Autonomous Vehicle Sensor Spoofing Detection and Motion Lockout
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Solution Overview
Problem
Autonomous vehicles are vulnerable to spoofing attacks that deceive sensors, such as LiDAR, leading to potential safety hazards and unauthorized control of the vehicle.
Innovation Solution
Implement a system using machine learning and rules-based algorithms to classify detected objects, cross-verify with multiple sensors, and engage user confirmation to mitigate spoofing attacks by preventing vehicle motion and executing security actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If autonomous vehicles use sensors to detect objects in the environment, then the vehicle can navigate and operate autonomously, but the vehicle becomes vulnerable to spoofing attacks that deceive the sensors
Solution Approach 1:
The system performs preliminary actions by detecting objects and predicting expected sensor readings before the spoofing attack can fully deceive the vehicle. The predicted readings are generated based on the object's detected position, velocity, and physical properties, creating a baseline for comparison that prevents deceptive inputs from causing harmful actions
Solution Approach 2:
The system implements feedback by comparing actual sensor readings against predicted readings generated from object detection data. This closed-loop verification mechanism allows the vehicle to identify discrepancies caused by spoofing attacks and adjust its behavior accordingly, maintaining reliable operation despite sensor deception attempts
2Reliability
If the vehicle stops to verify detected objects, then passenger safety is improved, but travel time and productivity are reduced
Solution Approach 1:
The system performs preliminary verification by generating predicted sensor readings based on detected object characteristics before the vehicle needs to stop. This allows the vehicle to proactively identify potential spoofing threats and make informed decisions about whether stopping is necessary, minimizing unnecessary travel time interruptions
Solution Approach 2:
The system applies partial verification by comparing only critical aspects of sensor readings against predictions rather than performing complete physical verification of all detected objects. This selective approach maintains passenger safety by verifying essential parameters while avoiding excessive stopping, thus preserving travel time and productivity
3Measurement precision
If the system uses multiple sensors and algorithms to verify objects, then spoofing detection accuracy is improved, but device complexity increases
Solution Approach 1:
The system achieves multi-functionality by using a single computing resource to perform both object detection and spoofing verification tasks. The predicted sensor readings mechanism serves dual purposes: it aids in object characterization and simultaneously provides a basis for detecting spoofing attacks, reducing the need for separate verification hardware
Solution Approach 2:
The system introduces an intermediary computational layer that generates predicted sensor readings based on object detection data. This intermediary mechanism mediates between raw sensor inputs and vehicle control decisions, providing a computationally efficient way to verify object authenticity without requiring complex additional sensors or hardware
Data Source
AI summary
Embodiments relate to the identification and mitigation of spoofing attacks on autonomous vehicles. A technique includes detecting using a sensor of the vehicle that an object appears in a path of the vehicle and preventing the vehicle from motion in response to detecting the object. The technique includes determining that the object is associated with a deception of the sensor of the vehicle and performing security actions in response to determining that the object is associated with the deception of the sensor of the vehicle.