Ali Shoker, Rehana Yasmin, Paulo Esteves-Verissimo (Resilient Computing & Cybersecurity Center (RC3), KAUST)

The increasing interest in Autonomous Vehicles (AVs) is notable, driven by economic, safety, and performance reasons. Despite the growing adoption of recent AV architectures hinging on the advanced AI models, there is a significant number of fatal incidents. This paper calls for the need to revisit the fundamentals of building safety-critical AV architectures for mainstream adoption of AVs. The key tenets are: (i) finding a balance between intelligence and trustworthiness, considering efficiency and functionality brought in by AI/ML, while prioritizing indispensable safety and security; (ii) developing an advanced architecture that addresses the hard challenge of reconciling the stochastic nature of AI/ML with the determinism of driving control theory. Introducing Savvy, a novel AV architecture leveraging the strengths of intelligence and trustworthiness, this paper advocates for a safety-first approach by integrating design-time (deterministic) control rules with optimized decisions generated by dynamic ML models, all within constrained time-safety bounds. Savvy prioritizes early identification of critical obstacles, like recognizing an elephant as an object, ensuring safety takes precedence over optimal recognition just before a collision. This position paper outlines Savvy’s motivations and concepts, with ongoing refinements and empirical evaluations in progress.

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