Using MEMS Microphone Arrays for Non-Line-of-Sight Vehicle Detection in Urban Intersections

As cities advance toward intelligent mobility and automated traffic control, the need for sensors that go beyond visual limitations becomes critical—especially at urban intersections where occluded vehicles pose safety hazards. Traditional radar and LiDAR sensors are effective only in line-of-sight (LOS) conditions, leaving a gap in early detection capabilities.

Recent breakthroughs in acoustic sensing have opened new possibilities. MEMS microphone arrays, when paired with deep learning, can detect vehicles that are not visible—through walls, around corners, or behind other vehicles.

What Is Acoustic Non-Line-of-Sight (NLOS) Detection?

Acoustic NLOS detection leverages the fact that sound waves diffract, reflect, and bend around objects, allowing sensors to “hear” what they cannot “see.” By deploying arrays of omnidirectional MEMS microphones, it’s possible to extract direction-of-arrival (DOA) and time-frequency features of sound, which can then be analyzed to infer vehicle presence and movement.

Using these inputs, researchers developed a parallel deep neural network that classifies:

• Whether a vehicle is approaching or leaving
• Its direction of motion
• Its status relative to the intersection

The result? Vehicles can be detected up to 1 second before entering the line of sight, enabling additional reaction time for autonomous and semi-autonomous systems.

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Performance and Real-World Results

Field tests in urban traffic environments demonstrated:

• 96.71% classification accuracy across 6 different intersection conditions
• Reliable prediction of vehicle direction, including occluded turns
• Accurate approaching/leaving status detection, improving decision-making for intelligent vehicles
• Superior performance compared to existing acoustic baselines for NLOS detection

These outcomes validate MEMS microphone arrays as viable low-power, high-resolution acoustic sensors in modern smart city infrastructure.

Applications and Market Impact

• Smart Intersections: Improve safety where vision sensors are blocked by buildings or buses.
• ADAS (Advanced Driver Assistance Systems): Gain early warnings of hidden traffic.
• Autonomous Vehicles: Enhance situational awareness without relying solely on cameras or LiDAR.
• Urban Surveillance & Emergency Services: Improve decision-making through passive, wide-area listening.

Related Research & External References

• Deep Learning for Non-Line-of-Sight Acoustic Sensing – IEEE Xplore
• AI-Based Acoustic Vehicle Detection for Traffic Monitoring – MDPI
• Direction of Arrival Estimation Using MEMS Microphone Arrays – ScienceDirect

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Our MEMS microphones provide the high signal-to-noise ratio (SNR), ultra-low power consumption, and compact footprint required for AI-powered acoustic sensing at the edge.

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Conclusion

Non-line-of-sight vehicle detection powered by MEMS microphone arrays and deep learning is redefining smart traffic safety. By extending situational awareness beyond the field of view, this acoustic-based system adds a new layer of intelligence to intersections and autonomous vehicles—providing critical milliseconds of reaction time and enhancing road safety for all.