When Should the Chicken Cross the Road? - Game Theory for Autonomous Vehicle - Human Interactions
When Should the Chicken Cross the Road? - Game Theory for Autonomous Vehicle - Human Interactions
复制标题
鸡应该什么时候过马路？
DOI：
10.5220/0006765404310439
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发表时间：
2018
期刊：
影响因子：
3.9
通讯作者：
N. Merat
中科院分区：
文献类型：
--
作者：
Charles W. Fox;F. Camara;G. Markkula;R. Romano;R. Madigan;N. Merat
自主车辆控制对于局部[15]是很好理解的，存在很好的近似，例如粒子滤波，
在非反应性环境中进行建模、绘图和规划-利用强大的计算能力来绘制样本
解决方案，但人为因素的复杂相互作用附近的解决方案。
站在[16]，尽管其精确解是NP难的，
与其他道路使用者尚未开发。
非交互环境下的路径规划
环境也有众所周知的易处理的解决方案，
这波-
A星算法给定一条路线，定位和控制-
本文提出了一个初步的谈判模型，
控制沿着该路线然后变成类似任务，
自动驾驶汽车和另一辆汽车之间，
1959年通用汽车Firebird III
无标志的十字路口或（等效）与行人
自动驾驶汽车[1]，使用电磁感应
在未签名的路口（乱穿马路），使用离散
沿着公路上的电线走
这条路是这样走的-
序贯博弈论该模型旨在作为一个基础，使用电线或SLAM，然后可以增强与
sic框架更现实和数据驱动的未来简单的安全逻辑停止车辆，如果有任何障碍，
extensions.该模型表明，当只有车辆PO-在其方式，如检测到的任何范围传感器。
位置被用来发出意图信号，开源系统在这个级别的“自动驾驶”中的最佳行为现在是
两个代理都必须包括非零概率的A1-广泛可用[6]。
从而导致发生碰撞。
与此相反的是，
这建议扩展到
这些车辆将面临的问题
与其他道路使用者的互动要困难得多
减少这种可能性，例如其他形式的
都需要公式化和解决。自动驾驶汽车不
信号和控制。与大多数博弈论应用不同，
只需要处理无生命的物体，传感器，
在经济学中，主动车辆控制需要真实的-
地图
从没有历史的多重均衡中选择时间，
他们目前必须处理其他代理人的问题
人类驾驶员和行人，最终还有其他Au-
我们提出并论证了一个新的解决方案概念，
元战略融合，适合这项任务。
Autonomous vehicle control is well understood for local- [15], good approximations exist such as particle �ltering,
ization, mapping and planning in un-reactive environ- which make use of large compute power to draw samples
ments, but the human factors of complex interactions near solutions.
stood [16], and despite its exact solution being NP-hard
with other road users are not yet developed.
Route planning in non-interactive envi-
ronments also has well known tractable solutions such as
This po-
the A-star algorithm. Given a route, localizing and con-
sition paper presents an initial model for negotiation be-
trol to follow that route then becomes a similar task to
tween an autonomous vehicle and another vehicle at an
that performed by the 1959 General Motors Firebird-III
unsigned intersections or (equivalently) with a pedestrian
self-driving car [1], which used electromagnetic sensing
at an unsigned road-crossing (jaywalking), using discrete
to follow a wire built into the road.
Such path follow-
sequential game theory. The model is intended as a ba- ing, using wires or SLAM, can then be augmented with
sic framework for more realistic and data-driven future simple safety logic to stop the vehicle if any obstacle is
extensions. The model shows that when only vehicle po- in its way, as detected by any range sensor.
sition is used to signal intent, the optimal behaviors for open source systems for this level of `self-driving' are now
both agents must include a non-zero probability of al- widely available [6].
lowing a collision to occur.
In contrast,
This suggests extensions to
problems that these vehicles will face
around interacting with other road users are much harder
reduce this probability in future, such as other forms of
both to formulate and solve. Autonomous vehicles do not
signaling and control. Unlike most Game Theory appli-
just have to deal with inanimate objects, sensors, and
cations in Economics, active vehicle control requires real-
maps.
time selection from multiple equilibria with no history,
They have to deal with other agents, currently
human drivers and pedestrians and eventually other au-
and we present and argue for a novel solution concept,
meta-strategy convergence , suited to this task.