Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses

Abstract

The rapid development of artificial intelligence, especially deep learning technology, has advanced autonomous driving systems (ADSs) by providing precise control decisions to counterpart almost any driving event, spanning from anti-fatigue safe driving to intelligent route planning. However, ADSs are still plagued by increasing threats from different attacks, which could be categorized into physical attacks, cyberattacks and learning-based adversarial attacks. Inevitably, the safety and security of deep learning-based autonomous driving are severely challenged by these attacks, from which the countermeasures should be analyzed and studied comprehensively to mitigate all potential risks. This survey provides a thorough analysis of different attacks that may jeopardize ADSs, as well as the corresponding state-of-the-art defense mechanisms. The analysis is unrolled by taking an in-depth overview of each step in the ADS workflow, covering adversarial attacks for various deep learning models and attacks in both physical and cyber context. Furthermore, some promising research directions are suggested in order to improve deep learning-based autonomous driving safety, including model robustness training, model testing and verification, and anomaly detection based on cloud/edge servers.

References in corpus (11)

• ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
• DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks
• Delving into Transferable Adversarial Examples and Black-box Attacks
• Towards Deep Neural Network Architectures Robust to Adversarial Examples
• Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net
• Improving Adversarial Robustness via Promoting Ensemble Diversity
• Adversarial Attacks on Neural Network Policies
• Physical Adversarial Attack on Vehicle Detector in the Carla Simulator
• Exploiting Social Navigation
• A Survey on Security Attacks and Defense Techniques for Connected and Autonomous Vehicles
• MobilBye: Attacking ADAS with Camera Spoofing