SCIENCE CHINA Information Sciences, Volume 64 , Issue 7 : 172203(2021) https://doi.org/10.1007/s11432-020-2961-8

Event-triggered shared lateral control for safe-maneuver of intelligent vehicles

More info
  • ReceivedJan 2, 2020
  • AcceptedMay 29, 2020
  • PublishedMay 18, 2021



The work was supported by National Natural Science Foundation of China (Grant Nos. 61751311, 61825305) and National Key R$\&$D Program of China (Grant No. 2018YFB1305105).


[1] Hu Y, Qu T, Liu J, et al. Human-machine Cooperative Control of Intelligent Vehicle: Recent Developments and Future Perspectives. Acta Automa Sin, 2019, 45: 1261--1280. Google Scholar

[2] 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. Human Factors, 2015, 57: 5--20. Google Scholar

[3] Hamid U Z A, Saito Y, Zamzuri H. A review on threat assessment, path planning and path tracking strategies for collision avoidance systems of autonomous vehicles. IJVAS, 2018, 14: 134-169 CrossRef Google Scholar

[4] Helmbrecht M, Olaverri-Monreal C, Bengler K. How Electric Vehicles Affect Driving Behavioral Patterns. IEEE Intell Transp Syst Mag, 2014, 6: 22-32 CrossRef Google Scholar

[5] Saito T, Wada T, Sonoda K. Control Authority Transfer Method for Automated-to-Manual Driving Via a Shared Authority Mode. IEEE Trans Intell Veh, 2018, 3: 198-207 CrossRef Google Scholar

[6] Sentouh C, Debernard S, Popieul J C, et al. Toward a shared lateral control between driver and steering assist controller. IFAC Proc Vol, 2010, 43: 404--409. Google Scholar

[7] Ludwig J, Martin M, Horne M. Driver observation and shared vehicle control: supporting the driver on the way back into the control loop. at - Automatisierungstechnik, 2018, 66: 146-159 CrossRef Google Scholar

[8] Huang C, Naghdy F, Du H. Shared control of highly automated vehicles using steer-by-wire systems. IEEE/CAA J Autom Sin, 2019, 6: 410-423 CrossRef Google Scholar

[9] de Winter J C F, Happee R, Martens M H. Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence. Transpation Res Part F-Traffic Psychology Behaviour, 2014, 27: 196-217 CrossRef Google Scholar

[10] Borroni F, Tanelli M. A weighting approach to the shared-control of lateral vehicle dynamics. IFAC-PapersOnLine, 2018, 51: 305-310 CrossRef Google Scholar

[11] Nguyen A T, Sentouh C, Popieul J C. Sensor Reduction for Driver-Automation Shared Steering Control via an Adaptive Authority Allocation Strategy. IEEE/ASME Trans Mechatron, 2018, 23: 5-16 CrossRef Google Scholar

[12] Zafeiropoulos S, Tsiotras P. Design of a lane-tracking driver steering assist system and its interaction with a two-point visual driver model. In: Proceedings of the 2014 American Control Conference, Portland, 2014. 3911--3917. Google Scholar

[13] Merah A, Hartani K, Draou A. A new shared control for lane keeping and road departure prevention. Vehicle Syst Dyn, 2016, 54: 86-101 CrossRef ADS Google Scholar

[14] Jiang J, Astolfi A. State and Output-Feedback Shared-Control for a Class of Linear Constrained Systems. IEEE Trans Automat Contr, 2016, 61: 3209-3214 CrossRef Google Scholar

[15] Jiang J, Astolfi A. Shared-Control for a Rear-Wheel Drive Car: Dynamic Environments and Disturbance Rejection. IEEE Trans Human-Mach Syst, 2017, 47: 723-734 CrossRef Google Scholar

[16] Jiang J, Astolfi A. Shared-Control for the Lateral Motion of Vehicles. In: Proceedings of the 2018 European Control Conference (ECC), Limassol, 2018. 225--230. Google Scholar

[17] 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

[18] Huang M, Gao W, Wang Y. Data-Driven Shared Steering Control of Semi-Autonomous Vehicles. IEEE Trans Human-Mach Syst, 2019, 49: 350-361 CrossRef Google Scholar

[19] Park K, Han S H, Lee H. A Study on Shared Steering Control in Driving Experience Perspective: How Strong and How Soon Should Intervention Be? 2018,. arXiv Google Scholar

[20] 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 the 2007 IEEE Intelligent Vehicles Symposium, Istanbul, 2007. 582--587. Google Scholar

[21] Li R J, Li S B, Gao H B, et al. Effects of Human Adaptation and Trust on Shared Control for Driver-Automation Cooperative Driving. SAE Technical Paper, No. 2017-01-1987. 2017. Google Scholar

[22] Anderson S J, Peters S C, Pilutti T E. An optimal-control-based framework for trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles in hazard avoidance scenarios. IJVAS, 2010, 8: 190-216 CrossRef Google Scholar

[23] Gray A, Ali M, Gao Y Q, et al. Semi-autonomous vehicle control for road departure and obstacle avoidance. In: Proceedings of the 2012 IFAC Symposium on Control in Transportation Systems, Sofla, 2012. 1--6. Google Scholar

[24] Wang Y, Zheng H, Zong C. Path-following control of autonomous ground vehicles using triple-step model predictive control. Sci China Inf Sci, 2020, 63: 209203 CrossRef Google Scholar

[25] Wu Y, Ren G P, Zhang H T. Dual-mode predictive control of a rotor suspension system. Sci China Inf Sci, 2020, 63: 112204 CrossRef Google Scholar

[26] Li Y, Tee K P, Yan R, et al. Shared control of human and robot by approximate dynamic programming. In: Proceedings of the 2015 American Control Conference (ACC), Chicago, 2015. 1167--1172. Google Scholar

[27] Lee D N. A Theory of Visual Control of Braking Based on Information about Time-to-Collision. Perception, 1976, 5: 437-459 CrossRef Google Scholar

[28] Zhang Y, Antonsson E K, Grote K. A new threat assessment measure for collision avoidance systems. In: Proceedings of the 2016 IEEE Intelligent Transportation Systems Conference, Toronto, 2006. 968--975. Google Scholar

[29] Brannstrom M, Coelingh E, Sjoberg J. Model-Based Threat Assessment for Avoiding Arbitrary Vehicle Collisions. IEEE Trans Intell Transp Syst, 2010, 11: 658-669 CrossRef Google Scholar

[30] Althoff M, Mergel A. Comparison of Markov Chain Abstraction and Monte Carlo Simulation for the Safety Assessment of Autonomous Cars. IEEE Trans Intell Transp Syst, 2011, 12: 1237-1247 CrossRef Google Scholar

[31] Anderson S J, Karumanchi S B, Iagnemma K. Constraint-based planning and control for safe, semi-autonomous operation of vehicles. In: Proceedings of the 2012 IEEE intelligent vehicles symposium, Alcala de Henares, 2012. 383--388. Google Scholar

[32] Wada N, Matsumoto T. Driver assistance for collision avoidance by constrained MPC. In: Proceedings of the 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Kanazawa, 2017. 90--93. Google Scholar

[33] Kitagawa L, Kobayashi T, Beppu T, et al. Semi-autonomous obstacle avoidance of omnidirectional wheelchair by joystick impedance control. In: Proceedings of the 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, Maui, 2001. 2148--2153. Google Scholar

[34] Itoh M, Tanaka H, Inagaki T. Toward Trustworthy Haptic Assistance System for Emergency Avoidance of Collision with Pedestrian. J Human-Robot Interaction, 2015, 4: 4 CrossRef Google Scholar

[35] Balachandran A, Brown M, Erlien S M. Predictive Haptic Feedback for Obstacle Avoidance Based on Model Predictive Control. IEEE Trans Automat Sci Eng, 2016, 13: 26-31 CrossRef Google Scholar

[36] Rajamani R. Vehicle Dynamics and Control. Berlin: Springer Science & Business Media, 2011. Google Scholar

[37] Gong J, Jiang Y, Xu W. Model Predictive Control for Self-driving Vehicles. Beijing: Beijing Institute of Technology Press, 2014. Google Scholar

[38] Martinez J, Black M J, Romero J. On human motion prediction using recurrent neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, 2017. 2891--2900. Google Scholar

[39] Lefèvre S, Vasquez D, Laugier C. A survey on motion prediction and risk assessment for intelligent vehicles. ROBOMECH journal, 2014, 1(1). Google Scholar

[40] Kim B D, Kang C M, Kim J, et al. Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network. In: Proceedings of 2017 IEEE 20th International Conference on Intelligent Transportation Systems, Yokohama, 2017. 399--404. Google Scholar

[41] Agamennoni G, Nieto J I, Nebot E M. Estimation of Multivehicle Dynamics by Considering Contextual Information. IEEE Trans Robot, 2012, 28: 855-870 CrossRef Google Scholar

[42] MacKay D J C. Bayesian Interpolation. Neural Computation, 1992, 4: 415-447 CrossRef Google Scholar

[43] Ticknor J L. A Bayesian regularized artificial neural network for stock market forecasting. Expert Syst Appl, 2013, 40: 5501-5506 CrossRef Google Scholar

[44] Sun Z, Chen Y, Li X. A Bayesian regularized artificial neural network for adaptive optics forecasting. Optics Commun, 2017, 382: 519-527 CrossRef ADS Google Scholar

[45] Foresee F D, Hagan M T. Gauss-Newton approximation to Bayesian learning. In: Proceedings of International Conference on Neural Networks (ICNN'97), Houston, 1997. 1930--1935. Google Scholar

[46] Ercan Z, Carvalho A, Tseng H E. A predictive control framework for torque-based steering assistance to improve safety in highway driving. Vehicle Syst Dyn, 2018, 56: 810-831 CrossRef ADS Google Scholar

[47] Chen H, Allg?wer F. A Quasi-Infinite Horizon Nonlinear Model Predictive Control Scheme with Guaranteed Stability??This paper was not presented at any IFAC meeting. This paper was accepted for publication in revised form by Associate Editor W. Bequette under the direction of Editor Prof. S. Skogestad.. Automatica, 1998, 34: 1205-1217 CrossRef Google Scholar

[48] Chmielewski D, Manousiouthakis V. On constrained infinite-time linear quadratic optimal control. Syst Control Lett, 1996, 29: 121-129 CrossRef Google Scholar

  • Figure 1

    (Color online) Diagram of the shared control framework.

  • Figure 2

    (Color online) The simplified “bicycle" model: c.g. is the center of gravity of the vehicle, $x$ and $y$ are the longitudinal and lateral positions of the vehicle in the body-fixed coordinates, respectively, $X$ and $Y$ are positions in the global coordinates, respectively, $\varphi$ is the vehicle yaw angle, $\delta_f$ is the steering angle for the front wheel, $l_f$ and $l_r$ are the distance from the front and rear axles to c.g., respectively.

  • Figure 3

    (Color online) The predicted trajectories of the host vehicle in a lane change process based on three different methods. (a) The constant velocity model; (b) the constant acceleration model; (c) the proposed method. The solid black and dash-dotted black lines represent the curbs and center-lines of roads, respectively. The solid black and blue rectangles represent the host and forward vehicles at a generic discrete-time sampling instant $k_0$, respectively. The dashed red, yellow, green, cyan and magenta lines represent the predicted trajectories of the host vehicle from $k_0$ to $k_0+3,k_0+6,k_0+9,k_0+12$, respectively.

  • Figure 12

    (Color online) The rear-end collision avoidance with the proposed controller and the LQR-based one. The dashed black and dashed blue rectangles depict the real states of the host and forward vehicles at the predicted collision time. $t=6.6$ s is the triggering time instant, $t=8.2$ s and $t=9.2$ s correspond to the time instants when $\alpha\geq~\bar~\alpha$.

  • Table 1  

    Table 1The value of the parameters

    Parameter Value Unit Parameter Value Unit
    $m$ 1820 kg $Q_{\eta}$ 200
    $l_f$ 1.170 m $Q_{\Theta}$ 100
    $l_r$ 1.770 m $R$ 10000
    $C_{cf}$72653 N/rad $S_{\rm~obs}$ 10
    $C_{cr}$121449 N/rad $M$ 45
    $I_z$ 3746 kg$\cdot$m$^2$ $\bar\alpha$ 0.8
    $\Theta_{\min}$ $-450$ $^{\circ}$ $r$ 5
    $\Theta_{\max}$ 450 $^{\circ}$ $\bar\varepsilon_1$ 50
    $\gamma$ 20 $\bar\varepsilon_2$ 10
    $n$ 10 $N$ 3147
    $p$ 100 $\kappa$ 0.005
    $l$ 15

    Algorithm 1 Implementation steps of the event-triggered shared lateral control

    Require:set tuning parameters $Q_{\eta}$, $Q_{\Theta}$, $R$, $S_{\rm~obs}$, $\bar\varepsilon_1$, $\bar\varepsilon_2$, $N_p$, $N_c$, $\bar{\alpha}$, $r$;

    for $k=1,\ldots$

    Generate $\eta_r(k+1),\ldots,\eta_r(k+N_p)$ using BRANN algorithm;

    Compute the risk level $w$ with 11;

    if $w<~w_0$ then

    Remain in human driving mode;


    Obtain $\Theta_d(k+1),\ldots,\Theta_d(k+N_p-1)$ with 13;

    Compute the shared control action $u(k|k)$ with the shared controller 15;

    Apply $u(k|k)$ to the vehicle;

    if $w<~w_0$ then

    Compute $u(k|k)$ with the shared controller 15 using cost 16;

    Apply $u(k|k)$ to the vehicle;

    if $\alpha(k-i)~\geq~\bar{\alpha}$, $i=1,\ldots,r$ then

    Break and switch back to human driving mode;

    end if

    end if

    end if

    end for


Contact and support