safety Systems”, 2001-06-0004. Society of Automotive Engineers, (2001).
2. [2] Green M., “How long does it take to stop? Methodological analysis of driver perception-brake times”, Transportation Human Factors, 2(3): 195-216, (2000).
3. [3] Lee J., McGehee D., Brown T. and Reyes M., “Collision warning timing, driver distraction, and driver response to imminent rear-end collisions in a high-fidelity driving simulator”, Human Factors, 44: 314–335, (2002).
4. [4] Choi C., Kang Y. and Lee S., “Emergency Collision Avoidance Maneuver based on Nonlinear Model Predictive Control”, IEEE International Conference on Vehicular Electronics and Safety, (2012).
5. [5] Jansson J. and Johansson J., “Decision Making for Collision Avoidance Systems”, SAE Technical Paper Series, 2002-01-0403, (2002).
6. [6] Markkula G., Benderius O., Wolff K. and Wahde, M., “A Review of Near-Collision Driver Behavior Models”, Human Factors, 54: 1117-1143, (2012).
7. [7] Wiacek C. and Najm W., “Driver/vehicle characteristics in rear-end pre-crash scenarios based on the general estimates system”, SAE Technical Paper Series, 1999-01-0817, (1999).
8. [8] Lechner D. and Van Elslande P., “Comportement du conducteur en situation d’accident [Driver behavior in accident situations]”, SAE Technical Paper Series, (1997).

9. [9] Engström J., Aust M. and Viström M., “Effects of Working Memory Load and Repeated Scenario Exposure on Emergency Braking Performance”, Human Factors, 52: 551, (2010).
10. [10] Itoh M., Horikome T. and Inagaki T., “Effectiveness and driver acceptance of a semi- autonomous forward obstacle collision avoidance system”, Proceedings of the Human Factors and Ergonomics Society 54th annual meeting, Santa Monica, CA, (2010).
11. [11] Gurov A., Sengupta A. and Jonasson M. and Drugge L., “Collision avoidance driver assistance system using combined active braking and steering”, Proceedings of the 12th International Symposium on Advanced Vehicle Control, Tokyo, (2014).
12. [12] Lu Q., Sorniotti A., Gruber P. and Smet J., “H∞ loop shaping for the torque-vectoring control of electric vehicles: Theoretical design and experimental assessment”, Mechatronics, (2016).
13. [13] Zhang N., Li P., Yin G., Chen N. and Li Y., “Application of hilbert transform in vehicle dynamics analysis”, IEEE International Conference on Vehicle Electronics and Safety, Jeddah, (2016).
14. [14] Gordon T., Gao Y. and Lidberg M., “Implementation of a modified Hamiltonian algorithm for control allocation”, Proceedings of the 13th International Symposium on Advanced Vehicle Control, Munich, (2016).
15. [15] Wang Y. and Winner H., “Estimation of vehicle yaw moment of inertia in dynamic road test using Wheel Force Sensor”, Proceedings of the 24th Symposium of the International Association for Vehicle System Dynamics, Graz, (2015).
16. [16] Ruscio D., Ciceri M. and Biassoni F., “How does a collision warning system shape driver's brake response time? The influence of expectancy and automation complacency on real-life emergency braking”, Accident Analysis and Prevention, 77: 72–81.11, (2015).
17. [17] Eckert A., Hartmann B., Sevenich M. and Rieth E., “Emergency steer & brake assist: A systematic approach for system integration of two complementary driver assistance systems”, Continental AG, Paper Number 11-0111, (2011).
18. [18] Brebner J. and Welford A., “Introduction: an historical background sketch. Pages 1-23”, Reaction times, Academic Press, New York, (1980).
19. [19] Fieandt K., Huhtala A., Kullberg P. and Saarl K., “Personal tempo and phenomenal time at different age levels”, Reports from the Psychological Institute, No. 2, University of Helsinki, (1956).
20. [20] Eidehall A., Pohl J., Gustafsson F. and Ekmark J., “Toward Autonomous Collision Avoidance by Steering”, IEEE Transactions on Intelligent Transportation Systems, 8(1): (2007).
21. [21] Maag C., Schneider N., Lübbeke T., Weisswange T. and Goerick C., “Car Gestures – Advisory warning using additional steering wheel angles”, Accident Analysis and Prevention, 143–153, (2015).
22. [22] Veldhuizen T., “Yaw rate feedback by active rear wheel steering”, [dissertation], Eindhoven: Technische Universiteit Eindhoven, (2007).
23. [23] Benderius O., “Modelling driver steering and neuromuscular behaviour”, [dissertation], Chalmers: Chalmers University of Technology, (2014).