Interacting safely with cyclists using Hamilton-Jacobi reachability and reinforcement learning
This addresses safety-critical interactions in autonomous driving for cyclists, though it is incremental as it builds on existing reachability and reinforcement learning methods.
The paper tackles the problem of enabling autonomous vehicles to safely and efficiently interact with cyclists by integrating Hamilton-Jacobi reachability analysis with deep Q-learning, resulting in a framework that provides safety guarantees and time-efficient navigation as validated through simulation comparisons.
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.