The paper proposes Perception Simplex (PS), a fault-tolerant application architecture for autonomous vehicles that aims to provide deterministic collision avoidance amidst faults in deep learning-based perception systems.
The key highlights are:
Reduces the complexity of safety-critical obstacle existence detection by carefully eliminating unnecessary object detection features like object type classification.
Establishes a detectability model for an existing LiDAR-based geometric obstacle detection algorithm (Depth Clustering), providing human-perceptible bounds on its capabilities and limitations.
Designs, analyzes, and implements the PS framework to achieve deterministic fault tolerance specifically targeting the perception fault of obstacle existence detection.
Evaluates the verifiable algorithm using real sensor data and software-in-the-loop simulation, demonstrating PS's response in obstacle existence detection fault scenarios.
The paper first surveys real-world fatal collisions involving autonomous vehicles, revealing that the most prevalent fault is related to the detection of obstacle existence. To address this critical issue, the authors apply the PS framework, which offers deterministic fault tolerance specifically targeting these obstacle existence detection faults.
The authors carefully reduce the requirements for obstacle existence detection for collision avoidance, eliminating unnecessary features of mission-critical object detection. They then conduct a detailed analysis of the Depth Clustering algorithm to establish a comprehensive detectability model, which outlines the algorithm's capabilities and limitations. This verifiable algorithm is then integrated into the PS pipeline, where the detectability model serves as the foundation for ensuring safety guarantees in collision avoidance scenarios.
Through extensive analysis and software-in-the-loop simulations, the authors demonstrate that PS provides predictable and deterministic fault tolerance against obstacle existence detection faults, establishing a robust safety guarantee.
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