A Moral Consensus Mechanism for Autonomous Driving: Towards a Law-compliant Basis of Logic Programming

Abstract

Research into autonomous vehicles is making progress. While implementation is progressing through machine learning and efficient sensor technology, one key challenge remains dealing with moral disputes. In general, traffic requires for moral decisions that might even decide on the life or death of participants. While people make intuitive decisions in accidents, a decision of an autonomous vehicle is made already at the programming stage. Thus, a concrete handling for implementation is needed. Due to a lack of legislation, this is still missing and prevents car manufacturers from a practical solution. The paper at hand addresses this problem by presenting a consensus mechanism, combining moral convictions, legislation, and programming guidelines. Based on a study of dilemma situations, moral principles of the 'correct action' of autonomous vehicles are derived. Of four principles, we confirm one, reject two, and propose one for further research investigation to form a basis for jurisdictions.

An Interactive Method for Detection of Process Activity Executions from IoT Data

“…Especially IoT sensors are capable of producing data at a very fine-grained level [49], which may not be suitable for process mining. Related work proposes to use, e.g., Complex Event Processing (CEP) [2,50], clustering [51], supervised machine learning [4,51,52] or combinations together with expert knowledge [53] to bridge this abstraction gap [54]. To enable process mining, the resulting coarse-grained events then need to be matched to the activities in a process model and correlated with the process instances through event-to-activity mappings [50,55,56].…”

“…In providing the process analyst with a guideline in the form of a structured interactive analysis method, we rely on domain knowledge for the activities of event extraction, event abstraction and event correlation. Once an initial set of IoT data from a specific IoT domain was analyzed and labeled by the process analyst, the knowledge about IoT data-activity-process correlations can be used as input for more sophisticated supervised learning techniques to automatically analyze larger data sets [37,53,57,58].…”