## 3 Materials and Methods
### Sensor Configuration  
Our experimental setup incorporated a comprehensive sensor suite to evaluate the resilience of autonomous vehicles against various attack vectors:  
* **RGB Cameras**: Front-facing RGB camera with \(800\times 600\) resolution and a \(100^{\circ}\) field of view, mounted at position (1.3, 0.2, 2.3) relative to the vehicle center, providing visual input for the agent's perception system.
* **Dynamic Vision Sensor (DVS)**: Event-based camera with \(800\times 600\) resolution and a \(100^{\circ}\) field of view, mounted at position (1.3, 0.0, 2.3), with positive and negative thresholds set to 0.3 to capture brightness changes in the scene with microsecond temporal resolution.
* **Depth Camera**: Depth-sensing camera with \(800\times 600\) resolution and a \(100^{\circ}\) field of view, aligned with the RGB camera position, providing per-pixel distance measurements for 3D scene understanding.  
* **LiDAR**: A 64-channel LiDAR sensor with an 85 m range, 600,000 points per second, and a 10 Hz rotation frequency, mounted at position (0.0, 0.0, 2.5), providing detailed 3D point cloud data of the surrounding environment.
* **GPS and IMU**: For localization and vehicle state estimation, enabling the agent to maintain awareness of its position and orientation within the environment.  
It is important to note that in this experimental setup, the DVS cameras and LiDARs were primarily used for research purposes and data collection and were not directly integrated into the vehicle's driving decision model.  
To better illustrate the RGB sensor integration and data flow within our experimental framework, Figure 1 presents a comprehensive workflow diagram that demonstrates the sensor attachment process, data acquisition pipeline, and the subsequent processing stages. This workflow visualization provides crucial insight into how RGB sensor data is captured, processed, and utilized within the CARLA simulation environment, forming the foundation for our cybersecurity evaluation methodology.  
Similarly, the dynamic vision sensor (DVS) follows a comparable workflow architecture to the RGB sensor system, with several key distinctions that reflect the unique  
Figure 1: RGB sensor attachment workflow diagram showing the complete data acquisition and processing pipeline in the CARLA simulation environment. The workflow illustrates sensor initialization, data capture, preprocessing stages, and integration with the autonomous driving agent.  
characteristics of event-based vision technology. Figure 2 illustrates the DVS sensor attachment and data processing pipeline within our experimental framework.  
The DVS data processing pipeline differs from the RGB pipeline in several critical aspects:  
* Event-based data capture generates asynchronous streams of brightness change events rather than synchronized frame sequences;
* Event-to-image conversion transforms sparse temporal events into dense spatial representations for compatibility with existing vision processing frameworks;
* Specialized defense mechanisms employ spatial clustering algorithms to detect and filter noise events; and
* Importantly, DVS data streams are utilized primarily for research analysis and anomaly detection rather than direct integration into the PCLA (Pretrained CARLA Leaderboard Agent) and vehicle control model.  
This architectural decision allows us to evaluate the intrinsic security characteristics of event-based vision.  
Sensor data was collected at 10 Hz to match the control frequency of the autonomous driving agent. All sensor data was synchronized using the CARLA simulator's internal clock to ensure temporal consistency across modalities. This comprehensive sensor config  
Figure 2: DVS sensor attachment workflow diagram demonstrating the processing pipeline.  
uration allowed us to evaluate the impact of attacks on individual sensors as well as the effectiveness of multi-sensor fusion for attack detection and mitigation.