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[2602.18097] Interacting safely with cyclists using Hamilton-Jacobi reachability and reinforcement learning
Summary
This paper presents a framework for autonomous vehicles to safely interact with cyclists by integrating Hamilton-Jacobi reachability analysis with reinforcement learning, ensuring both safety and optimal navigation.
Why It Matters
As autonomous vehicles become more prevalent, ensuring their safe interaction with vulnerable road users like cyclists is crucial. This research addresses safety concerns while optimizing navigation, contributing to the development of safer transportation systems.
Key Takeaways
- The framework combines Hamilton-Jacobi reachability with deep Q-learning.
- Safety metrics are integrated as structured rewards in reinforcement learning.
- The model accounts for cyclist behavior and comfort in vehicle interactions.
- Simulation results show improved safety compared to existing methods.
- This research supports the advancement of safe autonomous vehicle technologies.
Computer Science > Robotics arXiv:2602.18097 (cs) [Submitted on 20 Feb 2026] Title:Interacting safely with cyclists using Hamilton-Jacobi reachability and reinforcement learning Authors:Aarati Andrea Noronha, Jean Oh View a PDF of the paper titled Interacting safely with cyclists using Hamilton-Jacobi reachability and reinforcement learning, by Aarati Andrea Noronha and Jean Oh View PDF HTML (experimental) Abstract:In this paper, we present a framework for enabling autonomous vehicles to interact with cyclists in a manner that balances safety and optimality. The approach integrates Hamilton-Jacobi reachability analysis with deep Q-learning to jointly address safety guarantees and time-efficient navigation. A value function is computed as the solution to a time-dependent Hamilton-Jacobi-Bellman inequality, providing a quantitative measure of safety for each system state. This safety metric is incorporated as a structured reward signal within a reinforcement learning framework. The method further models the cyclist's latent response to the vehicle, allowing disturbance inputs to reflect human comfort and behavioral adaptation. The proposed framework is evaluated through simulation and comparison with human driving behavior and an existing state-of-the-art method. Comments: Subjects: Robotics (cs.RO); Machine Learning (cs.LG) Cite as: arXiv:2602.18097 [cs.RO] (or arXiv:2602.18097v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2602.18097 Focus to learn more arX...
