Autonomous Vehicle Ramming Attack Detection via Trajectory Anomaly
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Solution Overview
Problem
The increasing use of autonomous and semi-autonomous vehicles poses a new threat in vehicular ramming attacks, as these vehicles can be maliciously taken over for committing such attacks, and existing technologies lack effective methods for real-time detection and prevention.
Innovation Solution
The system employs enhanced control systems that generate and continuously update vehicle trajectories based on environmental data, using sensors and machine learning techniques like generative adversarial deep neural networks to detect anomalies and prevent ramming attacks by transmitting alerts to relevant authorities through 5G-MEC networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If autonomous and semi-autonomous vehicles are deployed, then vehicle control and navigation capabilities are improved, but vulnerability to malicious remote takeover for ramming attacks increases
Solution Approach 1:
The system performs preliminary actions by continuously generating predicted trajectories and establishing baseline vehicle behavior patterns before attacks occur. The anomaly detection system is pre-configured with normal driving patterns, enabling it to quickly identify deviations that may indicate malicious takeover attempts, thus preventing attacks before they can be executed
Solution Approach 2:
The system implements continuous feedback loops by monitoring vehicle sensor data, comparing actual trajectories against predicted trajectories, and automatically alerting authorities when anomalies are detected. This real-time feedback mechanism enables the system to respond dynamically to potential malicious takeovers, closing the security loop between detection and response
2Measurement precision
If real-time trajectory monitoring and anomaly detection are implemented, then detection capability is improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the most critical features from vehicle sensor data for anomaly detection, such as trajectory deviations, acceleration patterns, and steering behavior. By focusing on key indicators rather than processing all raw sensor data, the system achieves high detection precision while reducing computational complexity and processing requirements
Solution Approach 2:
The system transforms raw sensor data into meaningful parameters and features that are more suitable for anomaly detection. By changing the representation of vehicle data from raw sensor readings to derived trajectory parameters and behavior metrics, the system improves detection capability while reducing the dimensional complexity of the data processing task
3Loss of time
If continuous trajectory updates and real-time alerts are transmitted, then response time to attacks is reduced, but network bandwidth and communication energy consumption increase
Solution Approach 1:
The system implements periodic action by transmitting trajectory updates and alerts at optimized intervals rather than continuously. Normal trajectory updates are sent at regular intervals, while anomaly detection triggers more frequent updates only when necessary. This periodic communication approach reduces network bandwidth consumption and energy usage while maintaining rapid response capability when attacks are detected
Solution Approach 2:
The system applies partial action by transmitting full trajectory data only when anomalies are detected, while using summarized or reduced data for normal operating conditions. This selective data transmission approach ensures rapid response to attacks when needed while minimizing communication energy consumption during normal vehicle operation
Data Source
AI summary
Systems and methods for countering the usage of autonomous or semi-autonomous vehicles for ramming attacks on a roadway are disclosed. Digital representations of physical trajectories (e.g., roadway travel routes) across which vehicles are expected or permitted to travel are generated based at least on travel-related data (e.g., sensor readings) received from the vehicles over wireless networks. The disclosed systems and methods further generate digital representations of physical trajectories across which vehicles are not permitted to travel, such that the impermissible physical trajectories constitute a deviation from a safe travel route. Additional travel-related data is continuously received from the vehicles in real-time, and the additional data may be combined with non-vehicle data (e.g., pedestrian travel data) and compared to the generated digital representations of permissible and impermissible physical trajectories to determine if the vehicles' physical trajectory is indicative of a harmful impermissible physical trajectory, such as a vehicular ramming attack.