Physics-Informed Neural Optimal Control for Precision Immobilization Technique in Emergency Scenarios

2026-04-07

AI Summary

This work addresses the challenges of highly nonlinear dynamics, stringent safety constraints, and real-time requirements in automated precision interception (PIT) by proposing a hierarchical neural optimal control framework. The architecture comprises an upper virtual decision layer that generates scenario-compliant PIT strategies and a lower coupled model predictive control (MPC) layer for interaction-aware execution. A key innovation is the introduction of a lightweight PicoPINN physics-informed surrogate model, which—through knowledge distillation, hierarchical parameter clustering, and relation matrix reconstruction—reduces model parameters from 8,965 to 812. This study also establishes the first dedicated hierarchical neural optimal control architecture tailored for PIT. Simulations demonstrate an increase in PIT success rate from 63.8% to 76.7%, with an average heading angle error as low as 0.112 rad. Scaled vehicle experiments achieved three successes in four trials, confirming the method’s effectiveness and deployability.

Abstract

Precision Immobilization Technique (PIT) is a potentially effective intervention maneuver for emergency out-of-control vehicle, but its automation is challenged by highly nonlinear collision dynamics, strict safety constraints, and real-time computation requirements. This work presents a PIT-oriented neural optimal-control framework built around PicoPINN (Planning-Informed Compact Physics-Informed Neural Network), a compact physics-informed surrogate obtained through knowledge distillation, hierarchical parameter clustering, and relation-matrix-based parameter reconstruction. A hierarchical neural-OCP (Optimal Control Problem) architecture is then developed, in which an upper virtual decision layer generates PIT decision packages under scenario constraints and a lower coupled-MPC (Model Predictive Control) layer executes interaction-aware control. To evaluate the framework, we construct a PIT Scenario Dataset and conduct surrogate-model comparison, planning-structure ablation, and multi-fidelity assessment from simulation to scaled by-wire vehicle tests. In simulation, adding the upper planning layer improves PIT success rate from 63.8% to 76.7%, and PicoPINN reduces the original PINN parameter count from 8965 to 812 and achieves the smallest average heading error among the learned surrogates (0.112 rad). Scaled vehicle experiments are further used as evidence of control feasibility, with 3 of 4 low-speed controllable-contact PIT trials achieving successful yaw reversal.

Problem

• Precision Immobilization Technique
• optimal control
• nonlinear dynamics
• safety constraints
• real-time computation

Innovation

• PicoPINN
• physics-informed neural network
• hierarchical optimal control
• model predictive control
• knowledge distillation