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

• AcceptedMay 29, 2020
• PublishedMay 18, 2021
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### 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).

### References

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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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