Autonomous Vehicle Perception Against Real-Time Adversarial Attacks
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Solution Overview
Problem
Autonomous vehicles employing machine learning are vulnerable to real-time perceptual adversarial attacks, which can compromise safe driving plans, and existing methods lack effective real-time detection and mitigation strategies.
Innovation Solution
A method and system that monitor the environment around autonomous vehicles using sensor and map data to identify potential adversarial attacks by detecting inconsistencies, calculating probability scores, and planning vehicle motion to mitigate these attacks, including the use of multiple sensor modalities, crowd-sourced data, and human feedback to confirm and respond to adversarial scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning models are used for autonomous vehicle perception, then the vehicle can automatically detect and classify objects in the environment, but the vehicle becomes vulnerable to adversarial attacks that can manipulate sensor data and cause incorrect detections
Solution Approach 1:
The patent introduces an intermediary adversarial attack detection system that sits between the sensor data input and the machine learning model. This intermediary layer analyzes sensor data for signs of adversarial manipulation before the data reaches the perception model, thereby protecting the automated detection system from malicious inputs while preserving its automated operation
Solution Approach 2:
The system implements feedback mechanisms where detection results and confidence scores are continuously monitored and fed back into the detection process. When adversarial attacks are detected or confidence is low, the system adjusts its behavior by requesting additional sensor data or alternative detection methods, creating a closed-loop system that improves reliability through continuous validation
2Reliability
If the system monitors all regions in the environment for adversarial attacks, then detection coverage is maximized, but computational resources and processing time are excessively consumed
Solution Approach 1:
Instead of uniformly monitoring all environmental regions, the system applies local quality by focusing computational resources on specific regions of interest where adversarial attacks are most likely to occur. The system identifies and prioritizes monitoring of critical areas such as regions containing traffic signs, pedestrians, or objects with low confidence detections, thereby improving detection coverage where it matters most while reducing overall computational burden
Solution Approach 2:
The patent segments the environmental monitoring space into different regions based on risk levels and importance. High-risk regions receive intensive monitoring with multiple detection passes, while low-risk regions receive minimal or no monitoring. This segmentation allows the system to allocate computational resources efficiently across the environment, maintaining high detection coverage in critical areas while preserving processing efficiency
3Measurement precision
If the system requests additional sensor data or human feedback for low confidence detections, then detection accuracy improves, but response time and operational efficiency decrease
Solution Approach 1:
The system applies partial action by requesting additional verification only for detections that fall below a confidence threshold, rather than verifying all detections. This selective approach ensures high accuracy for uncertain detections while maintaining fast response times for high-confidence detections, thereby balancing precision and speed based on the specific detection scenario
Solution Approach 2:
The confidence threshold for requesting additional data is made dynamic rather than fixed. The system adjusts the threshold based on contextual factors such as environmental conditions, vehicle operating state, and historical data patterns. This dynamic adjustment allows the system to be more aggressive in seeking verification when conditions warrant it, while being more efficient when conditions are favorable, thereby optimizing the trade-off between accuracy and response time
4Reliability
If the system plans vehicle motion to account for potential adversarial attacks, then safety is improved, but the complexity of the planning system increases
Solution Approach 1:
The system performs preliminary actions by pre-planning multiple motion trajectories that account for potential adversarial scenarios before actual attacks occur. The planning system pre-computes alternative paths and safety maneuvers that can be quickly activated when threats are detected, rather than computing new plans in real-time during attacks. This preliminary preparation improves safety response while avoiding the computational complexity of real-time replanning
Data Source
AI summary
A method for mitigating an adversarial attack includes receiving input data. The input data includes sensor data from a plurality of sensors and map data. The method further includes monitoring, in real time, an environment around an autonomous vehicle to identify a region that is possibly subject to an adversarial attack and determining a probability of the adversarial attack in the region. The method further includes determining whether the probability of the adversarial attack in the region that is possibly subject to the adversarial attack is greater than a predetermined threshold and, in response, planning a motion of the autonomous vehicle by taking into account the adversarial attack to generate a planned motion. The method further includes controlling a host vehicle to move in accordance with the planned motion.