Context-Aware and Resilient System Architecture for Autonomous Vehicles
Tobias Kain, Philipp Mundhenk, Julian-Steffen MÃ¼ller,Hans Tompits,Maximilian Wesche,Hendrik Decke
Progress in Computing Applications, Vol. 14, No. 2, 2025.
DOI: https://doi.org/10.6025/pca/2025/14/2/69-76
Link: https://www.dline.info/pca/fulltext/v14n2/pcav14n2_1.pdf
Abstract:
This paper presents a novel, three-layered system architecture designed to enhance the reliability and safety
of autonomous vehicles by dynamically adapting to contextual changes. The architecture consists of the context
layer, reconfiguration layer, and architecture layer. The context layer extracts environmental and operational
data, such as weather, traffic, and user preferences, and derives requirements from these inputs. The
reconfiguration layer uses these requirements to plan adaptive actions, such as selecting software applications,
assigning redundancy levels, and optimizing hardware deployment. The architecture layer then implements
these changes on the vehicleâ€™s computing infrastructure, ensuring operational safety through monitoring and
validation.
Two illustrative use cases demonstrate how distinct driving scenariosâ€”like premium highway rides or budget
urban ridesâ€”influence application needs and redundancy levels. The application placement problem, which
involves mapping applications to computing nodes while optimizing for constraints like CPU demand and
safety, is tackled using techniques such as linear programming and reinforcement learning. System selfawareness
is maintained through continuous monitoring, enabling swift reconfiguration in the event of failures.
The proposed approach is unique in its application of full-stack context awareness to autonomous systems,
drawing inspiration from strategies in aerospace fault management. Future work includes implementing a
simulator to validate the architectureâ€™s real-world feasibility.