Microdecisions and autonomy in self-driving cars: virtual probabilities

Florian Sprenger

doi:10.1007/s00146-020-01115-7

Abstract

To operate in an unpredictable environment, a vehicle with advanced driving assistance systems, such as a robot or a drone, not only needs to register its surroundings but also to combine data from different sensors into a world model, for which it employs filter algorithms. Such world models, as this article argues with reference to the SLAM problem (simultaneous location and mapping) in robotics, consist of nothing other than probabilities about states and events arising in the environment. The model, thus, contains a virtuality of possible worlds that are the basis for adaptive behavior. The article shows that the current development of these technologies requires new concepts because their complex adaptive behaviors cannot be explained by referring them to mere algorithmic processes. Instead, it proposes the heuristic instrument of microdecisions to designate the temporality of decisions between alternatives that are created by probabilistic procedures of world modeling. Microdecisions are more than the implementation of deterministic processes—they decide between possibilities and, thus, always open up the potential of their otherness. By describing autonomous adaptive technologies with this heuristic, the question of sovereignty inevitably arises. It forces us to re-think what autonomy means when decisions can be automated.

Highlights

One cannot conceive of an environment without that which surrounds it
Self-driving cars, or rather the Advanced Driver Assistance Systems (ADAS) that are installed in all new cars, are at the center of public interest, since they are an element of the current transformations of
At least for more complex combinations of ADAS systems on higher levels of automation, the environment is a fragmentary and operational model that has been created by a specific correlation of sensors with different capabilities, filter algorithms that analyze sensor data, processes of machine learning that optimize pattern recognition, and operating decision modules. By focusing on this intersection of different technologies, this paper explores the epistemological constellation in which the autonomous car’s environment is constituted by processes of world modeling that, as is shown below, incorporate probabilities that are fundamental for microdecisions

Summary

Introduction

One cannot conceive of an environment without that which surrounds it. Both sides are relationally entangled. This heuristic concept is supposed to be helpful for describing the car, its technical systems, and its relation to the environment as a complex time-critical assemblage that allows the distributed robotic system to adapt its behavior and become autonomous in this operational and strategic sense Using their sensors and respective filter algorithms, autonomous systems must be able to register events and objects in their proximity, locate and situate themselves in relation to them, project their own behavior into the future, build world models and on this basis decide upon actions, reactions, and interactions. Once its contours have become clear, concept of microdecisions is shown to be of value for further investigations of these capacities

The microtemporality of decisions

Microdecisions and autonomy: a heuristic for the present

Conclusion