SCIENCE CHINA Information Sciences, Volume 63 , Issue 9 : 190201(2020) https://doi.org/10.1007/s11432-019-2987-x

Driver-automation shared steering control for highly automated vehicles

More info
  • ReceivedOct 17, 2019
  • AcceptedJul 9, 2020
  • PublishedAug 12, 2020



This work was supported by National Natural Science Foundation of China (Grant Nos. U19A2069, 61790563, U1664263), Project of the Education Department of Jilin Province (Grant No. JJKH20190165KJ), and Project of Development and Reform Commission of Jilin Province (Grant No. 2019C036-5).


[1] Veres S M, Molnar L, Lincoln N K. Autonomous vehicle control systems - a review of decision making. Proc Institution Mech Engineers Part I-J Syst Control Eng, 2011, 225: 155-195 CrossRef Google Scholar

[2] Dixit S, Fallah S, Montanaro U. Trajectory planning and tracking for autonomous overtaking: State-of-the-art and future prospects. Annu Rev Control, 2018, 45: 76-86 CrossRef Google Scholar

[3] Hu C, Wang Z, Taghavifar H. MME-EKF-Based Path-Tracking Control of Autonomous Vehicles Considering Input Saturation. IEEE Trans Veh Technol, 2019, 68: 5246-5259 CrossRef Google Scholar

[4] Hu C, Chen Y, Wang J. Fuzzy observer-based transitional path-tracking control for autonomous vehicles. IEEE transactions on intelligent transportation systems, 2020. Google Scholar

[5] Van Brummelen J, O'Brien M, Gruyer D. Autonomous vehicle perception: The technology of today and tomorrow. Transpation Res Part C-Emerging Technologies, 2018, 89: 384-406 CrossRef Google Scholar

[6] Schoettle B, Sivak M. A Preliminary Analysis of Real-world Crashes Involving Self-driving Vehicles. The University of Michigan Transportation Research Institute Report UMTRI-2015-34. 2015. Google Scholar

[7] Schoettle B, Sivak M. Public Opinion about Self-driving Vehicles in China, India, Japan, the US, the UK, and Australia. The University of Michigan Transportation Research Institute Report UMTRI-2014-30. 2014. Google Scholar

[8] Mars F, Chevrel P. Modelling human control of steering for the design of advanced driver assistance systems. Annu Rev Control, 2017, 44: 292-302 CrossRef Google Scholar

[9] Mars F, Deroo M, Hoc J M. Analysis of Human-Machine Cooperation When Driving with Different Degrees of Haptic Shared Control. IEEE Trans Haptics, 2014, 7: 324-333 CrossRef Google Scholar

[10] Kim J H, Song J B. Control logic for an electric power steering system using assist motor. Mechatronics, 2002, 12: 447-459 CrossRef Google Scholar

[11] van der Wiel D W J, van Paassen M M, Mulder M, et al. Driver adaptation to driving speed and road width: exploring parameters for designing adaptive haptic shared control. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, Kowloon, 2015. 3060--3065. Google Scholar

[12] Abbink D A, Mulder M, Van der Helm F C T. Measuring Neuromuscular Control Dynamics During Car Following With Continuous Haptic Feedback. IEEE Trans Syst Man Cybern B, 2011, 41: 1239-1249 CrossRef Google Scholar

[13] Abbink D A, Cleij D, Mulder M, et al. The importance of including knowledge of neuromuscular behaviour in haptic shared control. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), Seoul, 2012. 3350--3355. Google Scholar

[14] Iwano K, Raksincharoensak P, Nagai M. A Study on Shared Control between the Driver and an Active Steering Control System in Emergency Obstacle Avoidance Situations. IFAC Proc Volumes, 2014, 47: 6338-6343 CrossRef Google Scholar

[15] Boink R, van Paassen M M, Mulder M, et al. Understanding and reducing conflicts between driver and haptic shared control. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC), San Diego, 2014. 1510--1515. Google Scholar

[16] Do M T, Man Z, Zhang C. Robust Sliding Mode-Based Learning Control for Steer-by-Wire Systems in Modern Vehicles. IEEE Trans Veh Technol, 2014, 63: 580-590 CrossRef Google Scholar

[17] Brandt T, Sattel T, Bohm M. Combining haptic human-machine interaction with predictive path planning for lane-keeping and collision avoidance systems. In: Proceedings of IEEE Intelligent Vehicles Symposium, Istanbul, 2007. 582--587. Google Scholar

[18] Mulder M, Abbink D A, Boer E R. Sharing Control With Haptics. Hum Factors, 2012, 54: 786-798 CrossRef Google Scholar

[19] Switkes J P, Rossetter E J, Coe I A. Handwheel Force Feedback for Lanekeeping Assistance: Combined Dynamics and Stability. J Dynamic Syst Measurement Control, 2006, 128: 532-542 CrossRef Google Scholar

[20] Petermeijer S M, Abbink D A, de Winter J C F. Should Drivers Be Operating Within an Automation-Free Bandwidth? Evaluating Haptic Steering Support Systems With Different Levels of Authority. Hum Factors, 2015, 57: 5-20 CrossRef Google Scholar

[21] Na X, Cole D J. Game-Theoretic Modeling of the Steering Interaction Between a Human Driver and a Vehicle Collision Avoidance Controller. IEEE Trans Human-Mach Syst, 2015, 45: 25-38 CrossRef Google Scholar

[22] Well K. Aircraft control laws for envelope protection. In: Proceedings of AIAA Guidance, Navigation, and Control Conference and Exhibit, Keystone, 2006. 258--267. Google Scholar

[23] Erlien S M, Funke J, Gerdes J C. Incorporating non-linear tire dynamics into a convex approach to shared steering control. In: Proceedings of American Control Conference, Portland, 2014. 3468--3473. Google Scholar

[24] Erlien S M, Fujita S, Gerdes J C. Shared Steering Control Using Safe Envelopes for Obstacle Avoidance and Vehicle Stability. IEEE Trans Intell Transp Syst, 2016, 17: 441-451 CrossRef Google Scholar

[25] Salvucci D D, Gray R. A Two-Point Visual Control Model of Steering. Perception, 2004, 33: 1233-1248 CrossRef Google Scholar

[26] Nguyen A T, Sentouh C, Popieul J C. Driver-Automation Cooperative Approach for Shared Steering Control Under Multiple System Constraints: Design and Experiments. IEEE Trans Ind Electron, 2017, 64: 3819-3830 CrossRef Google Scholar

[27] Li R J, Li Y N, Li S B, et al. Driver-automation indirect shared control of highly automated vehicles with intention-aware authority transition. In: Proceedings of IEEE Intelligent Vehicles Symposium (IV), Redondo Beach, 2017. 26--32. Google Scholar

[28] Pl?chl M, Edelmann J. Driver models in automobile dynamics application. Vehicle Syst Dyn, 2007, 45: 699-741 CrossRef Google Scholar

[29] Biondi F, Alvarez I, Jeong K A. Human-Vehicle Cooperation in Automated Driving: A Multidisciplinary Review and Appraisal. Int J Human-Comput Interaction, 2019, 35: 932-946 CrossRef Google Scholar

[30] Johns M, Mok B, Sirkin D, et al. Exploring shared control in automated driving. In: Proceedings of the 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Christchurch, 2016. 91--98. Google Scholar

[31] Nishimura R, Wada T, Sugiyama S. Haptic Shared Control in Steering Operation Based on Cooperative Status Between a Driver and a Driver Assistance System. J Human-Robot Interaction, 2015, 4: 19-37 CrossRef Google Scholar

[32] Tan D, Chen W, Wang H. Shared control for lane departure prevention based on the safe envelope of steering wheel angle. Control Eng Practice, 2017, 64: 15-26 CrossRef Google Scholar

[33] Saleh L, Chevrel P, Claveau F. Shared Steering Control Between a Driver and an Automation: Stability in the Presence of Driver Behavior Uncertainty. IEEE Trans Intell Transp Syst, 2013, 14: 974-983 CrossRef Google Scholar

[34] Wang W, Xi J, Liu C. Human-Centered Feed-Forward Control of a Vehicle Steering System Based on a Driver's Path-Following Characteristics. IEEE Trans Intell Transp Syst, 2016, : 1-14 CrossRef Google Scholar

[35] MacAdam C C. An Optimal Preview Control for Linear Systems. J Dynamic Syst Measurement Control, 1980, 102: 188-190 CrossRef Google Scholar

[36] Haiping Du , Nong Zhang , Guangming Dong . Stabilizing Vehicle Lateral Dynamics With Considerations of Parameter Uncertainties and Control Saturation Through Robust Yaw Control. IEEE Trans Veh Technol, 2010, 59: 2593-2597 CrossRef Google Scholar

[37] Shen C, Shi Y, Buckham B. Integrated Path Planning and Tracking Control of an AUV: A Unified Receding Horizon Optimization Approach. IEEE/ASME Trans Mechatron, 2017, 22: 1163-1173 CrossRef Google Scholar

[38] Zhou L, Jia L, Wang Y L. A robust integrated model predictive iterative learning control strategy for batch processes. Sci China Inf Sci, 2019, 62: 219202 CrossRef Google Scholar

[39] Guo L, Chen H, Gao B. Energy management of HEVs based on velocity profile optimization. Sci China Inf Sci, 2019, 62: 89203 CrossRef Google Scholar

[40] Felipe E, Navin F. Automobiles on Horizontal Curves: Experiments and Observations. Transpation Res Record, 1998, 1628: 50-56 CrossRef Google Scholar

[41] Bella F. Driver perception of roadside configurations on two-lane rural roads: Effects on speed and lateral placement. Accident Anal Prevention, 2013, 50: 251-262 CrossRef Google Scholar

  • Figure 1

    A 2-DOF vehicle model.

  • Figure 2

    (Color online) Schematic diagram of the intelligent vehicle shared steering control.

  • Figure 3

    (Color online) Control block diagram of the shared steering vehicle.

  • Figure 6

    Simulation results of low-risk lane change. (a) Vehicle trajectory; (b) front wheel angle; (c) sideslip angle;protect łinebreak (d) yaw rate.

  • Figure 7

    Comparison of low-risk lane change test results.

  • Figure 10

    Comparison of high-risk scenario test results.

  • Table 1  

    Table 1Driver types and characteristics

    Driver type Driver characteristics
    D1 skillful, careful, smooth with preview
    D2 skillful, racy, direct with preview
    D3 without preview
    D4 untrained, racy, direct with preview
  • Table 2  

    Table 2Performance of three schemes in low-risk scenarios

    Average intervention rate (%) 0.4482 0.4531 1.3453
  • Table 3  

    Table 3High-risk virtual tests

    No. Test scenario Driver type Velocity (km/h) Friction coefficient
    1 slalom D1 80 0.75
    2 double-lane change D1 100 0.55
    3 obstacle avoidance D1 50 0.55
    4 slalom D2 85 0.75
    5 double-lane change D4 85 0.55
    6 double-lane change D3 75 0.55
  • Table 4  

    Table 4High-risk condition performance

    Average hazard rate (%) 16.7056 7.1562 4.3529