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Abstract

A motivational driver model is developed to design a rear-end crash avoidance system. Current driver assistance systems use engineering methods without considering psychological human aspects, which leads to false activation of assistance systems and complicated control algorithms. The presented driver model estimates driver’s psychological motivations using the combined longitudinal and lateral time to collision, the vehicle kinematics, and the vehicle dynamics. These motivations simplify both autonomous driving algorithms and human-machine interactions. The optimal point of a motivational multi-objective cost function defines the decision for the autonomous driving. Moreover, the motivations are used as risk assessment factors for driver–machine interaction in dangerous situations. The system is evaluated on 10 human subjects in a driving simulator. The assistance system had no false activation during the tests. It avoided collisions in all the rear-end crash avoidance scenarios, while 90% of human subjects did not.

Keywords

Multi-objective driver model driving motivations human-machine interaction rear-end crash avoidance system collision avoidance

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References

Michon

. A critical view of driver behavior models: what do we know, what should we do? In: Evans

Schwing

(eds) Human behavior and traffic safety. Boston: Springer, 1985, pp.485–520.

Wilde

. Target risk: dealing with the danger of death, disease and damage in everyday decisions. Toronto, ON, Canada: PDE Publication, 1994.

Fuller

. Towards a general theory of driver behaviour. Acc Anal Prevent 2005; 37(3): 461–472.

Gibson

Crooks

. A theoretical field-analysis of automobile-driving. Am J Psychol 1938; 51(3): 453–471.

Salvucci

. Modeling driver behavior in a cognitive architecture. Hum Factors 2006; 48(2): 362–380.

Hollnagel

. Time and time again. Theor Issue Ergonomic Sci 2002; 3(2): 143–158.

Fangxun

Rongjun

Hongxia

, et al. A new car-following model with consideration of the velocity difference between the current speed and the historical speed of the leading car. Phys A: Stat Mech Appl 2016; 464(1): 267–277.

Wahid

Zamzuri

Rahman

MAA

, et al. Study on potential field based motion planning and control for automated vehicle collision avoidance systems. In: IEEE international conference on mechatronics (ICM), Churchill, VIC, Australia, 13–15 February 2017, pp.208–213. New York: IEEE.

Erlien

Fujita

Gerdes

. Shared steering control using safe envelopes for obstacle avoidance and vehicle stability. IEEE Trans Intell Transp Syst 2016; 17(2): 441–451.

10.

Guo

Luo

. Adaptive coordinated collision avoidance control of autonomous ground vehicles. Proc IMechE, Part I: J Systems and Control Engineering 2018; 232(9): 1120–1133.

11.

Cao

, et al. A new vehicle path-following strategy of the steering driver model using general predictive control method. J Mech Eng Sci Part C 2016; 232(24): 4578–4587.

12.

Nagarajan

. Design of self-learning fuzzy based driver model. SAE technical paper 2017-26-0081, 2017.

13.

Norouzi

Kazemi

Azadi

. Vehicle lateral control in the presence of uncertainty for lane change maneuver using adaptive sliding mode control with fuzzy boundary layer. Proc IMechE, Part I: J Systems and Control Engineering 2018; 232(1): 12–28.

14.

Chonga

Montasir

Flintschc

, et al. A rule-based neural network approach to model driver naturalistic behavior in traffic. Transp Res Part C 2013; 32(1): 207–223.

15.

Gordon

Gao

. A flexible hierarchical control method for optimal collision avoidance. In: Proceedings of the 16th international conference on mechatronics—mechatronika (ME), Brno, 3–5 December 2014, pp.318–324. New York: IEEE.

16.

Hayashi

Isogai

Raksinchar

, et al. Autonomous collision avoidance system by combined control of steering and braking using geometrically optimized vehicular trajectory. Veh Syst Dyn 2012; 50(1): 151–168.

17.

Wnag

Zhao

, et al. Path planning and stability control of collision avoidance system based on active front steering. Sci China Technol Sci 2017; 60(8): 1231–1243.

18.

González

Pérez

Milanés

, et al. A review of motion planning techniques for automated vehicles. IEEE Trans Intell Transp Syst 2015; 17(4): 1135–1145.

19.

Hassan

Zamzuri

Ariff

. Modelling of driver’s steering behaviour control in emergency collision avoidance by using focused time delay neural network. In: International conference on information and communications technology (ICOIACT), Yogyakarta, Indonesia, 6–7 March 2018, pp.730–734. New York: IEEE.

20.

Schnelle

Wang

, et al. A Personalizable driver steering model capable of predicting driver behaviors in vehicle collision avoidance maneuvers. IEEE Trans Human-Mach Syst 2017; 47(5): 625–635.

21.

Horst

. Time-related measures for modelling risk in driver behaviour. In: Cacciabue

(ed.) Modelling driver behaviour in automotive environments. London: Springer, 2007, pp.235–252.

22.

Zhang

Kaber

. An empirical assessment of driver motivation and emotional states in perceived safety margins under varied driving conditions. Ergonomics 2013; 56(2): 256–267.

23.

Veres

Molnar

Lincoln

, et al. Autonomous vehicle control systems—a review of decision making. Proc IMechE, Part I: J Systems and Control Engineering 2010; 225(2): 155–195.

24.

Mozaffari

Nahvi

. A motivational driver steering model: task difficulty homeostasis from control theory perspective. Cog Syst Res 2018; 50: 67–82.

25.

Rajamani

. Vehicle dynamics and control. 2nd ed. New York: Springer, 2012.

26.

Shu

H-b

Shao

Y-m

Lin

, et al. Computational-based dynamic driving simulation for evaluation of mountain roads with complex shapes: a case study. Proc Eng 2016; 137: 210–219.

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