Asynchronous real-time framework for knowledge-based intersection assistance

Abstract

Recently, a demand for advanced driver assistance in sophisticated situations with multiple traffic objects and complex infrastructure has emerged.To handle this kind of situations we developed a knowledge-base to model abstract qualitative information of traffic situations, up to very complex traffic intersections. We further created an asynchronous real-time simulation framework to investigate applicability of knowledge-based driver assistance systems within vehicle or traffic management systems. While the simulation with sensor data is running with very short update cycles the knowledge-base is updated asynchronously since reasoning is time expensive. Driver assistance functions are able to query the knowledge-base once it is fully reasoned.We especially focus on safety assistance systems and performed tests of our knowledge-based framework on exemplary intersection assistance functions. Results show the capability to perform semi and fully autonomous warning and deescalation assistance functions in real-time.

Ontology-based context awareness for driving assistance systems

“…The time necessary to reason on an ontology is quite significant and has to be taken into consideration. For real time applications, reasoning on the ontology should be carried out asynchronously with the rest of the system, as done in [13].…”

“…It proposes a generic description of road intersections which is adaptable to every intersection. It has been demonstrated using simulation techniques [13].…”

A Survey on Knowledge Graph-based Methods for Automated Driving

“…Feld et al [24] presented an ontology focusing on humanmachine-interfaces and inter-vehicle communication. Huelsen et al [50,51] developed an ontology for traffic intersections to reason about the right-of-way including information about traffic signs and traffic lights. Related approaches can be found in [25,37,69,88].…”