基于预测风险场的智能汽车主动避撞运动规划

doi:10.19562/j.chinasae.qcgc.2021.07.017

Abstract

Abstract:

Aiming at the side and rear collision avoidance problem for autonomous vehicle， a predictive risk field fusing obstacle motion prediction and a motion planning method based on predictive risk field are proposed in this paper. In the Frenet coordinate system， the kinematics model is used to predict the information of each obstacle vehicle in the future scene， and the predictive risk field is established based on the three dimensions of longitudinal， lateral and time. Considering vehicle dynamics and velocity， acceleration and curvature constraints， the dynamic programming method is adopted to complete the behavior decision， and the polynomial curve and quadratic programming method are used to optimize the decision trajectory. The results show that the predictive risk field can accurately identify the changing trend of the potential risks of the surrounding obstacle vehicles over time， and plan the collision avoidance trajectory meeting various constraints， ensuring the safety and stability of vehicle operation.

Key words: intelligent vehicle, active collision avoidance, predictive risk field, motion planning, behavior decision

Anjie Wang,Ling Zheng,Yinong Li,Kan Wang. Motion Planning for Active Collision Avoidance of Intelligent Vehicles Based on Predictive Risk Field[J].Automotive Engineering, 2021, 43(7): 1096-1104.

Figures/Tables 18

算法1 变步长搜索算法求投影坐标
输入：道路参考线，起始A/B点和车辆P点的Cartesian坐标
输出：车辆位置P点的Frenet坐标 $(s P, d P, θ F P)$
1：	function $[s P, d P, θ F P]$ =Cartesian2Frenet（A，B，P）
2：	$s P ← 0, ? d P ← 0 ?$
3：	while $s i g n (A P · A B) ≠ s i g n (B P · A B)$ do
4：	if $s i g n (A P · A B) = ? 0 ? o r ? s i g n (B P · A B) ? = 0$ then
5：	$s P ← s A / B, ? θ F P ← (θ P - θ A / B)$
6：	$d P ← [(x P - x A / B) 2 + (y P - y A / B) 2] 1 / 2$
7：	break
8：	end if
9：	while $s B - s A > ε$ do
10：	$m i d ? A = 1, t a n θ A, ? m i d ? B = 1, t a n θ B$
11：	$s A ← s A + A P · m i d ? A / m i d ? A$
12：	$s B ← s B + B P · m i d ? B / m i d ? B$
13：	end while
14：	$s P ← s A, ? θ F P ← (θ P - θ A)$
15：	$d P ← [(x P - x A) 2 + (y P - y A) 2] 1 / 2$
16	break
17：	end while
18：	end function

References 16

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参数	本车	Obs1	Obs2	Obs3	Obs4
纵向位置s /m	20	35	12	5	20
横向位置d/m	0	0	-3.75	0	3.75
纵向速度v_s/（m·s^-1）	20	15	20	25	20
横向速度v_d/（m·s^-1）	0	0	0	0	-1
纵向加速度a_s/（m·s^-2）	0	-4	0	0	0
横向加速度a_d/（m·s^-2）	0	0	0	0	0
纵向安全距离s_min/m	3
横向安全距离d_min/m	2
车辆长度L/m	5
车辆宽度W/m	1.7
车道宽度lane_w/m	3.75
车道线风险值p	0.2
道路参考线：3阶贝塞尔曲线	（0，0），（30，0），（60，0），（100，20）

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Motion Planning for Active Collision Avoidance of Intelligent Vehicles Based on Predictive Risk Field

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