Contents In recent years, research institutes, automotive manufacturers, and IT companies have made a great effort to develop autonomous driving technology, which has great potential to improve the safety and efficiency of transportation systems [1]. Although fully autonomous driving is the goal, many technical problems remain to be solved, and highly automated vehicles are expected to play a significant role in the interim [2]. Highly automated driving, which retains a human driver in the control loop, presents an exciting new development in vehicle technology [3], [4]. The approach allows a human driver and an automation system to share control authority and cooperatively operate a vehicle [5], [6]. This poses a new challenge, namely, how to ensure safe and smooth control allocations and transitions between humans and automation systems.
Abstract:
In this article, a human–machine adaptive shared control method is proposed for automated vehicles (AVs) under automation performance degradation. First, a novel risk ass...Show MoreMetadata
Abstract:
In this article, a human–machine adaptive shared control method is proposed for automated vehicles (AVs) under automation performance degradation. First, a novel risk assessment module is proposed to monitor driving behavior and evaluate automation performance degradation for AVs. Then, an adaptive control authority allocation module is developed. In the event of any performance degradation, the control authority allocated to the automation system is decreased based on the assessed risk. Consequently, the control authority allocated to a human driver is adaptively increased and thus requires more driver engagement in the control loop to compensate for the automation degradation and ensure the vehicle’s safety. Experimental validation is conducted under different driving scenarios. The test results show that the approach can effectively compensate for vehicle automation performance degradation through human–machine adaptive shared control, ensuring the safety of automated driving.
Published in: IEEE Intelligent Transportation Systems Magazine ( Volume: 14, Issue: 2, March-April 2022)
Funding Agency:
References is not available for this document.
Select All
1.
P. Hang, C. Lv, Y. Xing, C. Huang and Z. Hu, "Human-like decision making for autonomous driving: A noncooperative game theoretic approach", IEEE Trans. Intell. Transp. Syst., Nov. 2020.
2.
J.-M. Hoc, "Towards a cognitive approach to human–machine cooperation in dynamic situations", Int. J. Human-Comput. Stud., vol. 54, no. 4, pp. 509-540, 2001.
3.
C. Lv et al., "Analysis of autopilot disengagements occurring during autonomous vehicle testing", IEEE/CAA J. Automatica Sinica, vol. 5, no. 1, pp. 58-68, 2017.
4.
W. Wang et al., "Decision-making in driver-automation shared control: A review and perspectives", IEEE/CAA J. Automatica Sinica, vol. 7, no. 5, pp. 1289-1307, 2020.
5.
C. Huang, C. Lv, F. Naghdy and H. Du, "Reference-free approach for mitigating human–machine conflicts in shared control of automated vehicles", IET Control Theory Appl., vol. 14, no. 18, pp. 2752-2763, 2020.
6.
C. Huang, F. Naghdy, H. Du and H. Huang, "Shared control of highly automated vehicles using steer-by-wire systems", IEEE/CAA J. Automatica Sinica, vol. 6, no. 2, pp. 410-423, 2019.
7.
F. Flemisch, M. Heesen, T. Hesse, J. Kelsch, A. Schieben and J. Beller, "Towards a dynamic balance between humans and automation: authority ability responsibility and control in shared and cooperative control situations", Cognit. Technol. Work, vol. 14, no. 1, pp. 3-18, 2012.
8.
M. A. Benloucif, A.-T. Nguyen, C. Sentouh and J.-C. Popieul, "A new scheme for haptic shared lateral control in highway driving using trajectory planning", IFAC-PapersOnLine, vol. 50, no. 1, pp. 13,834-13,840, 2017.
9.
A.-T. Nguyen, C. Sentouh and J.-C. Popieul, "Driver-automation cooperative approach for shared steering control under multiple system constraints: Design and experiments", IEEE Trans. Ind. Electron., vol. 64, no. 5, pp. 3819-3830, 2016.
10.
J. P. Vasconez, D. Carvajal and F. A. Cheein, "On the design of a human–robot interaction strategy for commercial vehicle driving based on human cognitive parameters", Adv. Mech. Eng., vol. 11, no. 7, pp. 1,687,814,019,862,715, 2019.
11.
C. Lv et al., "Human–machine collaboration for automated driving using an intelligent two-phase haptic interface", Adv. Intell. Syst., Feb. 2021.
12.
M. S. Erden and T. Tomiyama, "Human-intent detection and physically interactive control of a robot without force sensors", IEEE Trans. Robot., vol. 26, no. 2, pp. 370-382, 2010.
13.
Z. Hu, Y. Xing, C. Lv, P. Hang and J. Liu, "Deep convolutional neural network-based Bernoulli heatmap for head pose estimation", Neurocomputing, vol. 436, pp. 198-209, May 2021, [online] Available: https://www.sciencedirect.com/science/article/pii/S0925231221000692.
14.
C. Huang, F. Naghdy and H. Du, "Delta operator-based model predictive control with fault compensation for steer-by-wire systems", IEEE Trans. Syst. Man Cybern. Syst., vol. 50, no. 6, pp. 2257-2272, 2020.
15.
C. Huang, F. Naghdy and H. Du, "Fault tolerant sliding mode predictive control for uncertain steer-by-wire system", IEEE Trans. Cybern., vol. 49, no. 1, pp. 261-272, 2019.
16.
F. Favarò, S. Eurich and N. Nader, "Autonomous vehicles’ disengagements: Trends triggers and regulatory limitations", Accident Anal. Prevention, vol. 110, pp. 136-148, Jan. 2018.
17.
V. V. Dixit, S. Chand and D. J. Nair, "Autonomous vehicles: Disengagements accidents and reaction times", PLoS One, vol. 11, no. 12, pp. e0168054, 2016.
18.
H. Waschl, I. Kolmanovsky and F. Willems, Control Strategies for Advanced Driver Assistance Systems and Autonomous Driving Functions, Switzerland:Springer International, 2019.
19.
Z. Hu, C. Lv, P. Hang, C. Huang and Y. Xing, "Data-driven estimation of driver attention using calibration-free eye gaze and scene features", IEEE Trans. Ind. Electron., Feb. 2021.
20.
S. Lefèvre, D. Vasquez and C. Laugier, "A survey on motion prediction and risk assessment for intelligent vehicles", ROBOMECH J., vol. 1, no. 1, pp. 1-14, 2014.
21.
E. E. Nodine et al., Indicators of driver adaptation to forward collision warnings: A naturalistic driving evaluation, 2019.
22.
J. Wang, J. Wu, X. Zheng, D. Ni and K. Li, "Driving safety field theory modeling and its application in pre-collision warning system", Transp. Res. C Emerg. Technol., vol. 72, pp. 306-324, Nov. 2016.
23.
H. Wang, Y. Huang, A. Khajepour, Y. Zhang, Y. Rasekhipour and D. Cao, "Crash mitigation in motion planning for autonomous vehicles", IEEE Trans. Intell. Transp. Syst., vol. 20, no. 9, pp. 3313-3323, 2019.
24.
R. Schubert, C. Adam, M. Obst, N. Mattern, V. Leonhardt and G. Wanielik, "Empirical evaluation of vehicular models for ego motion estimation", Proc. 2011 IEEE Intell. Veh. Symp. (IV), pp. 534-539.
25.
A. Benloucif, A.-T. Nguyen, C. Sentouh and J.-C. Popieul, "Cooperative trajectory planning for haptic shared control between driver and automation in highway driving", IEEE Trans. Ind. Electron., vol. 66, no. 12, pp. 9846-9857, 2019.
26.
X. Fan, Y. Guo, H. Liu, B. Wei and W. Lyu, "Improved artificial potential field method applied for AUV path planning", Math. Problems Eng., vol. 2020, 2020.
27.
M. Tavana, F. Azizi, F. Azizi and M. Behzadian, "A fuzzy inference system with application to player selection and team formation in multi-player sports", Sport Manage. Rev., vol. 16, no. 1, pp. 97-110, 2013.
28.
X. Ji, K. Yang, X. Na, C. Lv and Y. Liu, "Shared steering torque control for lane change assistance: A stochastic game-theoretic approach", IEEE Trans. Ind. Electron., vol. 66, no. 4, pp. 3093-3105, 2018.
29.
X. Na and D. J. Cole, "Modelling of a human driver s interaction with vehicle automated steering using cooperative game theory", IEEE/CAA J. Automatica Sinica, vol. 6, no. 5, pp. 1095-1107, 2019.
