Physics-Aware Safety-Assured Design of Hierarchical Neural Network based Planner

TL;DR

This paper introduces a hierarchical, physics-aware neural network planner for safety-critical systems like autonomous driving, with formal verification methods to ensure safety across diverse physical scenarios.

Contribution

It proposes a novel hierarchical neural network planner that incorporates physical scenario analysis and develops an efficient verification method for safety assurance.

Findings

Improves safety and verifiability by considering physical scenarios.

Demonstrates effectiveness in autonomous driving tasks.

Outperforms baseline methods in case studies.

Abstract

Neural networks have shown great promises in planning, control, and general decision making for learning-enabled cyber-physical systems (LE-CPSs), especially in improving performance under complex scenarios. However, it is very challenging to formally analyze the behavior of neural network based planners for ensuring system safety, which significantly impedes their applications in safety-critical domains such as autonomous driving. In this work, we propose a hierarchical neural network based planner that analyzes the underlying physical scenarios of the system and learns a system-level behavior planning scheme with multiple scenario-specific motion-planning strategies. We then develop an efficient verification method that incorporates overapproximation of the system state reachable set and novel partition and union techniques for formally ensuring system safety under our physics-aware planner. With theoretical analysis, we show that considering the different physical scenarios and building a hierarchical planner based on such analysis may improve system safety and verifiability. We also empirically demonstrate the effectiveness of our approach and its advantage over other baselines in practical case studies of unprotected left turn and highway merging, two common challenging safety-critical tasks in autonomous driving.