Right in Time: Reactive Reasoning in Regulated Traffic Spaces
Simon Kohaut, Benedict Flade, Julian Eggert, Kristian Kersting, Devendra Singh Dhami

TL;DR
This paper introduces a reactive reasoning framework combining probabilistic inference and logical traffic regulations, enabling real-time, efficient decision-making for autonomous vehicles in complex, shared environments.
Contribution
It presents a novel reactive mission design approach that integrates Probabilistic Mission Design with Reactive Circuits for online, exact inference in hybrid domains.
Findings
Achieves significant speedup over previous probabilistic methods.
Enables real-time safety and compliance assertions for autonomous vehicles.
Demonstrated effectiveness in real-world vessel and urban drone scenarios.
Abstract
Exact inference in probabilistic First-Order Logic offers a promising yet computationally costly approach for regulating the behavior of autonomous agents in shared traffic spaces. While prior methods have combined logical and probabilistic data into decision-making frameworks, their application is often limited to pre-flight checks due to the complexity of reasoning across vast numbers of possible universes. In this work, we propose a reactive mission design framework that jointly considers uncertain environmental data and declarative, logical traffic regulations. By synthesizing Probabilistic Mission Design (ProMis) with reactive reasoning facilitated by Reactive Circuits (RC), we enable online, exact probabilistic inference over hybrid domains. Our approach leverages the Frequency of Change inherent in heterogeneous data streams to subdivide inference formulas into memoized, isolated…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAir Traffic Management and Optimization · Adversarial Robustness in Machine Learning · Autonomous Vehicle Technology and Safety
