When should the chicken cross the road?: Game theory for autonomous vehicle-human interactions

Fox, Charles and Camara, F. and Markkula, G. and Romano, R. and Madigan, R. and Merat, N. (2018) When should the chicken cross the road?: Game theory for autonomous vehicle-human interactions. In: Proc. 4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS).

When should the chicken cross the road?: Game theory for autonomous vehicle-human interactions
Fox2018chicken.pdf - Whole Document

Item Type:Conference or Workshop contribution (Poster)
Item Status:Live Archive


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-
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.

Keywords:game theory, autnomous vehicles
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
Divisions:College of Science > School of Computer Science
ID Code:32029
Deposited On:20 Oct 2018 20:46

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