What is the impact of sensor fusion on autonomous navigation?

• A. It slows processing and reduces reliability.
• B. It improves robustness by combining data from multiple sensors to compensate for weaknesses.
• C. It eliminates the need for individual sensors entirely.
• D. It increases the complexity with no reliability benefits.

Answer: (B)

Sensor fusion enhances autonomy by combining information from multiple sensing modalities to form a more reliable understanding of the environment. Each sensor has strengths and weaknesses, and conditions can degrade any single source—cameras struggle in low light, LiDAR can be sparse in certain scenes, and radar may have lower resolution. By merging data, the system can cross-verify detections, resolve ambiguities, and produce more accurate estimates of object positions, velocities, and the vehicle’s own location. This redundancy and complementary information make navigation more robust, so the vehicle can operate safely even if one sensor underperforms. For example, using both camera and LiDAR lets the system leverage the camera’s rich texture and the LiDAR’s precise geometry for better object detection and distance estimation. While fusion adds some complexity, the reliability benefits are the key takeaway.