社会性驾驶交互关键效用析取与应用

doi:10.19562/j.chinasae.qcgc.2024.02.005

Abstract

Abstract:

In shared road space， human driving interaction behavior has the social characteristics of considering the impact on surrounding vehicles. Lacking the understanding of such social characteristics， autonomous vehicles often struggle to estimate the potential impact of their behavior on surrounding vehicles， thus falling into over conservativeness of decision-making dilemma. A game-theory-based social driving interaction model is constructed by introducing in the behavioral characteristics of drivers considering the impact on surrounding vehicles to capture the action dependencies among road users. With this model， a generalized measurement， utility term of interaction activeness （UTIA）， is proposed to quantify the potential impact of the host vehicle's anticipated behavior on its interactants. By introducing the UTIA into the planning objective， the interaction activeness of motion planning algorithm can be directionally adjusted. The results of highway exit experiments show that without compromising safety， enhancing interaction activeness can improve the success rate of the exit task within a given distance by 3.9% and 5.2% for optimization-based and sampling-based motion planning algorithm， respectively.

Key words: autonomous driving, motion planning, social driving interaction, game theory, highway exiting

Xiaocong Zhao,Shiyu Fang,Zirui Li,Jian Sun. Extraction and Application of Key Utility Term for Social Driving Interaction[J].Automotive Engineering, 2024, 46(2): 230-240.

Figures/Tables 13

参数	均值	范围	单位
期望速度 $v ?$	$120$	$± 20$	$k m / h$
最大加速度 $a m a x$	$5$		$m / s 2$
舒适加速度 $a c o m f$	$2$	$± 1$	$m / s 2$
最小停车间距 $s 0$	$2$		$m$
期望车头时距 $T ?$	$2$	$± 0.5$	$s$
常参数 $β$	$5$

算法1：最优迭代响应法求解纳什均衡
输入：主车上一时刻最优轨迹解 $p e (t - 1)$ 主车的当前位置 $p e$ 和速度 $v e$ 目标车道前、后车的当前位置 $p f$ ， $p b$ 和速度 $v f$ ， $v b$
输出：规划轨迹 $p e (t)$
1	初始化：迭代序号 $k ← 0$ 主车轨迹解 $p e [k], * ← O (N + 1) × 2$ 目标车道后车轨迹解 $p ? b [k], * ← a r g ? m a x p b [k] ∈ P b p b, v b, p e [0], * ? R p b [k]$
2	while $k = 0$ or $p e [k], * - p e [k - 1], * ≥ ?$ do
3	set $k ← k + 1$
4	迭代主车轨迹解 $p e [k], * ← a r g m a x p e [k] ∈ P e p e, v e, p b [k - 1], * ? R p e [k] + θ · U a p e [k]$
5	迭代目标车道后车轨迹解 $p ? b [k], * ← a r g m a x p b [k] ∈ P b p b, v b, p e [k], * ? R p b [k] ? p e [k], *$
6	保存迭代最优轨迹解 $p e * ← p e [k], $ ， $p ? b ← p ? b [k], *$
7	end while
8	if $(p e *, p f, v f)$ 不满足碰撞约束 do
9	保留上一时刻最优规划轨迹解 $p e * ← p e (t - 1)$
10	end if
11	输出规划轨迹解 $p e (t) ← p e *$

算法2：Frenet最优规划
输入：目标车道前后车当前位置 $p f$ ， $p b$ 和速度 $v f$ ， $v b$ 主车当前位置 $p e$ 和速度 $v e$ 参考路径特征参数备选集 $S$
输出：规划轨迹 $p e (t)$
1	初始化效用列表 $U l i s t$ 与备选轨迹列表 $P l i s t$
2	for $s e$ in $S$ do
3	生成特征参数为 $s e$ 的Sigmoid参考路径
4	基于参考路径构建Frenet坐标系
5	将初始状态投影至Frenet坐标系（ $p f F$ ， $p b F$ ， $p e F$ ， $v f F$ ， $v b F$ ， $v e F$ ）←（ $p f$ ， $p b$ ， $p e$ ， $v f$ ， $v b$ ， $v e$ ）
6	计算目标前瞻点位置 $p t p F$ ←（ $p f F$ ， $p b F$ ， $p e F$ ， $v f F$ ， $v b F$ ， $v e F$ ）
7	生成横向和纵向的五次多项式位移函数并插值获得备选轨迹 $p e F$ ←（ $p e F$ ， $v e F$ ， $p t p F$ ）
8	if $p e F$ 符合动力学约束与碰撞约束 do
9	计算横、纵向收益 $R l a t$ ， $R l o n$ 和交互效用项 $U a$
10	计算整体轨迹效用 $U i ← R l a t + R l o n + θ · U a$
11	记录备选轨迹效用 $U l i s t i ← U i$
12	将备选轨迹投影至笛卡尔坐标系 $p e$
13	记录备选轨迹 $P l i s t i ← p e$
14	end if
15	end for
16	遍历 $U l i s t$ 获取最大项及其对应序号 $i m a x$
17	输出最大效用轨迹 $p e (t) ← P l i s t i m a x$

规划算法		换道后TTC/s
规划算法		前向TTC 均值	后向TTC 均值
纳什博弈规划	基线	4.54	-4.99
	合作型（ $θ = 0.5$ ）	4.72	-4.62
	竞争型（ $θ = - 0.5$ ）	4.26	-4.80
Frenet 最优规划	基线	-0.75	1.93
	合作型（ $θ = 0.5$ ）	-0.80	1.91
	竞争型（ $θ = - 0.5$ ）	-0.58*	1.69*

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Extraction and Application of Key Utility Term for Social Driving Interaction

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Figures/Tables 13

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