Context-Conditioned Hamilton-Jacobi Reachability for Adaptive Safety Filtering

Key points

• We learn a backward reachable tube for an eight-state vehicle model conditioned on local boundary geometry, friction coefficient, and adversarial disturbance scale.1
• Geometry enters through an ego-frame boundary observation that defines the local containment constraint, while friction and disturbance scale enter as explicit operating-condition variables.1
• Lateral containment holds in every hardware session under both adversarial driving and autonomous racing, with 99th-percentile acceleration magnitude reaching 0.99 g.1
• Across three certificates evaluated under a fixed autonomous racing controller, lap time varies by only 3.1%, demonstrating that a context-conditioned reachability certificate can transfer to deployment geometries absent from synthesis with modest performance cost.1

Sources (1)

• [1]Context-Conditioned Hamilton-Jacobi Reachability for Adaptive Safety Filtering
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 10:36 AM

  Hamilton-Jacobi reachability constructs safety certificates for specified dynamics and safety constraints, tying each certificate to the deployment context for which it is synthesized.

  We learn a backward reachable tube for an eight-state vehicle model conditioned on local boundary geometry, friction coefficient, and adversarial disturbance scale.

Extractive summary: sentences quoted from the sources.