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Perception Simplex: Verifiable Collision Avoidance in Autonomous Vehicles Amidst Obstacle Detection Faults

Ayoosh Bansal, Hunmin Kim, Simon Yu, Bo Li, Naira Hovakimyan, Marco Caccamo, Lui Sha

TL;DR

Perception simplex (PS) is proposed, a fault‐tolerant application architecture designed for obstacle detection and collision avoidance and provides deterministic fault tolerance against obstacle existence detection faults, establishing a robust safety guarantee.

Abstract

Advances in deep learning have revolutionized cyber-physical applications, including the development of Autonomous Vehicles. However, real-world collisions involving autonomous control of vehicles have raised significant safety concerns regarding the use of Deep Neural Networks (DNN) in safety-critical tasks, particularly Perception. The inherent unverifiability of DNNs poses a key challenge in ensuring their safe and reliable operation. In this work, we propose Perception Simplex (PS), a fault-tolerant application architecture designed for obstacle detection and collision avoidance. We analyze an existing LiDAR-based classical obstacle detection algorithm to establish strict bounds on its capabilities and limitations. Such analysis and verification have not been possible for deep learning-based perception systems yet. By employing verifiable obstacle detection algorithms, PS identifies obstacle existence detection faults in the output of unverifiable DNN-based object detectors. When faults with potential collision risks are detected, appropriate corrective actions are initiated. Through extensive analysis and software-in-the-loop simulations, we demonstrate that PS provides predictable and deterministic fault tolerance against obstacle existence detection faults, establishing a robust safety guarantee.

Perception Simplex: Verifiable Collision Avoidance in Autonomous Vehicles Amidst Obstacle Detection Faults

TL;DR

Perception simplex (PS) is proposed, a fault‐tolerant application architecture designed for obstacle detection and collision avoidance and provides deterministic fault tolerance against obstacle existence detection faults, establishing a robust safety guarantee.

Abstract

Advances in deep learning have revolutionized cyber-physical applications, including the development of Autonomous Vehicles. However, real-world collisions involving autonomous control of vehicles have raised significant safety concerns regarding the use of Deep Neural Networks (DNN) in safety-critical tasks, particularly Perception. The inherent unverifiability of DNNs poses a key challenge in ensuring their safe and reliable operation. In this work, we propose Perception Simplex (PS), a fault-tolerant application architecture designed for obstacle detection and collision avoidance. We analyze an existing LiDAR-based classical obstacle detection algorithm to establish strict bounds on its capabilities and limitations. Such analysis and verification have not been possible for deep learning-based perception systems yet. By employing verifiable obstacle detection algorithms, PS identifies obstacle existence detection faults in the output of unverifiable DNN-based object detectors. When faults with potential collision risks are detected, appropriate corrective actions are initiated. Through extensive analysis and software-in-the-loop simulations, we demonstrate that PS provides predictable and deterministic fault tolerance against obstacle existence detection faults, establishing a robust safety guarantee.
Paper Structure (45 sections, 7 theorems, 33 equations, 16 figures, 7 tables, 1 algorithm)

This paper contains 45 sections, 7 theorems, 33 equations, 16 figures, 7 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Scope and Motivation
  3. Related Work
  4. Minimal Requirements
  5. Classification
  6. Collision Risk
  7. Height
  8. Distance
  9. Underestimation Error
  10. Overestimation Error
  11. Projection
  12. Coverage
  13. Summary
  14. Parameters and Constraints
  15. LiDAR Parameters
  16. ...and 30 more sections

Key Result

theorem 1

The obstacle is detected at a distance $D$, if and only if one of the following conditions is true:

Figures (16)

  • Figure 1: Perception Simplex design.
  • Figure 2: Example scenes. (a) Role of obstacle height. (b) Distance and Occlusion of Obstacles. (c) Projection of Obstacles.
  • Figure 3: A representation of point segmentation to ground vs obstacle based on $\alpha$ thresholding when two points return from the obstacle.
  • Figure 4: Representation for $\alpha$ thresholding with only one point on the obstacle.
  • Figure 5: The obstacle elevated above the ground plane, causing a gap between the ground plane and the obstacle.
  • ...and 11 more figures

Theorems & Definitions (11)

  • theorem 1
  • proof
  • corollary 1
  • corollary 2
  • corollary 3
  • theorem 2
  • proof
  • theorem 3
  • proof
  • theorem 4
  • ...and 1 more