Verifiable Collision Avoidance in Autonomous Vehicles Amidst Obstacle Detection Faults

Основні поняття

Perception Simplex (PS) is a fault-tolerant application architecture designed to provide deterministic collision avoidance in autonomous vehicles by leveraging verifiable obstacle detection algorithms to detect and mitigate faults in unverifiable deep learning-based perception systems.

Анотація

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:

1. Reduces the complexity of safety-critical obstacle existence detection by carefully eliminating unnecessary object detection features like object type classification.

2. Establishes a detectability model for an existing LiDAR-based geometric obstacle detection algorithm (Depth Clustering), providing human-perceptible bounds on its capabilities and limitations.

3. Designs, analyzes, and implements the PS framework to achieve deterministic fault tolerance specifically targeting the perception fault of obstacle existence detection.

4. 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.

Статистика

The material presented in this paper is based upon work supported by the National Science Foundation (NSF) under grant no. CNS 1932529, ECCS 2020289, the Air Force Office of Scientific Research (AFOSR) under grant no. #FA9550-21-1-0411, the National Aeronautics and Space Administration (NASA) under grant no. 80NSSC22M0070, 80NSSC20M0229, AWD-000577-G1, and University of Illinois Urbana-Champaign under grant no. STII-21-06.

Цитати

"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."
"Safety-critical software necessitates logical analysis and verification, requirements that current DNN solutions are not yet equipped to fulfill."

Ключові висновки, отримані з

Perception Simplex

by Ayoosh Bansa... о arxiv.org 04-08-2024

https://arxiv.org/pdf/2209.01710.pdf

Глибші Запити

How can the Perception Simplex architecture be extended to handle other perception faults beyond obstacle existence detection

To extend the Perception Simplex architecture to handle other perception faults beyond obstacle existence detection, the framework can be designed to incorporate additional verifiable algorithms for different perception tasks. By identifying the minimal requirements for each specific perception task crucial for safety-critical operations, similar to the approach taken for obstacle detection, the architecture can be expanded to include fault-tolerant solutions for a range of perception faults. This extension would involve analyzing the specific requirements and constraints for each perception task, developing verifiable algorithms to detect faults in meeting those requirements, and integrating them into the safety layer of the Perception Simplex framework. By modularizing the architecture and ensuring that each verifiable algorithm addresses a specific perception fault, the system can provide deterministic fault tolerance against a variety of perception faults, not just limited to obstacle existence detection.

What are the potential challenges and limitations in integrating the verifiable obstacle detection algorithm with the mission-critical self-driving software in a real-world autonomous vehicle system

Integrating the verifiable obstacle detection algorithm with the mission-critical self-driving software in a real-world autonomous vehicle system may present several challenges and limitations. One potential challenge is the synchronization and real-time processing of data between the safety layer, where the verifiable algorithm operates, and the mission layer responsible for autonomous driving functions. Ensuring seamless communication and data exchange between these layers while maintaining the performance and reliability of the overall system can be complex. Additionally, the computational overhead introduced by the verifiable algorithm and the additional processing required for fault detection and correction may impact the real-time responsiveness of the autonomous vehicle system. Furthermore, the validation and certification of the integrated system to meet safety standards and regulatory requirements could pose challenges in ensuring the robustness and reliability of the system in real-world scenarios.

How can the Perception Simplex framework be adapted to address the challenges posed by adversarial attacks on the perception system, which could lead to undetectable obstacle existence faults

Adversarial attacks on the perception system, leading to undetectable obstacle existence faults, present a significant challenge for the Perception Simplex framework. To address this issue, the framework can be adapted to incorporate anomaly detection and robustness mechanisms to identify and mitigate the impact of adversarial attacks. By integrating techniques such as anomaly detection algorithms, adversarial training, and data augmentation strategies, the Perception Simplex framework can enhance its resilience against malicious attacks on the perception system. Additionally, implementing secure communication protocols, encryption methods, and intrusion detection systems can help safeguard the integrity of the perception data and prevent unauthorized access or manipulation by external entities. By proactively addressing the challenges posed by adversarial attacks, the Perception Simplex framework can strengthen its fault tolerance capabilities and ensure the safety and reliability of autonomous vehicle operations.