Safety Assurance System Utilizing Visual Attention for Advanced Driver-Assistance Systems

Jing, Haoming; Wang, Zhuoyuan; Nakahira, Yorie · 2023 · ROSA P / Mobility21, Carnegie Mellon University

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Summary

This research report addresses the challenge of ensuring safety in stochastic autonomous systems, such as self-driving vehicles, which operate under uncertainties with unbounded support. Existing barrier function-based approaches often face a trade-off between computational efficiency and long-term safety guarantees. Methods that evaluate long horizons are computationally expensive, while myopic methods may accumulate tail probabilities of unsafe events over time. The authors propose a novel safety assurance system that utilizes visual attention concepts to provide provable long-term safety guarantees with low computational cost, suitable for real-time control. The proposed method characterizes safety through probabilistic forward invariance and convergence, ensuring the probability of remaining within a safe set stays above a tolerable threshold ($1 - \epsilon$) over a fixed or receding time horizon. The core innovation is a sufficient safety condition derived from the forward invariance of level sets of safe probability. This condition is affine in the control action, allowing it to be integrated into convex or quadratic programs. The authors develop two control algorithms: an additive modification scheme that adjusts nominal control inputs to increase safety probability, and a constrained optimization approach. To improve the accuracy of gradient estimations required for these conditions, the authors leverage convection-diffusion equations that characterize the relationship between safe probabilities of neighboring states, combining this with Monte Carlo methods. The report validates the proposed method through deployment and experiments, comparing it against three existing stochastic safe controllers: Stochastic Control Barrier Functions (StoCBF), Probabilistic Safety Barrier Certificates (PrSBC), and Conditional-Value-at-Risk Barrier Functions (CVaR). The experimental design involves testing these algorithms under stochastic system dynamics to evaluate their ability to maintain safety margins. The results demonstrate that the proposed controller achieves superior performance by balancing computational efficiency with robust long-term safety guarantees. Unlike myopic approaches that risk accumulating unsafe tail probabilities, or conservative methods that compromise nominal performance, the proposed method allows for intuitive tuning of the safety-performance trade-off based on exact probability characterizations. The significance of this work lies in its ability to overcome the limitations of current stochastic control methods. By providing a framework that ensures safety over a specified time horizon without heavy computational burdens, it enables real-time deployment in delay-critical systems. The approach offers a rigorous mathematical foundation for handling uncertainties with unbounded support, making it applicable to advanced driver-assistance systems and other autonomous agents operating in complex, unpredictable environments. The report concludes that this method effectively bridges the gap between computational efficiency and provable long-term safety, advancing the state-of-the-art in stochastic control theory.

Key finding

The proposed safety assurance system provides provable long-term safety guarantees in stochastic environments through computationally efficient, myopic control policies derived from convection-diffusion equations.

Methodology

modeling

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

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• situational awareness

Information type

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• Theoretical Contribution: computational model