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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

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

Abstract


Acknowledgment

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).


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  • 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;

    else

    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

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