“Self-stabilizing Manoeuvre Negotiation: the Case of Virtual Traffic Lights”
Revision as of 23:14, 25 November 2019 by Casim
in 38th International Symposium on Reliable Distributed Systems (SRDS 2019), Fast Abstract, Lyon, France, Sept. 2019.
Abstract: The vision of automated driving promises to have safer and more cost-efficient transport systems. Automated driving systems have to demonstrate high levels of dependability and affordability. Recent advances of new communication technologies, e.g., 5G, allow significant cost reduction of timely shared sensory information. However, the design of fault-tolerant automated driving systems remains an open challenge. This work considers the design of automated driving systems through the lenses of self-stabilization — a very strong notion of fault-tolerance. Our self-stabilizing algorithms guarantee, within a bounded period, recovery from a broad fault model and arbitrary state corruption. After this recovery period, our algorithms provide safe maneuver execution despite the presence of failures, such as unbounded periods of packet loss and timing failures as well as inaccurate sensory information and malicious behavior. We evaluate the proposed algorithms through a rigorous correctness proof and a worst-case analysis as well as a prototype that focuses on an intersection crossing protocol. We validate our prototype via computer simulations and a testbed implementation. Our preliminary results show a reduction in the number of vehicle collisions and dangerous situations.
Research line(s): Timeliness and Adaptation in Dependable Systems (TADS)